Organisations are rushing to deploy AI systems. Generative AI assistants are being embedded into customer service platforms. Machine learning models are making credit decisions. Computer vision systems are screening job applications. The pace of AI adoption is extraordinary—but so is the compliance challenge it creates.

Every AI system introduces risks. Regulatory risks, where non-compliance with emerging AI regulations can lead to significant fines and operational restrictions. Ethical risks, where biased or harmful AI systems damage brand reputation and erode stakeholder trust. Operational risks, where poorly governed AI systems make decisions that harm customers or violate legal requirements.

Managing these risks requires organisations to maintain comprehensive inventories of their AI systems, understand which regulations apply to each system, and regularly assess whether systems meet compliance requirements. For large enterprises with dedicated compliance teams, this is challenging. For SMEs with limited resources, it's often impossible.

This is the problem we set out to solve with ComplyChain AI—an autonomous, multi-agent system designed to automate AI compliance management from end to end.

The AI Compliance Challenge

The project emerged from a simple observation: organisations are struggling to operationalise AI safely. They're deploying systems without clear inventories of what they've deployed. They're uncertain which regulatory frameworks apply to their use cases. And they lack reliable mechanisms to prove ongoing compliance when regulators or auditors come asking.

The challenge breaks down into three core problems.

First, visibility Most organisations don't have comprehensive inventories of their AI systems. Models are deployed by different teams, using different tools, in different environments. Some are built in-house. Others are third-party services accessed through APIs. Some are critical production systems. Others are experimental prototypes that never got decommissioned. Without visibility into what AI systems exist, where they're deployed, and who's responsible for them, compliance is impossible.

Second, regulatory intelligence. AI regulations are proliferating rapidly. The EU AI Act establishes a risk-based framework for regulating AI systems across the European Union. The NIST AI Risk Management Framework provides voluntary guidance for managing AI risks in the United States. ISO/IEC 42001 offers an international standard for AI management systems. Each framework has different requirements, different risk classifications, and different compliance obligations. Organisations need to understand which frameworks apply to their systems and what those frameworks require

Third, ongoing assessment Compliance isn't a one-time exercise. AI systems evolve. Models are retrained. Deployment contexts change. Regulations are updated. Organisations need mechanisms to continuously assess whether their AI systems remain compliant and to generate evidence of compliance when needed. Manual assessment processes don't scale. By the time a compliance team finishes reviewing one system, three more have been deployed.

The ComplyChain AI Solution

ComplyChain AI addresses these challenges through a phased, multi-agent architecture. Rather than attempting to solve everything at once, we designed the system in three iterations, each building on the previous one

Iteration 1: AI Asset Visibility Agent solves the inventory problem. The agent automatically discovers AI systems across an organisation's infrastructure, collecting metadata about each system—what it does, where it's deployed, who owns it, what data it processes. This information is stored in a central SQL database that serves as the single source of truth for the organisation's AI landscape

The workflow is fully automated. A scheduler triggers the agent daily at 09:00. The agent scans designated repositories, cloud environments, and API endpoints, identifying AI models and systems. For each asset discovered, the agent extracts metadata and validates data quality using a LangChain-powered validator. The validated data is stored in the central database, providing the foundation for compliance management

The performance metrics are compelling. The agent achieves over 90% asset discovery rates and maintains over 85% data quality. It automates 40-60% of the work that would otherwise require manual effort, at a monthly cost of $400-850. Most importantly, it provides the visibility that organisations need before they can manage AI compliance effectively.

Iteration 2: AI RM Intelligence Agent solves the regulatory intelligence problem. This agent functions as a chatbot-style interface that answers questions about AI regulations and compliance requirements. Behind the scenes, it uses a Retrieval-Augmented Generation (RAG) architecture with a vector database storing the full text of regulatory frameworks—NIST AI Risk Management Framework, EU AI Act, and ISO/IEC 42001.

When a user asks a question—"What are the requirements for high-risk AI systems under the EU AI Act?" or "How should we document our AI model training process?"—the agent retrieves relevant regulatory content from the vector database and generates a contextual answer using an LLM. The system implements intelligence caching to reduce costs and human review tags to ensure accuracy. Verification mechanisms check that generated content accurately reflects regulatory requirements.

The performance is impressive. The agent achieves over 90% query accuracy with response times under 10 seconds. It automates 60-75% of regulatory research tasks that would otherwise require compliance specialists to manually review hundreds of pages of regulatory text. The monthly cost ranges from $675-1,400, a fraction of what organisations would spend on manual compliance research.

Iteration 3: AI RM Assessment Agent solves the ongoing assessment problem. This agent evaluates AI systems against regulatory requirements, generating compliance reports that categorise findings as "Fully Met" or "Partially Met" along with recommended remediation actions.

The assessment workflow integrates data from the previous two iterations. The agent retrieves AI asset information from the central database and regulatory requirements from the vector database. It then uses a RAG pipeline to compare asset descriptions against requirements, identifying compliance gaps. The agent supports both generic assessments (quick, high-level checks) and strict assessments (comprehensive evaluations with evidence validation).

The output is a detailed compliance report that provides executives with summary findings and compliance teams with specific remediation guidance. The reports are generated automatically, with human-in-the-loop validation for critical findings. The agent achieves over 95% assessment accuracy with response times under 5 seconds and autonomous completion rates over 85%. Monthly costs range from $1,250-2,500, depending on the number of systems assessed.

The Technology Stack

ComplyChain AI leverages modern AI and cloud technologies to deliver autonomous compliance management.

The AI components include LangChain for orchestration, OpenAI APIs for large language model capabilities, and Pinecone or Weaviate for vector database storage. We use sentence transformers for generating embeddings and LlamaIndex for indexing regulatory content. The RAG pipeline retrieves relevant regulatory text and generates contextual responses that are grounded in actual regulatory requirements rather than hallucinated content.

The backend infrastructure uses Python and FastAPI for API services, Docker for containerisation, and Kubernetes for orchestration. The system is designed to run on AWS or Azure, taking advantage of managed services for databases, storage, and compute. This cloud-native architecture ensures scalability—the system can handle organisations with dozens of AI systems or thousands.

Security is embedded throughout the stack. We use Lakera Guard and Lakera Red to protect against OWASