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Balancing AI Assistance and Digital Sovereignty

April 22, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

In today’s rapidly evolving digital landscape, the tension between embracing AI assistance technologies and maintaining digital sovereignty has become a critical concern for governments, organizations, and individuals alike. As Large Language Models (LLMs) and AI-powered systems become increasingly integrated into our daily operations, finding the appropriate balance between leveraging these powerful tools and preserving control over our digital destiny presents both opportunities and challenges. This report examines the intricate relationship between AI assistance and digital sovereignty, exploring strategic approaches to harness AI benefits while maintaining necessary control over data, technology, and infrastructure.

Understanding Digital Sovereignty in the AI Era

Digital sovereignty refers to an entity’s ability to maintain control over its digital destiny – encompassing the data, hardware, and software that it relies on and creates. As the World Economic Forum explains, this concept extends beyond mere data protection to include “fostering entrepreneurship and funding innovation”. Digital sovereignty has emerged as a fundamental prerequisite for technological and economic competitiveness and security, particularly as AI technologies become increasingly pervasive.

The concept operates across multiple layers:

Data Sovereignty

This involves control over data creation, storage, processing, and transfer. With AI systems requiring vast amounts of data for training and operation, questions of data ownership become paramount. As Thomas Taroni, Executive Chairman of Phoenix Technologies, notes, “Developing AI systems necessitates access to vast amounts of sensitive information, including patents, emails, messages and legal documents”. Ensuring this data remains under appropriate control is essential for maintaining sovereignty.

Technological Sovereignty

This dimension focuses on control over the technological infrastructure and capabilities necessary for AI development and deployment. The European Union has been particularly active in pursuing technological sovereignty through comprehensive regulatory frameworks including the AI Act, Digital Markets Act (DMA), and Digital Services Act (DSA). These efforts aim to reduce dependence on non-EU tech companies and ensure that AI development complies with European values and regulations.

Implementation Sovereignty

This refers to the ability to deploy and operate AI systems according to one’s own policies and values. As nations develop “Sovereign AI,” they seek not isolation but strategic resilience – building domestic AI capabilities while participating in global innovation ecosystems.

The Value and Challenges of AI Assistance

AI assistance encompasses a range of technologies that augment human capabilities, from conversational AI Assistants to sophisticated systems that automate complex tasks. These technologies are powered by Large Language Models and other advanced AI architectures that have demonstrated remarkable capabilities in understanding and generating human-like text and solving complex problems.

Benefits of AI Assistance

AI assistance technologies offer significant advantages across sectors:

Increased productivity through automation of routine tasks
Enhanced decision-making supported by data analysis
Improved customer experiences through personalized interactions
Accelerated innovation through rapid prototyping and development

As AWS notes, “From accelerating research and enhancing customer experiences to optimizing business processes, improving patient outcomes, and enriching public services, the transformative potential of AI is being realized across sectors”.

Sovereignty Challenges

Despite these benefits, AI assistance technologies often present sovereignty challenges:

Data collection and processing may occur outside national or organizational boundaries
Dependency on foreign AI technologies and infrastructure
Potential vulnerability to foreign legal frameworks (e.g., the US Cloud Act, which “ostensibly gives the US government access to any data managed by a US provider”)
Risk of losing competitive advantage in critical AI capabilities

These concerns have led to increased emphasis on sovereign approaches to AI development and deployment.

Human-in-the-Loop (HiTL) Systems: Preserving Agency in AI Deployment

Human-in-the-Loop (HiTL) approaches represent a crucial strategy for balancing AI assistance with sovereignty requirements. These systems integrate human oversight and decision-making authority within AI workflows, ensuring that AI remains a tool rather than an autonomous decision-maker in critical contexts.

Strategic Implementation of HiTL

Implementing HiTL effectively requires:

Identifying critical decision points where human judgment is essential
Designing interfaces that facilitate meaningful human oversight
Training humans to work effectively alongside AI systems
Establishing clear accountability frameworks

HiTL systems can be particularly valuable in sovereignty-sensitive contexts, as they allow organizations to leverage AI capabilities while maintaining human control over sensitive processes and data. This approach ensures that AI assistance remains aligned with organizational values and regulatory requirements.

HiTL in Practice

In practice, HiTL systems might involve:

Human review of AI-generated content before publication
Expert verification of AI-suggested decisions in healthcare or legal contexts
Human oversight of AI-powered security monitoring
Collaborative workflows where AI suggests options but humans make final decisions

The HiTL paradigm recognizes that while AI systems may excel at pattern recognition and data processing, human judgment remains essential for contextual understanding, ethical reasoning, and strategic thinking.

AI Application Generators: Building Sovereign AI Solutions

AI Application Generators (also called AI App Builders or AI App Generators) offer a promising approach to developing sovereign AI solutions. These tools enable organizations to create AI-powered applications without extensive development resources, democratizing access to AI capabilities.

Sovereign Development Through AI Application Generators

AI Application Generators allow organizations to “accelerate the development of generative AI-powered applications with a combination of low-code APIs and code-first orchestration”. This capability is particularly valuable for organizations seeking to maintain sovereignty while leveraging AI, as it enables:

Local development and deployment of AI applications
Customization of AI solutions to meet specific regulatory requirements
Reduced dependency on external vendors
Preservation of organizational control over data and algorithms

As noted in the search results, these generators are “revolutionizing how enterprise applications are built,” allowing developers to create sophisticated products with reduced development effort and time10.

Architectural Considerations

When implementing AI Application Generators for sovereign solutions, organizations should consider:

Data residency and processing requirements
Integration with existing sovereignty-compliant systems
Security and privacy controls
Alignment with organizational governance frameworks

By carefully selecting and implementing AI Application Generators, organizations can develop AI solutions that advance their objectives while maintaining appropriate control over their digital assets.

Strategic Frameworks for Balancing AI Assistance and Sovereignty

Achieving the optimal balance between AI assistance and digital sovereignty requires comprehensive strategic frameworks that address technological, regulatory, and organizational dimensions.

Six Strategic Pillars for Sovereign AI

Drawing from World Economic Forum insights, nations and organizations can build sovereignty through six strategic pillars:

Data Infrastructure and Management: Building robust data infrastructure that enables secure data collection, storage, and processing within sovereign boundaries.
AI Research and Development: Investing in domestic AI research capabilities to reduce dependency on external technologies.
Talent Development: Cultivating local AI expertise through education and training programs.
Regulatory and Ethical Framework: Establishing clear guidelines for AI development and deployment that balance innovation with ethical considerations and regulatory compliance.
Stimulating AI Industry: Creating conducive environments for AI-driven businesses through incentives, grants, and public-private partnerships.
International Cooperation: Engaging in dialogues and partnerships with other nations to set global standards while protecting sovereign interests.

Technological Enablers for Digital Sovereignty

Several technological approaches can enhance sovereignty while leveraging AI assistance:

Open-Source Solutions: Open-source AI technologies provide transparency and control, reducing dependency on proprietary systems. As German federal states note, “open source models and open source software and hardware offer an option to create competitive, secure and transparent alternatives to proprietary systems”.
Sovereign Cloud Implementation: Deploying cloud infrastructure that aligns with specific geographic and legal requirements ensures data residency and compliance with local regulations.
Low-Code Platforms: These tools enable organizations to “rapidly develop custom solutions that align with their specific requirements”, reducing dependency on external vendors.
Open Digital Infrastructure: Investing in open digital infrastructure is essential for “the development of sovereign AI” as it keeps “AI development accessible and open to competition”.

Implementing Balanced Approaches: Case Studies and Best Practices

The European Approach to Sovereign AI

The European Union has taken a proactive stance on balancing AI advancement with sovereignty concerns. Through its comprehensive regulatory framework, including the AI Act, DMA and DSA, the EU has emphasized a human rights-oriented approach to AI governance.

Key elements of the EU approach include:

Conformity assessments for high-risk AI systems
Transparency requirements for AI systems
Emphasis on open standards and interoperability
Support for local AI innovation and infrastructure

As noted by the Sovereign Tech Agency, “True technological sovereignty is not built by betting on a few unicorns; it requires investing in open digital infrastructure to foster competition and innovation for all”.

Sovereign Cloud Implementation for AI Workloads

Organizations seeking to maintain sovereignty while leveraging cloud-based AI capabilities can implement sovereign cloud solutions. These deployments ensure that data remains within specific jurisdictions and under appropriate control.

Oracle’s approach to Sovereign AI demonstrates this balance by offering “increased control over where you run your AI workloads and how you manage data and operate infrastructure”. Their distributed cloud offers various deployment models that address specific sovereignty requirements while enabling advanced AI capabilities.

Enterprise Computing Solutions with Sovereignty Safeguards

Organizations implementing Enterprise Computing Solutions can enhance digital sovereignty by carefully selecting and implementing business software that maximizes control while delivering necessary functionality. This balanced approach evaluates solutions not only on their technical capabilities but on how they contribute to the organization’s sovereignty goals.

Future Outlook: Evolving Balance in the AI Era

As AI technologies continue to advance, the balance between AI assistance and digital sovereignty will remain dynamic. Several trends will shape this evolution:

Emerging Sovereign AI Technologies

The development of sovereign AI technologies, including foundation models trained on locally controlled data, will provide alternatives to dominant models from large tech companies. These developments will enable more organizations to leverage AI while maintaining control over critical aspects of their digital infrastructure.

Evolving Regulatory Landscapes

Regulatory frameworks will continue to evolve as governments seek to protect sovereignty while enabling innovation. The implementation of frameworks like the EU AI Act will provide valuable insights into effective balanced approaches.

Growing Emphasis on Open Technologies

Open-source AI technologies will gain importance as tools for maintaining sovereignty while fostering innovation. As German federal states emphasize, establishing “binding open standards for AI models, interfaces and data formats” is necessary “in order to guarantee sustainable digital sovereignty”.

International Cooperation on Balanced Frameworks

While pursuing sovereignty, nations will increasingly recognize the importance of international cooperation on AI governance. As the World Economic Forum notes, “This push for Sovereign AI does not necessarily mean digital isolation, but rather a push for strategic resilience — and it can be done in tandem with global cooperation”.

Conclusion

Balancing AI assistance and digital sovereignty represents one of the most significant challenges and opportunities of our digital era. By implementing strategic frameworks that incorporate Human-in-the-Loop approaches, leverage AI Application Generators, and adopt sovereignty-focused technological infrastructures, organizations and nations can harness the transformative potential of Large Language Models and AI systems while maintaining necessary control over their digital destiny.

The path forward requires thoughtful integration of technological, regulatory, and organizational measures that preserve sovereignty without sacrificing innovation. As AI continues to transform our world, those who successfully navigate this balance will be best positioned to thrive in the evolving digital landscape.

Rather than viewing AI assistance and digital sovereignty as opposing forces, we must recognize them as complementary elements of a responsible and sustainable approach to technological advancement. Through balanced strategies that combine the best of human intelligence with artificial intelligence, we can create a future where technology serves our values and priorities rather than determining them.

References:

The Evolving Landscape of Salesforce Competitors in 2025

April 22, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction: How AI is Reshaping the CRM Market

As we navigate through 2025, the competitive landscape surrounding Salesforce continues to evolve dramatically, primarily driven by artificial intelligence innovations. Companies are no longer competing solely on traditional CRM features but are increasingly differentiating themselves through AI capabilities, custom application development tools, and specialized industry solutions. This comprehensive analysis examines how Salesforce competitors are leveraging cutting-edge technologies to challenge the market leader’s dominance and create unique value propositions for businesses of all sizes.

Major Players and Their AI-Driven Strategies

The competitive field challenging Salesforce has expanded beyond traditional CRM providers to include technology giants and specialized AI-focused companies, each bringing unique capabilities to the market.

Enterprise Giants with Comprehensive Solutions

Oracle stands as a formidable Salesforce competitor with extensive cloud offerings. Its development approach combines human interaction and artificial intelligence to create advanced functionality. Oracle’s strengths in data science are evident in their advanced analytics and AI technologies that enhance customer insights, including their code app builder that distinguishes them in the market.

Microsoft Dynamics 365 has positioned itself as a unified solution combining CRM and ERP capabilities. This integration allows businesses to manage customer relationships while simultaneously handling operational aspects, creating a more cohesive business management experience. Their AI integration focuses on streamlining workflows and providing predictive insights.

SAP continues to leverage its enterprise resource planning heritage while strengthening its CRM features. Its approach incorporates human expertise with unsupervised learning to provide solutions that exceed typical CRM functionality.

Specialized Competitors with Focused Value Propositions

Adobe Experience Cloud has emerged as a significant competitor by focusing specifically on customer experience management. They prioritize human interaction and artificial intelligence in their development process, providing smart analytics and AI tools that offer deep insights into customer behavior.

Zoho CRM has gained market share by offering affordable and customizable solutions. Their AI-driven assistant, Zia, provides predictive sales analytics, lead scoring, and sentiment analysis, enabling businesses to make data-informed decisions.

HubSpot CRM continues to be a strong alternative for businesses seeking comprehensive marketing automation alongside CRM functionality. Their platform gives complete real-time sales pipeline visibility and integrates AI capabilities for enhanced marketing and sales operations.

The Rise of AI Assistants in CRM Platforms

AI Assistance Transforming Customer Interactions

AI Assistants have become central differentiators among Salesforce competitors, with various implementations addressing different aspects of customer relationship management.

Salesforce itself has developed Einstein AI, its native artificial intelligence solution that predicts sales closures, rates leads, and offers customer behavior analysis. However, competitors are rapidly developing their own AI capabilities to challenge this offering.

Harmonix AI, which integrates with Salesforce and other CRM platforms, offers automatic recording of calls, emails, chats, and meetings, providing a unified view of all customer interactions. This AI Assistant can suggest next steps and personalized follow-up emails while automating administrative tasks.

Other specialized AI Assistants like Gong analyze business calls to identify risks and opportunities in every conversation, providing sales coaching based on real data. Similarly, Drift automates conversations with leads using smart chat that qualifies prospects in real time.

The Competitive Edge of Next-Generation AI Assistance

In 2025, we’re seeing a shift toward more autonomous AI agents. Salesforce’s own predictions indicate that “In 2025, we’ll increasingly see more complex, multi-agent orchestrations solving higher-order challenges across the enterprise, like simulating new product launches or marketing campaigns and developing recommendations for adjustments”.

Competitors like Cobbai are developing purpose-built AI agents for customer service designed to resolve inquiries autonomously and integrate seamlessly with business systems. These agents can be deployed across multiple platforms, including Zendesk, Freshdesk, and Intercom, offering platform-agnostic AI assistance that directly challenges Salesforce’s more ecosystem-dependent approach.

Human-in-the-Loop (HiTL) Integration Approaches

Balancing Automation with Human Expertise

Human-in-the-Loop (HiTL) approaches have become increasingly important in differentiating CRM solutions. These approaches combine the efficiency of AI with critical human oversight and intervention.

Several Salesforce competitors are emphasizing HiTL methodologies in their development processes. As noted in industry analyses, companies like Oracle and Adobe Experience Cloud combine “human expertise with unsupervised learning” to provide features that exceed typical CRM systems.

The Human-in-the-Loop paradigm represents a significant shift from fully automated systems to hybrid models where AI handles routine tasks while escalating complex issues to human agents. This creates a more balanced approach that maintains the efficiency of automation while preserving the quality and judgment that human agents bring.

Specialized Human in the Loop Competitors

The search results mention several specialized companies focused on Human-in-the-Loop AI services that could potentially integrate with or compete with aspects of Salesforce’s offering:

Companies like Humans in the Loop, Cogito, Alegion, Lionbridge AI, iMerit, Scale AI, and Appen specialize in data annotation and collection services for AI systems. These services can significantly enhance the training and performance of AI systems used in CRM platforms.

These specialized HiTL providers represent potential partners or acquisition targets for larger CRM competitors looking to enhance their AI capabilities with strong human oversight components.

AI Application Generators and App Builders Reshaping Development

Low-Code and No-Code AI App Generators

AI Application Generators and AI App Builders have emerged as crucial differentiators in the CRM space, allowing businesses to create custom applications with minimal coding requirements.

Salesforce alternatives like Aire AI App Builder compete in this space. Other alternatives include Airtable, Appy Pie, Nintex, and Quickbase, which provide no-code development platforms with varying levels of AI integration.

ServiceNow App Engine exemplifies this trend by emphasizing the ability to “build low-code apps quickly, with more creators and less complexity.” This platform claims to bring “new enterprise apps to market in half the time and one-third the cost with low-code apps”.

Custom UI Generation with AI App Generators

Buildox represents an innovative approach as an AI Application Generator specifically tailored for Salesforce. This web application creates Salesforce UI components (Lightning Web Components) based on text descriptions. Users specify the component they want, and the AI produces the HTML, CSS, and JavaScript code with a live preview for adjustments.

This trend toward AI-powered development tools suggests that in 2025, the ability to rapidly create custom applications without extensive coding knowledge has become a key competitive factor. As one consultant noted regarding Buildox: “It would be useful for me to pitch an idea to a customer and be able to show them the UI on the fly, get their input real time and develop it together”.

The Role of Large Language Models in CRM Innovation

Large Language Models Powering Next-Generation Capabilities

Large Language Models (LLMs) have become foundational technologies driving innovation across the CRM landscape. Salesforce has incorporated LLMs into its Atlas Reasoning Engine, which “employs multiple large language models (LLMs), large action models (LAMs), and specialized RAG modules to perform distinct subtasks like (re)ranking, refining, and synthesizing, leading to state-of-the-art levels of trustable autonomy”.

Competitors are also leveraging LLMs to enhance their offerings. Mistral AI’s Mistral Large 2 model, Pixtral Large multimodal model, and Le Chat chatbot are direct competitors to other state-of-the-art language models that could potentially integrate with CRM systems.

Enhanced Customer Interactions Through Language Understanding

The integration of Large Language Models into CRM systems has transformed how businesses interact with customers. These models enable more natural conversation flows, better understanding of customer intent, and the ability to generate personalized responses at scale.

As demonstrated in a Salesforce keynote, when a generative AI assistant taps into data that already lives on Salesforce, it can provide exceptional customer experiences. In one demonstration, an AI assistant named Sophie helped a customer locate a product for same-day pickup by accessing store inventory data.

How Smaller Competitors Are Leveraging AI Against Salesforce

AI as an Equalizer for Small and Medium Businesses

One significant trend in 2025 is how smaller businesses are using AI-powered CRM alternatives to compete more effectively with larger enterprises. According to Salesforce’s own predictions, “SMBs will surpass large competitors by leveraging AI agents”.

More affordable Salesforce alternatives like Zoho CRM, Pipedrive, and Monday CRM are particularly attractive to small and medium businesses. These platforms offer AI capabilities at lower price points, making advanced customer relationship management accessible to organizations with limited resources.

Specialized AI Solutions for Niche Markets

Smaller competitors are also finding success by developing specialized AI solutions for specific industries or use cases. For example, Regie.ai focuses on generating personalized outreach content at scale, essentially serving as an “AI writing assistant” for sales teams.

Similarly, Gem-E by UserGems specializes in relationship tracking and intent-based lead generation, mining historical data to identify “hidden champions” like past customers who’ve moved to new companies.

Future Predictions for the Salesforce Competitive Landscape

The Battle of AI Titans

Industry analysts are closely watching the competition between technology giants in the AI-enhanced CRM space. Wedbush has identified both Palantir Technologies and Salesforce as their “top AI software picks for 2025”.

Palantir, though not traditionally considered a direct Salesforce competitor, is increasingly entering similar territory with its data integration and AI capabilities. Its platforms – Gotham, Foundry, Apollo, and AIP – are attracting attention for their AI-driven success in both government and commercial sectors.

Autonomous AI Agents as the Next Frontier

The development of increasingly autonomous AI agents appears to be the next major competitive battleground. As Mick Costigan, VP of Salesforce Futures, notes: “In 2024, AI agents began augmenting people in simpler use cases, such as sales and service. In 2025, we’ll increasingly see more complex, multi-agent orchestrations solving higher-order challenges across the enterprise”.

Competitors like Cobbai are directly challenging Salesforce in this area, claiming their “AI agents are purpose-built for customer service, designed to resolve inquiries autonomously and integrate seamlessly with business systems,” while criticizing Salesforce’s AgentForce AI as “a repackaged version of its older Einstein AI, which has been criticized for poor response accuracy and basic AI capabilities”.

Conclusion

The competitive landscape surrounding Salesforce in 2025 is defined by accelerating AI innovation across multiple dimensions. From AI Assistants that automate customer interactions to Human-in-the-Loop approaches that balance automation with human expertise, from AI Application Generators that democratize development to Large Language Models that enable more sophisticated understanding and communication – AI has become the primary battleground for CRM dominance.

Salesforce maintains significant advantages due to its established market position and extensive customer base, with “90% of Fortune 500 companies already us[ing] their platform”. However, competitors ranging from enterprise giants like Oracle and Microsoft to specialized AI-focused companies and nimble startups are all finding ways to differentiate themselves through innovative AI applications.

As the market continues to evolve, organizations evaluating CRM solutions will increasingly base their decisions not just on traditional CRM capabilities but on the depth, flexibility, and effectiveness of AI integration. The most successful competitors will be those that can deliver tangible business outcomes through AI while maintaining the usability, customization, and integration capabilities that businesses require.

References:

Enterprise Insights: How To Create Apps Without Coding

April 21, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

As enterprises race to digitize operations and respond to market demands with greater agility, the ability to create applications without traditional coding has become a strategic advantage. Today’s no-code and low-code platforms, enhanced by artificial intelligence, are revolutionizing how businesses develop software solutions. This comprehensive report explores how enterprises can leverage these technologies to create sophisticated applications quickly and efficiently, with special focus on AI-powered tools and human-in-the-loop approaches that optimize the development process.

The Evolution of No-Code Development in Enterprise Settings

The landscape of application development has undergone a profound transformation in recent years. According to Gartner, over 70 percent of enterprise apps will be built using low-code/no-code technologies by 2025. This shift represents a fundamental change in how organizations approach digital solution creation, moving from developer-centric processes to more democratized, business-user friendly approaches.

Enterprise low-code application platforms (LCAPs) are defined by Gartner as “platforms for accelerated development and maintenance of applications”. These platforms have matured significantly, offering visual interfaces, pre-built templates, and drag-and-drop functionality that enable even non-technical users to create functional, sophisticated applications. The value proposition is compelling: faster development cycles, reduced dependency on scarce technical talent, and greater business agility.

Key Drivers Behind Enterprise Adoption

Several factors have accelerated enterprise adoption of no-code solutions. The persistent shortage of skilled developers, combined with increasing pressure to digitize operations quickly, has created the perfect conditions for alternative development approaches to flourish. Business users are increasingly unwilling to wait months for IT departments to deliver solutions when market conditions demand immediate responses.

Moreover, the rise of digital transformation initiatives across industries has amplified the need for rapid application development capabilities that can be deployed by those closest to the business problems. This democratization of development empowers domain experts to create tailored solutions without the traditional technical barriers.

AI-Powered No-Code Platforms

The integration of artificial intelligence technologies, particularly Large Language Models (LLMs), has dramatically enhanced the capabilities of no-code platforms, creating a new generation of tools that further simplify application development while expanding what’s possible without coding.

AI Application Generators and AI App Builders

AI Application Generators represent one of the most significant advancements in the no-code space. These specialized tools leverage artificial intelligence to further simplify the app creation process, sometimes requiring little more than a description of what the user wants to build.

For example, Builder.ai employs an AI assistant named Natasha that guides users through the development process. Users can chat with Natasha about their app idea, and the AI will offer recommendations based on patterns identified from previously built applications. The system then uses AI to assemble app features like building blocks, calculates a fixed price, and provides accurate timelines – all without requiring users to understand programming concepts.

Similarly, Appy Pie’s AI App Generator allows users to simply describe their app concept, after which the AI handles all design complexities. Users manage the deployment process, making their app live in just a few steps. The platform enables the creation of professional Android and iOS apps without writing a single line of code, making sophisticated app development accessible to virtually anyone.

How Large Language Models Transform No-Code Development

Large Language Models (LLMs) have become instrumental in advancing no-code platforms by enabling more intuitive human-computer interactions. These sophisticated AI models can understand natural language instructions, generate code snippets, provide suggestions, and assist users throughout the development process.

No-Code LLM platforms empower users to leverage advanced language processing capabilities without requiring extensive programming knowledge. They provide intuitive interfaces and pre-built functionalities that allow users to create, deploy, and manage language models seamlessly, democratizing access to sophisticated AI tools.

Applications built with LLM integration offer enterprises powerful capabilities:

AI Assistants for content creation: Tools like CustomGPT can efficiently produce human-like text for marketing teams, expanding upon basic drafts by adding depth and detail with natural language understanding capabilities.
Intelligent document processing: LLM-powered applications can analyze, summarize, and extract key information from documents, automating previously manual workflows.
Advanced customer service: PolyAI enables the creation of high-quality voice assistants that can handle customer inquiries 24/7, providing natural conversational experiences.

Human-in-the-Loop: Ensuring Quality and Control

While AI-powered automation offers tremendous efficiency gains, the most successful no-code implementations recognize the critical importance of human oversight. Human-in-the-Loop (HiTL) approaches strategically combine human intelligence with automated systems to ensure optimal outcomes.

Understanding Human-in-the-Loop in No-Code Contexts

Human-in-the-Loop (HiTL) in the context of no-code development refers to workflows that incorporate strategic human intervention at key decision points. This approach acknowledges that while automation can handle routine tasks efficiently, human judgment remains essential for complex decisions, quality assurance, and exception handling.

HiTL is particularly valuable in no-code AI app builders, as it empowers individuals without coding experience to create AI-powered apps while maintaining control over critical aspects of the process. By incorporating human feedback at strategic points, HiTL enhances AI models, making them more adaptable and reliable for enterprise use cases.

Implementing HiTL in Enterprise Applications

Several platforms now offer sophisticated HiTL capabilities designed specifically for enterprise workflows. For example, Make’s Human in the Loop modules allow users to add human approval steps where needed to ensure accuracy, compliance, and accountability in automated or semi-automated workflows. These modules include functions for creating review requests, managing approvals, and tracking completed reviews.

Lindy.ai offers another approach to HiTL automation, allowing users to build their own AI agents to automate business workflows while maintaining appropriate human oversight. The platform provides flexible options for human involvement, such as requesting confirmation before taking important actions or sending real-time updates via email or Slack so users can intervene only when necessary.

Benefits of HiTL for Enterprise Applications

The strategic incorporation of Human-in-the-Loop approaches offers several critical benefits for enterprise application development:

Risk mitigation: Human oversight helps identify and correct errors that automated systems might miss, particularly for high-stakes processes where mistakes could have significant consequences.
Regulatory compliance: In heavily regulated industries, human review can ensure that automated processes meet legal and regulatory requirements.
Continuous improvement: Human feedback provides valuable input for refining and improving automated systems over time, creating a virtuous cycle of enhancement.
Exception handling: While automated systems excel at routine tasks, human intervention is invaluable for managing edge cases and unusual situations that weren’t anticipated in the original design.

Leading No-Code Platforms for Enterprise Use

The market for enterprise no-code solutions has expanded dramatically, with a range of platforms offering different capabilities and specializations. Here are some leading options that enterprises should consider:

Comprehensive Enterprise Platforms

BRYTER: A no-code platform designed specifically for knowledge work automation, enabling professionals in fields such as law, finance, tax, and compliance to build interactive applications without programming skills1.
Studio Creatio: A globally recognized provider of no-code platforms alongside Customer Relationship Management (CRM) applications, primarily targeting workflow automation across various industries.
Joget: An open source platform that combines business process automation, workflow management, and rapid application development within a simple, flexible environment.

Specialized AI-Enhanced Solutions

Builder.ai: A composable software development platform featuring Natasha, an AI assistant that guides users through the app development process and helps calculate costs and timelines.
Appy Pie: An AI-powered no-code platform for creating apps, websites, and chatbots without coding skills, offering professional templates and simple deployment processes.
Fuzen.io: A free no-code LLM builder that enables users to create and manage language models without writing code, reducing learning curves and development time significantly.

Industry Applications and Success Stories

No-code platforms are being successfully deployed across various industries to address specific business challenges and opportunities:

Healthcare Applications

Healthcare organizations are using no-code platforms to create applications that improve patient experiences and streamline operations. Builder.ai highlights how healthcare providers can deliver better patient experiences with cloud-based registration systems, appointment reminders, and prescription dashboards. These applications help address critical industry challenges like patient engagement and administrative efficiency without requiring extensive development resources.

Financial Services Innovation

The financial services sector has embraced no-code development to create everything from customer onboarding applications to regulatory compliance tools. Builder.ai notes that their platform makes it “fast, easy and cost-effective” for financial institutions to create specialized software without technical skills. This capability is particularly valuable in an industry that faces both intense competitive pressure and strict regulatory requirements.

Retail and E-commerce Solutions

Retailers are leveraging no-code platforms to build applications for inventory management, staff scheduling, and sales tracking. These tools help simplify complex operational processes while enabling better customer experiences across physical and digital channels. The ability to quickly adapt to changing consumer behaviors and market conditions is particularly valuable in the fast-moving retail sector.

Benefits and Considerations for Enterprises

No-code development offers significant advantages for enterprises, but also requires careful consideration of potential limitations and implementation challenges.

Key Advantages

Development speed: No-code platforms drastically shorten development timelines, allowing applications to be built in days rather than months or years. This acceleration enables businesses to respond more quickly to market opportunities and operational challenges.
Reduced technical debt: By using standardized components and architectures, no-code platforms can help reduce the accumulation of technical debt that often plagues custom development projects.
Democratized innovation: These platforms enable business users to create solutions to their own problems, fostering innovation throughout the organization rather than restricting it to IT departments.
Cost efficiency: The combination of faster development cycles and reduced dependency on scarce (and expensive) development talent can significantly lower the total cost of application development and maintenance.

Implementation Considerations

Governance and security: As application development becomes more distributed throughout the organization, enterprises must establish clear governance frameworks to ensure security, compliance, and quality standards are maintained.
Integration capabilities: Enterprises should carefully evaluate how well no-code platforms can integrate with existing systems and data sources, as seamless integration is often critical for business applications.
Scalability limits: Some no-code solutions may face challenges with very high user loads or complex processing requirements, potentially limiting their applicability for certain enterprise scenarios.
Customization boundaries: While no-code platforms continue to expand their capabilities, there may still be limits to customization for highly specialized or complex requirements.

The Future of No-Code Development in Enterprises

The evolution of no-code platforms continues to accelerate, with several important trends shaping their future role in enterprise application development:

AI Enhancement and Automation

AI capabilities will become increasingly sophisticated, automating more aspects of the development process while providing more intelligent assistance to non-technical creators. Advanced AI assistants will offer contextual recommendations, automatically generate complex logic, and even predict user needs based on organizational patterns.

Expanded Enterprise Capabilities

No-code platforms will continue to develop more robust enterprise features, including enhanced security controls, governance frameworks, and compliance capabilities. This evolution will make these platforms increasingly viable for mission-critical applications in regulated industries.

Convergence with Professional Development

The line between no-code platforms and traditional development environments will continue to blur, with more platforms offering “escape hatches” that allow professional developers to extend no-code applications with custom code when necessary. This hybrid approach will enable enterprises to leverage the speed of no-code development while maintaining the flexibility to address unique requirements.

Conclusion

The rise of AI-powered no-code platforms represents a fundamental shift in how enterprises approach application development. By combining the accessibility of visual development tools with the power of AI and the control of Human-in-the-Loop approaches, these platforms enable organizations to create sophisticated applications faster and more cost-effectively than ever before.

As enterprises continue to face growing demands for digital transformation with limited technical resources, no-code development will play an increasingly important role in their technology strategies. By carefully selecting the right platforms, implementing appropriate governance frameworks, and strategically incorporating human oversight, organizations can harness the full potential of no-code development to drive innovation, improve operational efficiency, and respond more quickly to changing business needs.

The future belongs to enterprises that can effectively balance the speed and accessibility of AI-powered no-code development with the quality assurance and human judgment provided by Human-in-the-Loop approaches, creating a development ecosystem that combines the best of both automation and human expertise.

References:

The Limitations of No-Code Automation

April 21, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

No-code automation platforms have revolutionized how businesses approach software development and AI implementation, enabling users without technical expertise to create functional applications. While these tools, including AI Application Generators and AI App Builders, have democratized development, they come with significant limitations that users should understand before committing to these solutions. This report examines the key constraints of no-code automation platforms, with special attention to AI-powered solutions and Human-in-the-Loop (HITL) systems.

Technical Limitations and Customization Constraints

No-code platforms fundamentally restrict users to predefined building blocks and templates, creating inherent limitations in what can be accomplished without traditional coding.

Restricted Customization Options

No-code automation platforms typically use a limited set of building blocks for creating applications, making it challenging to develop solutions with specific or complex requirements. This limitation becomes particularly evident when users attempt to implement unique business logic or specialized functionalities that fall outside the platform’s predefined components.

For AI App Generators specifically, the limitations in customization can restrict the sophistication of AI functionalities that can be implemented. While these platforms might offer drag-and-drop interfaces for basic AI features, they often lack the flexibility needed for advanced AI implementations that could otherwise be achieved through custom coding.

Complex Functionality Barriers

When working with AI Assistants and integrating advanced features, no-code platforms often fall short. These tools are typically designed for general-purpose applications and struggle with niche functionalities. For instance, implementing specialized algorithms, advanced natural language processing beyond what a Large Language Model directly offers, or complex decision trees often exceeds the capabilities of no-code platforms.

Code Quality and Performance Issues

Applications built using AI App Builders may suffer from suboptimal code quality, leading to performance issues, especially at scale. Since users don’t have direct access to the underlying code, they cannot optimize it for specific use cases or improve efficiency through custom solutions. The generated code might not follow best practices, potentially resulting in slower execution times and higher resource consumption.

Scalability and Performance Constraints

One of the most significant limitations of no-code automation tools involves their ability to handle growth and maintain performance under increased loads.

Limited Handling of Data Volume

No-code platforms generally struggle to efficiently manage large volumes of data or users, creating challenges when applications need to scale. This limitation becomes particularly problematic for businesses experiencing rapid growth or processing significant amounts of information.

Resource Inefficiency

AI App Generators often create applications that are not optimized for resource usage. The resulting applications might consume more processing power, memory, or storage than custom-built alternatives, leading to higher operational costs and potentially degraded user experiences.

Response Time Degradation

As user interactions or data processing requirements increase, no-code applications frequently experience slower response times. This degradation can negatively impact user satisfaction and overall application effectiveness, particularly for time-sensitive operations where immediate responses are crucial.

Integration and Interoperability Challenges

Modern business environments require seamless connections between various systems, an area where no-code platforms often encounter significant obstacles.

API Limitations

No-code tools may not support complex API calls or advanced authentication mechanisms, limiting their ability to integrate with other systems. This constraint can be particularly problematic when attempting to connect with legacy systems or specialized services that require sophisticated API interactions.

Real-Time Syncing Issues

Maintaining data consistency across multiple systems poses challenges for no-code platforms, particularly when real-time synchronization is required. These limitations can lead to data discrepancies, processing delays, or failed operations when information needs to be current across different applications or services.

Ecosystem Dependencies

Applications built with AI App Builders often operate within closed ecosystems, creating potential interoperability issues with external systems. This dependency can limit the application’s ability to work with other tools or services, constraining its overall utility and flexibility.

Human-in-the-Loop (HITL) Implementation Challenges

No-code platforms offer promising capabilities for Human-in-the-Loop systems, but implementing effective HITL workflows comes with unique challenges.

Limited HITL Workflow Flexibility

While Human-in-the-Loop approaches are valuable for enhancing AI system performance, no-code platforms often provide limited options for implementing sophisticated HITL workflows. These constraints can reduce the effectiveness of human intervention and oversight in complex decision-making processes.

HITL Interface Limitations

Creating effective interfaces for Human in the Loop interactions requires careful design considerations that may exceed the capabilities of no-code platforms3. These limitations can impact the quality of human-AI collaboration, potentially reducing the overall effectiveness of the HITL system.

Integration of AI Assistance with Human Expertise

No-code platforms may struggle to effectively balance automated AI processing with human expertise in HITL systems. This limitation can lead to suboptimal allocation of tasks between AI and human operators, reducing the potential benefits of the hybrid approach.

Security and Compliance Concerns

Security considerations present significant challenges for applications built using no-code automation tools.

Limited Security Controls

No-code platforms might not provide the necessary security controls required for applications handling sensitive data or operating in highly regulated industries. This limitation can expose organizations to potential vulnerabilities or compliance issues.

Regulatory Compliance Challenges

Applications built with AI App Generators may struggle to meet stringent compliance requirements in industries like healthcare, finance, or government. These challenges can limit the applicability of no-code solutions in regulated sectors where specific security and privacy measures are mandated.

Data Privacy Vulnerabilities

No-code platforms might not offer comprehensive data protection features, creating potential privacy concerns for applications processing personal or sensitive information. These limitations can increase organizational risk, particularly in jurisdictions with strict data protection regulations.

Business and Strategic Limitations

Beyond technical constraints, no-code automation platforms present several business-related limitations that organizations should consider.

Vendor Lock-In

Relying on specific no-code platforms can lead to vendor lock-in, making it difficult and costly to migrate to alternative solutions if business needs change. This dependency can limit organizational flexibility and potentially increase long-term costs.

Intellectual Property Concerns

Applications developed using AI App Builders may have unclear intellectual property rights, particularly regarding the AI-generated components. These uncertainties can create legal and business complications, especially for organizations with strict IP requirements.

Innovation Limitations

The constrained nature of no-code platforms can inhibit technological innovation, potentially limiting competitive advantages for businesses seeking to differentiate through unique software capabilities. Organizations focused on cutting-edge solutions may find no-code tools insufficient for their innovation needs.

Large Language Model Integration Challenges

Large Language Models present specific challenges when integrated into no-code automation platforms.

LLM Hallucinations and Accuracy Issues

When no-code platforms incorporate Large Language Models, they inherit the LLMs’ tendencies to generate inaccurate or fabricated information. These “hallucinations” can compromise the reliability of applications, particularly those requiring factual precision or domain-specific accuracy.

Knowledge Update Limitations

Large Language Models integrated into no-code solutions typically have knowledge cutoffs, meaning they lack awareness of recent events or information. This limitation can reduce the relevance and utility of applications requiring current knowledge.

Context Window Constraints

No-code platforms utilizing LLMs often face challenges with limited context windows, restricting the amount of information that can be processed simultaneously. These constraints can impact the effectiveness of applications requiring comprehensive context understanding or processing of lengthy documents.

Conclusion

While no-code automation platforms, including AI Application Generators and AI App Builders, offer significant advantages in terms of accessibility and development speed, they come with substantial limitations that must be carefully considered. From technical constraints and scalability issues to integration challenges and security concerns, these limitations can significantly impact the suitability of no-code solutions for specific use cases.

Organizations should evaluate these constraints against their specific requirements, considering both immediate needs and long-term objectives. For some applications, particularly those with straightforward requirements or where rapid development is prioritized over customization, no-code solutions can be highly effective. However, for complex, specialized, or highly scalable applications, traditional development approaches or hybrid solutions that incorporate Human-in-the-Loop systems may prove more suitable.

Understanding these limitations enables more informed decision-making regarding no-code adoption, helping organizations maximize the benefits of these platforms while mitigating potential risks and challenges. As no-code technologies continue to evolve, some of these limitations may be addressed, but a realistic assessment of current capabilities remains essential for successful implementation.

References:

The AI Assistant and LLM Sovereignty

April 11, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

The emergence of advanced AI Assistants powered by Large Language Models (LLMs) has transformed how we interact with technology while raising critical questions about data sovereignty, privacy, and the role of human oversight. As these technologies rapidly evolve, organizations and governments face the challenge of harnessing their potential while maintaining control over sensitive data and ensuring alignment with local values and regulations.

Understanding Sovereign LLMs and Their Significance

Sovereign Large Language Models represent a new paradigm in artificial intelligence development tailored to specific national or regional requirements. Unlike general-purpose LLMs developed by multinational corporations, Sovereign LLMs are designed and operated with a focus on local languages, dialects, cultural nuances, and regulatory frameworks.

These specialized Large Language Models offer several distinct advantages over their global counterparts. They can help revitalize and preserve endangered languages, empower linguistic minorities who may not be fluent in official languages, and address nation-specific research priorities. Moreover, they foster greater public trust in AI by aligning with local cultural norms, historical contexts, and ethical values that resonate with the populations they serve.

The primary motivation behind developing Sovereign LLMs stems from the recognition that globally available AI models often reflect the biases, legal frameworks, and ethical standards of their countries of origin. This creates a misalignment when these technologies are deployed in regions with different regulatory environments, cultural contexts, and socioeconomic priorities.

Benefits of Sovereign LLMs for AI Assistance

When implemented as the foundation for AI Assistants, Sovereign LLMs provide enhanced compliance with local regulations, greater alignment with domestic policies, and improved data protection measures. This is particularly crucial for applications in sensitive domains such as healthcare, government services, and financial institutions, where data sovereignty concerns are paramount.

For instance, an AI Assistant powered by a Sovereign LLM can better understand regional dialects, cultural references, and local regulations, resulting in more accurate and contextually appropriate assistance. Furthermore, the data processed by such systems can remain within national borders, addressing concerns about foreign access to sensitive information.

Human-in-the-Loop: The Essential Component for Responsible AI Assistants

Human-in-the-loop (HITL) is a collaborative AI approach that integrates human intelligence with machine learning to enhance decision-making processes. This hybrid methodology stands in contrast to fully automated AI systems by incorporating critical human judgment at various stages of the AI lifecycle.

How HITL Functions in AI Assistant Development

In a HITL system, human operators fulfill three primary roles: labeling training data to establish ground truth, tuning the machine learning model by scoring outputs, and validating final decisions to ensure accuracy and appropriateness. This human oversight is particularly valuable for addressing complex scenarios or edge cases where pure machine intelligence might struggle.

The implementation of Human in the Loop processes for AI Assistants ensures that these systems remain accountable and aligned with human values. For example, when an AI Assistant encounters a query it cannot confidently address, a human can intervene to provide the correct response, which then becomes part of the system’s training data for future improvement.

Enhancing AI Assistant Performance Through HITL

The integration of HITL approaches dramatically improves the effectiveness of AI Assistants. According to industry data, AI Assistants developed with robust HITL methodologies can achieve success rates of 96% on average, with some reaching as high as 99.88% when properly trained. This stands in stark contrast to competitors without effective HITL processes, which typically achieve less than 50% success rates.

Modern HITL implementations have also become more accessible, no longer requiring extensive technical expertise. Today, anyone with domain knowledge can participate in training and refining AI Assistants through user-friendly interfaces that automatically generate training suggestions based on unrecognized queries.

AI Application Generators: Democratizing AI Assistant Development

The rise of AI Application Generators and AI App Builders has significantly lowered the barrier to entry for creating custom AI-powered applications. These tools allow users to design and deploy sophisticated applications without requiring coding expertise, effectively democratizing access to AI technology.

Features and Capabilities of AI App Generators

Platforms like Jotform’s AI App Generator enable users to describe their desired application through natural language conversation, after which the AI creates customized apps for various business purposes. Similarly, Apsy’s AI-driven app builder transforms ideas into functional applications rapidly through an intuitive interface where users can simply communicate their vision.

These AI App Builder platforms typically offer:

1. No-code development environments accessible to non-technical users
2. Customization options for branding, design, and functionality
3. Integration capabilities with existing systems and payment processors
4. Cross-platform compatibility for mobile, tablet, and desktop devices
5. Quick deployment processes that reduce go-to-market timeframes

Connecting AI App Generators with AI Assistance

The intersection of AI App Generators and AI Assistants creates powerful opportunities for organizations to rapidly develop and deploy custom AI solutions tailored to their specific needs. For instance, businesses can use these tools to create specialized customer service applications powered by AI Assistants that understand their unique products, services, and customer base.

Moreover, when combined with Sovereign LLMs and HITL approaches, these applications can maintain high levels of data sovereignty while delivering effective AI assistance that respects local regulations and cultural contexts.

Data Privacy and Sovereignty Challenges in AI Assistant Deployment

As enterprises increasingly integrate AI Assistants and LLMs into their operations, they face significant challenges related to data privacy and sovereignty. These challenges are particularly acute when organizations rely on popular solutions like OpenAI’s ChatGPT or Hugging Face models, which may process data according to the regulations of their home countries rather than those of the user’s jurisdiction.

Regulatory Frameworks and Compliance Requirements

Governments worldwide are rapidly developing legislative and compliance frameworks specifically addressing data privacy, ownership, and usage in the context of AI systems. These regulations often impose strict requirements on how personal and corporate data can be collected, processed, stored, and transferred, creating a complex landscape for organizations deploying AI Assistants across different regions.

The fundamental challenge for enterprises becomes: “How to harness the power of AI, LLMs, and Machine Learning while maintaining stringent data sovereignty and data controls”. This challenge is particularly significant for organizations in regulated industries or those handling sensitive information.

Sovereign Solutions for AI Assistance

Developing AI Assistants based on Sovereign LLMs represents a promising approach to addressing these challenges. By training models on local data and operating them within specific jurisdictional boundaries, organizations can ensure compliance with regional regulations while still benefiting from advanced AI capabilities.

This approach requires careful consideration of the entire AI value chain, from data collection and model training to deployment and monitoring. Organizations must evaluate where their data is processed, who has access to it, and how the AI Assistant’s outputs align with local laws and ethical standards.

Future Directions: Integrating Sovereignty, HITL, and AI Application Development

The future of AI Assistants likely lies at the intersection of Sovereign LLMs, Human-in-the-loop methodologies, and accessible development platforms. This integration presents several promising directions for advancement.

Localized AI Ecosystems

As Sovereign LLMs continue to develop, we may see the emergence of complete AI ecosystems tailored to specific regions or industries. These ecosystems would include not only the foundational Large Language Models but also specialized AI Assistants, development tools, and data governance frameworks aligned with local requirements.

Enhanced HITL Systems with Specialized Expertise

Future HITL systems for AI Assistants may incorporate more sophisticated forms of human oversight, drawing on specialized expertise for different domains. For example, legal experts might review AI Assistant responses related to regulatory compliance, while cultural consultants could evaluate outputs for cultural appropriateness and sensitivity.

Seamless Integration of Development and Deployment

The continued evolution of AI App Generators will likely lead to more seamless integration between development and deployment processes. Organizations may be able to create, test, and refine AI Assistants through intuitive interfaces, with built-in safeguards to ensure data sovereignty and regulatory compliance.

Conclusion

The intersection of AI Assistants, Large Language Models, and sovereignty concerns represents a critical frontier in artificial intelligence development. By leveraging Sovereign LLMs, implementing robust Human-in-the-loop processes, and utilizing accessible AI Application Generators, organizations can develop AI Assistants that deliver value while respecting data privacy, regulatory requirements, and cultural contexts.

As these technologies continue to evolve, maintaining the balance between innovation and sovereignty will remain essential. The most successful implementations will likely be those that thoughtfully integrate advanced AI capabilities with appropriate human oversight and localized adaptation, ensuring that AI Assistants truly serve the needs of the communities and organizations they are designed to assist.

References:

[1] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-nvidia-revealing-the-path-forward-with-sovereign-llms.pdf
[2] https://www.ebsco.com/research-starters/computer-science/human-loop-hitl
[3] https://www.holisticai.com/blog/human-in-the-loop-ai
[4] https://www.jotform.com/ai/app-generator/
[5] https://www.apsy.io
[6] https://aws.amazon.com/what-is/large-language-model/
[7] https://www.amazee.io/blog/post/ai-llm-data-privacy-protection/
[8] https://ebi.ai/human-in-the-loop/
[9] https://www.telusdigital.com/glossary/human-in-the-loop
[10] https://codeplatform.com/ai
[11] https://swiftspeed.app
[12] https://www.cloudflare.com/learning/ai/what-is-large-language-model/
[13] https://techcrunch.com/2025/02/16/open-source-llms-hit-europes-digital-sovereignty-roadmap/
[14] https://help.crewai.com/how-to-use-hitl
[15] https://levity.ai/blog/human-in-the-loop
[16] https://www.appypie.com/ai-app-generator
[17] https://aireapps.com
[18] https://en.wikipedia.org/wiki/Large_language_model
[19] https://illuminem.com/illuminemvoices/personal-llms-a-doubleedged-sword-for-data-sovereignty-sustainability-and-society-iii
[20] https://hasura.io/blog/build-safer-ai-assistants-with-promptql-human-in-the-loop-guardrails

Interoperable Applications with Aire AI Assistant for Corteza

April 10, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

The integration of AI-driven development tools with low-code platforms has revolutionized enterprise application development. Aire AI Assistant for Corteza represents a cutting-edge approach that combines the power of artificial intelligence with the flexibility of open-source low-code development. This report explores how organizations can build fully interoperable applications using this innovative technology stack while leveraging human-in-the-loop methodologies to optimize results.

The Corteza Low-Code Ecosystem

Open-Source Foundation for Enterprise Applications

Corteza stands as a premier open-source low-code platform, positioning itself as “the Open Source Salesforce Alternative” with a robust architecture designed for enterprise-grade applications. Developed in 2019, Corteza provides organizations with a self-hosted solution that eliminates vendor lock-in while delivering enterprise-level functionality. The platform is built on a modern technology stack, with its backend developed in Golang – the multi-threaded computing language created by Google—and its frontend implemented in Vue.js.

Architectural Advantages for Interoperability

Corteza’s commitment to interoperability is evidenced by its adherence to W3C standards and formats, ensuring compatibility across diverse systems. All Corteza components are accessible via RestAPI, facilitating seamless integration with third-party systems and services. This cloud-native platform deploys via Docker containers, offering flexibility in deployment options while maintaining robust integration capabilities.

Aire AI Application Generator: Transforming App Development

Revolutionary AI-Powered Development

The Aire AI App Builder represents a paradigm shift in application development, leveraging advanced Large Language Models to transform natural language prompts into functional enterprise applications. This AI Application Generator empowers users to create production-ready apps in minutes—without requiring coding experience or technical expertise. The system generates complete data models, user interfaces, relationships, and even charts based on simple text descriptions.

Key Features of the AI App Generator

Aire’s AI Assistant capabilities extend beyond basic code generation to provide comprehensive application infrastructure:

1. AI-Powered Simplicity: Users can initiate development with a single text prompt, from which Aire generates data models, charts, pages, and relationships instantly.

2. Smart Prompt Builder: The system guides users through creating detailed, accurate prompts to optimize the AI’s understanding of requirements.

3. Customizable Scope: Developers can specify the size and complexity of the desired application (small, medium, or large) to match specific business needs.

4. Intelligent Field Configuration: The AI Assistant automatically assigns appropriate field types (text, numbers, dropdowns) and even prepopulates options based on the application context.

5. Relationship Mapping: Complex data relationships are intelligently constructed with clear explanations of the underlying logic.

Human-in-the-Loop (HiTL) Development Methodology

Balancing Automation with Human Expertise

While Aire leverages powerful AI capabilities, it embraces a Human-in-the-Loop (HiTL) methodology that combines automated generation with human oversight and refinement. This approach recognizes that while Large Language Models excel at generating initial structures, human expertise remains essential for customization and domain-specific optimizations.

Implementing Human in the Loop Processes

The HiTL implementation in Aire follows a structured approach:

1. Step-by-Step Validation: Aire divides app-building into discrete components (modules, fields, relationships), delivering each separately for human review before proceeding.

2. Manual Adjustment Capabilities: At each development stage, users can manually customize elements—adding, deleting, or modifying modules, fields, and relationships.

3. Iterative Refinement: The AI Assistant seamlessly integrates human changes into subsequent development steps, maintaining consistency throughout the application.

4. Visual Customization: Developers can fine-tune charts, dashboards, and user interfaces through intuitive editing tools after AI generation.

Building Interoperable Applications with Aire for Corteza

Development Workflow for Maximum Interoperability

Creating interoperable applications with Aire AI Assistant follows a streamlined process that balances automation with thoughtful design:

1. Initial Prompt Creation: Developers begin by describing their application requirements using natural language, which the AI Application Generator interprets.

2. Data Model Generation: Aire analyzes the prompt and constructs a comprehensive data model with appropriate entities and relationships.

3. Human Validation and Refinement: Following the HiTL approach, developers review and refine the generated models before proceeding.

4. Interface and Automation Design: The system generates user interfaces, dashboards, charts, and workflow automation based on the validated data model.

5. Deployment to Corteza: The completed application deploys to the Corteza Low-Code platform, where it benefits from the platform’s interoperability features.

6. API Integration: Developers can leverage Corteza’s RestAPI capabilities to connect with external systems and services.

Customization and Scalability Features

The platform offers extensive customization options that enhance interoperability while maintaining enterprise-grade functionality:

1. No-Code Tools: Intuitive interfaces allow for fine-tuning apps, adding workflows, roles, permissions, and advanced reporting without coding.

2. Open-Source Flexibility: Applications can be deployed on self-hosted Corteza instances or exported as source code for complete control and customization.

3. Enterprise-Grade Integration: The system seamlessly integrates with third-party systems via REST APIs, supporting comprehensive digital ecosystems.

4. Branding and Configuration: Every aspect of the application can be customized, from visual branding to complex configurations.

Applications and Use Cases

Versatile Enterprise Solutions

The AI Assistance provided by Aire for Corteza enables the rapid development of diverse enterprise applications:

1. Customer Relationship Management (CRM): Organizations can build customized CRM solutions tailored to specific industry requirements and workflows.

2. Enterprise Resource Planning (ERP): Comprehensive business management applications that integrate across departments and functions.

3. Compliance Management Systems: Specialized applications for tracking and managing regulatory compliance requirements.

4. Electronic Health Records (EHR): Healthcare-specific solutions that maintain patient data while integrating with existing medical systems.

5. Custom Business Process Applications: Tailored solutions for unique business workflows and data management needs.

Conclusion

Aire AI Assistant for Corteza Low-Code represents a significant advancement in enterprise application development, combining the power of Large Language Models with the flexibility of open-source low-code platforms. By implementing a Human-in-the-Loop methodology, the system balances AI-powered automation with human expertise, resulting in applications that are both rapidly developed and carefully refined.

The platform’s commitment to interoperability—through open standards, comprehensive APIs, and flexible deployment options—ensures that organizations can build applications that seamlessly integrate with their existing digital ecosystems while maintaining full control over their data and infrastructure.

For businesses seeking to accelerate digital transformation initiatives without sacrificing quality or customization, the combination of AI Application Generation and human-guided refinement offers an optimal approach to modern enterprise software development.

References:

[1] https://ie.linkedin.com/company/cortezaproject
[2] https://www.linkedin.com/company/aireapps
[3] https://www.toolify.ai/tool/aire-ai-app-builder
[4] https://cortezaproject.org
[5] https://www.youtube.com/watch?v=rSqCN4e30ZY
[6] https://theresanaiforthat.com/s/aire/
[7] https://www.planetcrust.com/the-low-code-enterprise-system
[8] https://aireapps.com
[9] https://www.cio.com/article/3616160/los-copilotos-de-ia-generativa-revolucionan-el-low-code-para-acelerar-el-time-to-market-y-minimizar-costes-del-desarrollo-de-software.html
[10] https://help.aireapps.com

AI Assistance and the Emerging Threat to History

April 9, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

The Emerging Threat to Historical Integrity: AI Content Writing and the Devaluation of Human Narratives

The rapid advancement of artificial intelligence has ushered in an era where AI-powered content creation tools can generate vast amounts of written material at unprecedented speeds. This technological revolution presents a growing threat to the integrity and authenticity of historical scholarship, as AI content writing technology increasingly floods digital spaces with machine-generated historical narratives. This article explores the multifaceted challenges posed by this phenomenon and examines potential solutions to preserve the value of human historical expertise in an AI-dominated landscape.

The Scale Problem: When Quantity Overwhelms Quality

The sheer volume of content that Large Language Machines (LLMs) can produce creates an unprecedented imbalance in information ecosystems. While human historians might spend months or years crafting meticulously researched articles or books, AI Application Generators can churn out thousands of historical narratives in minutes. This massive disparity in production capacity threatens to drown authentic human voices beneath a deluge of AI-generated content.

“AI-generated historical content frequently lacks the rigorous verification processes that human historians employ. While artificial intelligence can compile vast amounts of historical data rapidly, it often fails to validate the credibility of its sources, leading to distortions and inaccuracies,” notes a recent analysis on the risk of distorted history. The absence of critical human oversight means that AI-generated history can reinforce errors rather than correct them, posing a significant risk to historical scholarship.

When search results prioritize content based on volume and recency rather than accuracy or depth, AI-generated historical content may dominate search results, creating an impression of consensus or depth where none truly exists. This digital saturation threatens to marginalize human-written scholarship that often contains the nuanced contextual understanding essential for genuine historical knowledge.

Historical Accuracy Under Siege

AI systems rely on training data that often contains inherent biases, inaccuracies, or gaps. Without critical evaluation, these flaws propagate through AI-generated historical narratives. AI tends to oversimplify complex historical events, reducing multifaceted debates into generalized summaries that fail to capture the depth and nuance of historical developments.

Moreover, AI writing tools struggle with the interpretative aspects of historical scholarship. They cannot truly understand the sociopolitical contexts, emotional resonance, or ethical dimensions of historical events – they can only mimic patterns from their training data. This fundamental limitation leads to historical content that may appear legitimate on the surface but lacks the critical analytical depth that defines quality historical scholarship.

The potential for AI to distort historical narratives extends beyond simple inaccuracies. As noted in recent research on adversarial misuse of generative AI, threat actors have begun experimenting with generative AI tools to create and localize content. While current observations suggest these activities are still limited in sophistication, the trajectory of improvement suggests that the deliberate manipulation of historical narratives through AI could become increasingly prevalent and difficult to detect.

Human-in-the-Loop: A Critical Safeguard

The concept of Human-in-the-Loop (HITL or HiTL) offers a promising approach to mitigate the risks of AI-generated historical content. This methodology integrates human expertise and judgment into automated AI processes, ensuring that technology benefits from human intuition and expertise rather than replacing it entirely.

“Human-in-the-loop (HITL) is a model of AI and automation where human intervention is integrated into the system’s decision-making process,” explains a recent analysis. “Rather than allowing AI to operate entirely autonomously, HITL ensures that humans remain involved in critical points, either as a final decision-maker or as a participant in continuous learning loops. This approach mitigates risks associated with AI, such as errors, bias, and ethical concerns, by combining the strengths of AI with human judgment and expertise”.

In the context of historical content, HITL approaches could involve historians reviewing, correcting, and enhancing AI-generated drafts before publication. This collaborative model leverages the efficiency of AI while preserving the critical thinking, contextual understanding, and ethical judgment that human historians bring to their work. The continuous feedback loop between human experts and AI systems could also gradually improve the quality of AI-generated historical content over time.

The Threat to Historical Profession and Education

Beyond concerns about content accuracy, the proliferation of AI writing tools poses existential questions for the historical profession itself. As journalist Alison Hill reflects on the impact of AI on journalism (which shares many concerns with historical writing): “The greatest threat AI poses in my opinion is that it will take over the creative process”.

This concern extends to history education, where students might increasingly rely on AI App Builders to generate essays and research papers. This trend could undermine the development of critical thinking skills, research methodologies, and the ability to evaluate historical sources – all fundamental competencies for understanding history.

“Right now, I’m working on a history PhD and the question about how to teach students about AI looms large in this discipline,” notes one academic on LinkedIn. “There is a lot of nervousness about students using AI to write essays, but here is the thing: students frequently suck at writing the type of prompts that will elicit a comprehensive report from AI… If they do manage to use AI to generate something good, that means they understand their topic and the demands of the assignment. It also means they have remained in control of the outcome, not AI”.

This observation highlights a crucial point: meaningful engagement with historical content requires understanding the underlying historical concepts, contexts, and methodologies – skills that AI cannot replace.

Polymorphic Content and the Challenge of Detection

A particularly concerning development is the emergence of AI tools capable of generating “polymorphic” content—material that can dynamically change its form to evade detection. While research in this area has focused primarily on malware generation, the concept applies equally to content creation.

AI App Generators could potentially create historical content that appears unique across multiple generations, making it increasingly difficult to identify AI-authored material. This capability would compound the challenge of distinguishing between human and machine-authored historical narratives, further blurring the lines between authentic and synthetic historical scholarship.

As AI writing technology evolves, we may face a future where distinguishing between human-authored and AI-generated historical content becomes virtually impossible without specialized detection tools – tools that themselves may struggle to keep pace with advancing AI capabilities.

Balancing AI Assistance with Human Expertise

Despite these challenges, AI writing technology need not be viewed as entirely antagonistic to historical scholarship. When properly implemented as an AI Assistant rather than a replacement, these tools can enhance historical research and writing.

AI Assistance can help historians process vast archives of historical documents, identify patterns across large datasets, translate historical texts, and generate preliminary drafts that human historians can refine. This collaborative approach recognizes the complementary strengths of both humans and machines: AI excels at processing large volumes of data and identifying patterns, while humans excel at critical thinking, contextual understanding, and ethical judgment.

“The key benefits of a HITL approach include: AI systems operate within regulatory and ethical boundaries. Sensitive data and data integrity are protected. AI is transparent and explainable to build trust with stakeholders”. By maintaining humans as the ultimate arbiters of historical content, we can harness the efficiency of AI while preserving the integrity of historical scholarship.

Strategies for Preserving Historical Integrity

Several approaches can help mitigate the threat posed by AI content writing to historical scholarship:

1. Implement robust Human-in-the-Loop frameworks: Ensure that all AI-generated historical content undergoes human expert review before publication, particularly for educational and scholarly materials.

2. Develop authentication standards: Create verifiable credentials for human-authored historical content, similar to the “Created by Humans” licensing platform mentioned in discussions about copyright and AI.

3. Enhance AI literacy: Educate students, researchers, and the public about the limitations of AI-generated historical content and the importance of critical evaluation.

4. Establish ethical guidelines: Develop professional standards for the appropriate use of AI tools in historical research and writing, including transparency requirements about AI involvement.

5. Support human scholarship: Ensure continued funding and institutional support for human-led historical research to prevent the marginalization of authentic historical scholarship.

Conclusion: Preserving Human Agency in Historical Narratives

The threat posed to history by AI content writing technology producing more content than human writers is substantial but not insurmountable. By implementing thoughtful Human-in-the-Loop approaches and viewing Large Language Machines as tools for assistance rather than replacement, we can navigate this technological transition while preserving the integrity of historical scholarship.

As one researcher notes: “We believe in the human spirit and our inherent love of storytelling. This alone could save the industry by ‘keeping it real'”. This sentiment applies equally to historical writing – the human connection to our shared past and the unique insights that human historians bring to its interpretation remain irreplaceable aspects of historical scholarship.

The future of historical understanding in the age of AI will depend on our ability to harness the benefits of AI Application Generators and AI App Builders while maintaining human agency in the creation and interpretation of historical narratives. By establishing appropriate boundaries, ethical frameworks, and collaborative models between humans and AI, we can ensure that historical scholarship remains authentic, nuanced, and trustworthy even as AI content writing technology continues to evolve.

References:

[1] https://timesofindia.indiatimes.com/blogs/blackslate-corner/artificial-intelligence-and-the-risk-of-distorted-history-balancing-innovation-with-accuracy/
[2] https://www.virtual-operations.com/insight/the-critical-role-of-human-in-the-loop-in-intelligent-automation-and-ai
[3] https://cloud.google.com/blog/topics/threat-intelligence/adversarial-misuse-generative-ai
[4] https://www.nccgroup.com/us/research-blog/analyzing-ai-application-threat-models/
[5] https://www.writersdigest.com/write-better-nonfiction/is-journalism-under-threat-from-ai
[6] https://openethics.ai/balancing-act-navigating-safety-and-efficiency-in-human-in-the-loop-ai/
[7] https://www.tomsguide.com/computing/malware-adware/this-fake-ai-image-generator-is-pushing-info-stealing-malware-onto-macs-and-pcs
[8] https://www.linkedin.com/pulse/thoughts-ai-writing-studying-history-mary-elizabeth-baxter-lrzvc
[9] https://keylabs.ai/blog/human-in-the-loop-balancing-automation-and-expert-labelers/
[10] https://infosecwriteups.com/exploiting-generative-ai-apps-with-prompt-injection-33b0ff1aa07a
[11] https://rosalienebacchus.blog/2025/02/23/the-writers-life-the-growing-threat-of-ai/
[12] https://www.aiguardianapp.com/post/what-is-human-in-the-loop-ai
[13] https://www.hyas.com/blog/blackmamba-using-ai-to-generate-polymorphic-malware
[14] https://tripleareview.com/ai-writing-history/
[15] https://cx-journey.com/2023/10/human-in-the-loop-hitl-what-cx-leaders-should-know.html
[16] https://techxplore.com/news/2025-04-ai-threats-software-revealed.html
[17] https://www.ptara.com/2022/12/15/artificial-stupidity-a-threat-to-history/
[18] https://lightit.io/blog/understanding-human-in-the-loop-where-humans-meet-machines/
[19] https://www.reddit.com/r/copywriting/comments/1dey5m6/threat_of_ai_realistically/
[20] https://www.sciencedirect.com/science/article/abs/pii/S0952197623005602
[21] https://www.rfi.fr/en/science-and-technology/20230407-data-regulators-scramble-to-stop-chatgpt-rewriting-history
[22] http://botpress.com/docs/hitl-1
[23] https://www.deepscribe.ai/resources/optimizing-human-ai-collaboration-a-guide-to-hitl-hotl-and-hic-systems
[24] https://clickup.com/features/ai/threat-model-generator
[25] https://digital.ai/catalyst-blog/monitor-threats-to-your-apps-with-digital-ai-app-aware/
[26] https://akademie.dw.com/en/generative-ai-is-the-ultimate-disinformation-amplifier/a-68593890
[27] https://www.iriusrisk.com/ai-threat-modeling
[28] https://www.ibm.com/think/insights/10-ai-dangers-and-risks-and-how-to-manage-them
[29] https://www.quokka.io/blog/security-risks-of-ai-in-app-development
[30] https://visualstudiomagazine.com/Articles/2025/03/27/Low-Code-Report-Says-AI-Will-Enhance-Not-Replace-DIY-Tools.aspx
[31] https://aireapps.com/ai/10-web-development-trends-2025-rise-of-the-ai-app-builder/
[32] https://dobetter.esade.edu/en/artificial-intelligence-technological-revolution-existential-threat-AI
[33] https://www.comidor.com/blog/artificial-intelligence/ai-powered-fraud-detection/
[34] https://www.aiixx.ai/blog/replit-agent-a-comprehensive-review-of-the-ai-app-builder
[35] https://www.darkreading.com/application-security/ai-in-software-development-the-good-the-bad-and-the-dangerous

Aire Supply Chain Tariff Impact Calculator POC

April 8, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

We asked the Aire AI Assistant for Corteza to build a POC of a Supply Chain Tariff Impact Calculator. The suggested blueprint it came up with can be found in the Aire Public Library by signing in here. Needs some TLC, but it’s not too shabby and effort 🙂

This analysis explores how the Supply Chain Tariff Impact Calculator proof-of-concept application would function in practice for manufacturing businesses. The Corteza-based solution offers a robust framework for managing tariffs, analyzing supply chain impacts, and optimizing international trade operations.

Core Application Architecture and Purpose

The Supply Chain Tariff Impact Calculator is designed as an end-to-end solution that allows manufacturing businesses to model, track, and optimize their global trade operations with specific focus on tariff impacts. The application integrates data across the entire supply chain, providing a comprehensive view of how tariffs affect product costs, sourcing decisions, and regulatory compliance.

The application’s architecture is built around interconnected modules that capture every aspect of the international trade process. At its core, the system allows users to:

1. Track applicable tariffs for specific products and materials
2. Calculate the financial impact of tariffs across the supply chain
3. Identify opportunities for tariff reductions through exemptions or trade agreements
4. Ensure regulatory compliance across multiple jurisdictions
5. Optimize shipping routes and logistics to minimize tariff costs

Data Model and Key Modules

The application is structured around several key interconnected modules that form a comprehensive data model for tariff impact analysis:

Tariff Rate Management

The foundation of the application is the Tariff Rate module, which maintains a database of tariff codes, rates, and associated regulations. This module stores critical information including:

– Harmonized System (HS) codes for product classification
– Ad valorem rates (percentage-based tariffs)
– Specific duties (fixed amount tariffs)
– Quota limitations and reduced rates
– Effective and expiration dates for tariff provisions
– Country of origin specifications

Users can search and filter tariff rates based on multiple criteria, helping them quickly identify applicable tariffs for specific products and trade lanes. The system also tracks duty exemptions, allowing users to identify potential cost-saving opportunities through categories like diplomatic goods, humanitarian aid, educational supplies, or environmental protection initiatives.

Supply Chain Partner Integration

The Supply Chain Partner module maintains comprehensive data about all entities involved in the manufacturing and distribution process. Each partner record includes:

– Company information and contact details
– Supply chain role (manufacturer, supplier, distributor, etc.)
– Country and regional information
– Trade compliance ratings
– Risk assessments
– Associated tariff codes and trade agreements

This module enables organizations to map their entire supply network and associate relevant tariff and compliance information with each partner. The system’s dashboard presents metrics on supply chain partners, allowing managers to quickly assess the distribution of suppliers by region, role, or compliance status.

Practical Application Workflows

In practice, the application would support several key business workflows:

Tariff Impact Assessment and Product Costing

A primary function of the application is to help manufacturers calculate the true landed cost of products by factoring in all applicable tariffs and duties. Here’s how this process would work in practice:

1. Product managers enter or import commodity codes for their products and materials
2. The system automatically associates the appropriate tariff rates based on HS codes and countries of origin
3. Users can run impact assessments to see how tariffs affect product cost structures
4. Finance teams can incorporate these calculations into pricing models

The application’s dashboard shows metrics for tariff rates, with visualizations displaying rates by duty exemption status and quota requirements. This gives management a quick overview of the tariff landscape affecting their products.

Trade Agreement Optimization

The International Trade Agreement module allows companies to leverage preferential trade terms:

1. Trade compliance teams catalog all applicable agreements between trading countries
2. The system associates relevant exemptions or reduced rates with specific commodity codes
3. Supply chain managers can run comparisons to identify the most cost-effective sourcing options
4. The application flags upcoming expiration dates for agreements, allowing proactive planning

The module captures comprehensive agreement details including tariff percentages, volume limits, covered goods, and required documentation. This enables businesses to ensure they’re properly documenting shipments to qualify for preferential treatment.

Manufacturing Process and Tariff Classification

The Manufacturing Process module helps companies manage tariff implications throughout production:

1. Manufacturing engineers document production steps, including required materials and processes
2. The system links each step to relevant tariff classifications
3. Compliance teams can verify that proper tariff codes are applied to finished goods
4. The application tracks regulatory compliance requirements associated with each step

This module helps ensure accurate product classification, which is critical for proper tariff determination. It also tracks process costs, enabling analysis of how tariffs impact overall manufacturing expenses.

Shipping Route Optimization

The Shipping Route module enables logistics teams to plan cost-effective transportation:

1. Logistics managers enter route information including origin/destination ports
2. The system calculates associated tariffs, duties, and fees
3. Alternative routes can be compared to identify potential savings
4. Environmental impact metrics help balance cost concerns with sustainability goals

The application provides detailed tracking of all logistics costs including fuel surcharges, handling fees, and insurance costs. This comprehensive view allows companies to make informed decisions about shipping methods and routes.

Exemption Management and Compliance

A particularly valuable feature is the Tax Exemption module, which helps companies identify and manage potential duty savings:

1. Compliance specialists catalog applicable exemptions by jurisdiction and industry
2. The system matches products against potential exemptions
3. Documentation requirements are clearly outlined to ensure proper exemption qualification
4. Validity periods are tracked to prevent reliance on expired exemptions

The application dashboard provides metrics on exemption statuses (exempt, partially exempt, pending approval, etc.), giving management visibility into potential duty reduction opportunities[1].

Regulatory Compliance Management

The Regulatory Compliance module ensures adherence to complex trade regulations:

1. Compliance teams document applicable regulations by country and product category
2. The system associates compliance requirements with specific tariff codes
3. Risk levels are assigned to highlight areas requiring special attention
4. Audit frequencies and remediation plans are tracked to maintain compliance

This module helps prevent costly penalties and shipment delays by ensuring all regulatory requirements are identified and addressed proactively.

Integration and Data Flow

The application’s strength lies in its integrated approach, connecting data across all international trade functions:

1. Commodity codes link to tariff rates and regulatory requirements
2. Supply chain partners connect to shipping routes and trade agreements
3. Manufacturing processes tie to commodity codes and material costs
4. Currency exchange rates integrate with pricing models for accurate financial calculations

This integrated data flow ensures that changes in one area (like a new tariff rate) could automatically update calculations throughout the system, providing real-time visibility into tariff impacts.

Conclusion

The Supply Chain Tariff Impact Calculator represents a sophisticated approach to managing the complex challenges of international trade for manufacturing businesses. By integrating tariff data with supply chain, manufacturing, and logistics information, the application provides a comprehensive platform for optimizing global operations.

In practice, this system would help companies reduce tariff-related costs, ensure regulatory compliance, and make more informed sourcing and routing decisions. The modular structure allows for flexible implementation, with the ability to focus on specific areas of concern or implement the full suite for end-to-end tariff management.

This proof-of-concept demonstrates how a modern data-driven approach can transform what has traditionally been a complex, manual process into a streamlined, analytically powerful business function that directly impacts bottom-line performance.

How Do We Make LLM Technology Safer?

April 8, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

As Large Language Models (LLMs) continue to revolutionize how we interact with technology, ensuring their safe and responsible deployment has become increasingly crucial. This report explores comprehensive strategies and best practices for enhancing the safety of LLM technology, with a particular focus on human oversight mechanisms and secure application development.

Understanding LLM Safety and Its Importance

LLM Safety, a specialized area within AI Safety, focuses on safeguarding Large Language Models to ensure they function responsibly and securely. This includes addressing vulnerabilities like data protection, content moderation, and reducing harmful or biased outputs in real-world applications. As these models gain more autonomy and access to personal data while handling increasingly complex tasks, the importance of robust safety measures cannot be overstated.

The rapid advancement of LLM technology has raised significant cybersecurity concerns. According to McKinsey research, 51% of organizations view cybersecurity as a major AI-related concern. These concerns are well-founded, as unsecured LLMs can lead to data breaches, privacy violations, and the production of harmful content.

Major Risks Associated with LLM Technology

Data Privacy and Security Risks

LLMs trained on vast datasets may inadvertently memorize and reproduce sensitive information. This creates significant privacy risks, particularly when these models are integrated into AI Assistants that handle personal data. The risk of sensitive data exposure has been identified by OWASP as one of the most prominent risks for AI applications.

Adversarial Attacks and Prompt Manipulation

Malicious actors can insert harmful content into LLM prompts to manipulate model behavior or extract sensitive information. These prompt injection attacks represent a significant vulnerability, especially in AI Assistance systems where users directly interact with the model.

Unvalidated Outputs and Model Vulnerabilities

Unvalidated outputs from LLMs can create vulnerabilities in downstream systems, potentially giving end-users unauthorized access to backend systems. Additionally, models may contain third-party components with inherent vulnerabilities that can be exploited.

Human-in-the-Loop: A Critical Safety Mechanism

The Importance of Human Oversight

Human-in-the-loop (HITL) machine learning is a collaborative approach that integrates human input and expertise into the lifecycle of machine learning and artificial intelligence systems. This approach is fundamental to LLM safety, as it provides crucial oversight and intervention capabilities.

While Large Language Machine systems possess remarkable capabilities, they benefit substantially from human expertise in areas requiring judgment, contextual understanding, and handling incomplete information. HITL bridges this gap by incorporating human input and feedback into the LLM pipeline.

Implementing HITL in LLM Systems

Human in the Loop processes can be implemented at various stages of LLM deployment:

1. Training and fine-tuning: Humans can provide feedback on model outputs to improve safety and reduce harmful content.

2. Output validation: Human reviewers can verify model outputs before they’re presented to end-users, particularly for high-stakes applications.

3. Continuous improvement: Ongoing human feedback helps identify and address emerging safety concerns.

Developing Safer AI Applications

Security by Design Principles

When developing applications powered by LLMs, security must not be an afterthought. Implementing “security by design” ensures potential vulnerabilities are addressed early by:

– Conducting threat modeling to identify and mitigate potential security risks
– Defining security requirements alongside functional requirements
– Ensuring secure coding practices are followed throughout development

Role of AI Application Generators and Development Tools

AI Application Generators and AI App Builders can streamline the development process while incorporating safety features. When selecting an AI App Generator, it’s crucial to choose tools that prioritize security and provide robust risk management capabilities.

However, it’s essential to choose these tools carefully. Not all AI applications are safe, and threat actors have created fake apps designed to trick users into downloading malware. Organizations should only use AI tools that have been properly vetted and approved.

Best Practices for LLM Safety

Data Security and Privacy Measures

To ensure LLM safety, organizations should:

1. Implement data minimization: Only collect data necessary for the AI application to function.
2. Use anonymization and pseudonymization: Protect personal data by making it harder to trace back to individuals.
3. Obtain user consent: Ensure users explicitly consent to having their data collected and processed.
4. Avoid inputting sensitive information: Never input Personal Identification Information (PII) into AI assistants.

Model Security and Monitoring

Protecting LLM models themselves involves:

1. Implementing robust access controls: Restrict access to models, ensuring only authorized personnel can interact with or modify them[9].
2. Continuous model monitoring: Monitor AI models for unusual activities or performance anomalies that might indicate an attack[9].
3. Regular updates: Keep models and underlying systems updated with security patches[9].

Testing and Validation Approaches

Comprehensive testing is essential for LLM safety:

1. Code reviews and audits: Regularly conduct security audits to identify and fix vulnerabilities.
2. Automated testing: Implement automated security testing tools to continuously check for security issues.
3. Include expected failure cases: Test functions with arguments that should cause them to fail, helping identify tampering.

Risk Mitigation Strategies

Comprehensive Risk Assessment

The first step in mitigating LLM risks is conducting a comprehensive assessment to understand potential threats and vulnerabilities. This includes identifying:

– Types of data the AI has access to
– How the AI makes decisions
– Potential impact of security breaches or system failures

Establishing Clear Guidelines and Policies

Organizations should create guidelines that outline how LLM technology should be used, including:

– Defining specific use cases
– Setting quality standards and testing procedures
– Implementing security measures and access controls

Continuous Monitoring and Incident Response

Even with best practices in place, security incidents can occur. Organizations should establish:

1. Real-time monitoring: Implement tools to detect and respond to security threats promptly.
2. Incident response plan: Develop and regularly update plans to ensure quick and effective action during security breaches.
3. Post-incident analysis: Conduct thorough reviews after incidents to improve security measures.

Future Directions in LLM Safety

As LLM technology continues to evolve, safety approaches must adapt accordingly. Emerging strategies include:

1. Non-deterministic behavior: Introducing randomness in guard triggers and outputs to make it harder for malicious actors to predict system behavior.
2. Transparency and explainability: Developing methods to make LLM decision-making processes more transparent and understandable.
3. Advanced threat modeling: Using AI-powered tools to identify potential vulnerabilities before they can be exploited.

Conclusion

Ensuring the safety of LLM technology requires a multi-faceted approach combining technical safeguards, human oversight, and robust governance frameworks. By implementing Human-in-the-Loop processes, adopting security-by-design principles, and following best practices for data protection and model security, organizations can harness the power of Large Language Models while minimizing associated risks.

As AI Assistants and AI Applications become increasingly integrated into our daily lives and business operations, the responsibility to deploy this technology safely falls on all stakeholders – from developers and organizations to regulators and end-users. By prioritizing safety from the outset and continuously adapting to emerging threats, we can ensure that LLM technology fulfills its promise as a beneficial and transformative force.

References:

[1] https://www.confident-ai.com/blog/the-comprehensive-llm-safety-guide-navigate-ai-regulations-and-best-practices-for-llm-safety
[2] https://cloud.google.com/discover/human-in-the-loop
[3] https://granica.ai/blog/llm-security-risks-grc
[4] https://www.trendmicro.com/vinfo/us/security/news/security-technology/ces-2025-a-comprehensive-look-at-ai-digital-assistants-and-their-security-risks
[5] https://qrs24.techconf.org/download/webpub/pdfs/QRS-C2024-43b2F0XafenffERHWle5q5/656500a074/656500a074.pdf
[6] https://www.nightfall.ai/blog/building-your-own-ai-app-here-are-3-risks-you-need-to-know-about–and-how-to-mitigate-them
[7] https://www.onlinegmptraining.com/risks-of-ai-apps-like-chatgpt-or-bard/
[8] https://travasecurity.com/learn-with-trava/blog/6-ways-to-be-safe-while-using-ai/
[9] https://calypsoai.com/news/best-practices-for-secure-ai-application-development/
[10] https://clickup.com/p/ai-agents/risk-mitigation-plan-generator
[11] https://www.adelaide.edu.au/technology/secure-it/generative-ai-it-security-guidelines
[12] https://digital.ai/application-security-best-practices/
[13] https://www.linkedin.com/pulse/risk-mitigation-strategies-generative-ai-code-chris-hudson-tznwe
[14] https://logicballs.com/tools/site-safety-protocol-generator
[15] https://www.miquido.com/blog/how-to-secure-generative-ai-applications/
[16] https://www.manageengine.com/appcreator/application-development-articles/low-code-powered-ai-risk-mitigation.html
[17] https://www.nec.com/en/global/techrep/journal/g23/n02/230214.html
[18] https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/
[19] https://www.appsecengineer.com/blog/top-5-reasons-of-llm-security-failure
[20] https://eonreality.com/eon-reality-introduces-groundbreaking-ai-safety-assistant/
[21] https://arxiv.org/abs/2411.02317
[22] https://digital.ai/products/application-security/
[23] https://www.mcafee.com/blogs/other-blogs/mcafee-labs/the-rise-and-risks-of-ai-art-apps/
[24] https://drapcode.com/ai-app-generator
[25] https://www.sprinklr.com/blog/evaluate-llm-for-safety/
[26] https://www.ninetwothree.co/blog/human-in-the-loop-for-llm-accuracy
[27] https://llmmodels.org/blog/llm-fine-tuning-guide-to-hitl-and-best-practices/
[28] https://www.elastic.co/es/blog/combating-llm-threat-techniques-with-elastic-ai-assistant
[29] https://www.adobe.com/legal/licenses-terms/adobe-gen-ai-user-guidelines.html
[30] https://www.builder.ai/blog/app-security-assessment
[31] https://techcommunity.microsoft.com/blog/educatordeveloperblog/embracing-responsible-ai-measure-and-mitigate-risks-for-a-generative-ai-app-in-a/4276931
[32] https://www.kuleuven.be/english/education/leuvenlearninglab/support/toolguide/guidelines-for-safe-use-of-genai-tools
[33] https://www.datastax.com/guides/ai-app-development-guide
[34] https://www.youtube.com/watch?v=WXMn7Vm6Im8
[35] https://www.hypotenuse.ai/blog/what-you-need-to-know-about-ai-safety-regulation
[36] https://aireapps.com/ai/secure-scalable-no-code-database-apps/
[37] https://riskacademy.blog/risk-management-ai/
[38] https://genai.calstate.edu/guidelines-safe-and-responsible-use-generative-ai-tools
[39] https://snyk.io/blog/10-best-practices-for-securely-developing-with-ai/
[40] https://www.taskade.com/generate/project-management/project-risk-mitigation-plan

Understanding Large Language Models: Your AI Friends Explained

April 7, 2025/0 Comments/in AI, App Development, Articles /by Niall McCarthy

Introduction

LLM’s explained for a 10 year old 🙂

Large Language Models (LLMs) are like super-smart digital brains that can understand and create text a bit like humans do. They power many of the cool technology tools you might already use or hear about. Let’s explore these amazing AI helpers and how they work!

What Are Large Language Models?

Imagine having a giant digital encyclopedia that doesn’t just store information – it can respond like a real person! That’s what an LLM is. These special computer programs can read and write text, answer questions, tell stories, and even help with your homework.

Think of an LLM like a friend who has read millions of books, watched tons of videos, and listened to countless conversations. After learning from all that information, this digital friend can have conversations with you about almost anything.

How Do LLMs Learn?

LLMs learn just like you do when learning a new language or skill:

– They “read” millions of books, articles, and conversations to understand how words work together
– They look for patterns in language, just like how you learn that “once upon a time” usually starts a fairy tale
– They practice by predicting what word should come next in a sentence
– They get better over time, just like how you improve at reading or math with practice

For example, if you type “I want to build a sandcastle at the…” an LLM might complete it with “beach” because it has learned that beaches and sandcastles often go together.

What Is an AI Assistant?

An AI Assistant is a helpful program powered by LLMs that can understand what you ask and respond in a helpful way[2]. Popular AI Assistants you might have heard of include:

– Siri on Apple devices
– Alexa in Amazon Echo speakers
– ChatGPT which can answer questions and help with homework

These AI Assistants use LLMs to understand your questions and give you answers that make sense. They provide AI Assistance by helping you find information, remember things, or just have fun conversations.

Human-in-the-Loop: People and AI Working Together

Even though LLMs are super smart, they still need humans to help them learn and improve. This is called “Human-in-the-Loop” or HITL.

Human in the Loop means that real people provide feedback to the AI, helping it understand when it makes mistakes or needs to improve. It’s like having a teacher check your homework and show you how to fix your errors.

Why Is HITL Important?

Imagine if you were learning to ride a bike with no one watching or helping. You might develop bad habits or not know when you’re making mistakes! The same is true for AI.

Human-in-the-loop is important because:

– People can check if the AI’s answers are correct
– Humans can teach the AI new things it hasn’t learned yet
– People can make sure the AI is being helpful and kind
– Humans can guide the AI to improve, just like how parents guide their children[3]

Building Your Own AI Applications

Did you know that kids like you can actually create your own AI programs? That’s right! Using special tools called AI App Generators or AI Application Generators, you can build cool projects without needing to be a computer expert.

An AI App Builder helps you create programs that can:

– Recognize pictures and tell you what they show
– Understand speech and respond to voice commands
– Play games that adapt to your skill level
– Tell stories based on your ideas

Making Your Own Little LLM

There are even programs that help kids build simplified versions of LLMs. Instead of just using AI, you can learn how to create your own!

For example, with a program called “Little Language Models,” kids can learn the basic ideas behind how LLMs work by building small versions themselves. This helps you understand that AI isn’t magic – it’s a technology that follows patterns and rules that you can learn about.

How AI Applications Help Us Every Day

AI is all around us, helping with many everyday tasks:

– Streaming services like Netflix use AI to suggest movies you might like
– Games use AI to create characters that respond to your actions
– Educational apps use AI to help you learn at your own pace
– Social media uses AI to show you posts from friends you interact with most[6]

These applications use LLMs and other AI technologies to make our lives easier and more fun.

Conclusion

Large Language Models are amazing AI technologies that learn from huge amounts of information to understand and generate text like humans do. They power AI Assistants that help us every day, and with AI App Generators, even kids can build their own AI applications.

Remember that the best AI systems use Human-in-the-Loop approaches, where people and machines work together. Humans help guide and improve the AI, just like teachers help students learn and grow.

Now that you understand LLMs better, maybe someday you’ll build your own AI Assistant or even help improve the technology as you grow up!

Citations:
[1] https://www.technologyreview.com/2024/10/25/1106168/kids-are-learning-how-to-make-their-own-little-language-models/
[2] https://www.jetlearn.com/blog/build-your-ai-assistant
[3] https://encord.com/blog/human-in-the-loop-ai/
[4] https://www.codingal.com/coding-for-kids/blog/how-kids-can-build-ai-powered-apps/
[5] https://www.codingal.com/coding-for-kids/blog/llm-for-kids-explained/
[6] https://www.softwareacademy.co.uk/ai-for-kids/
[7] https://www.youtube.com/watch?v=XTf0n3CVx4g
[8] https://skoolofcode.us/blog/what-kids-need-to-know-about-generative-ai/
[9] https://www.reddit.com/r/LocalLLaMA/comments/1dcy2ow/newbie_question_on_using_llm_at_home_to_help_kid/
[10] https://codeyoung.com/blog/what-is-artificial-intelligence-for-kids-a-beginners-guide-cm5aqb7il0002b0v1l3ze8dm6
[11] https://levity.ai/blog/human-in-the-loop
[12] https://kidgeni.com
[13] https://www.linkedin.com/pulse/deepseek-llm-ai-guide-curious-minds-how-explain-your-kids-mousa-8r5mc
[14] https://codakid.com/blog/introduce-ai-concepts-to-kids/
[15] https://erichudson.substack.com/p/how-to-be-a-human-in-the-loop
[16] https://storytimeaiapp.com
[17] https://www.youtube.com/watch?v=6gn2J3hCrN8
[18] https://www.codingal.com/coding-for-kids/blog/ai-for-kids/
[19] https://bwatwood.edublogs.org/2024/06/13/the-human-in-the-loop/
[20] https://apps.apple.com/us/app/ai-baby-generator-face-maker/id1607753158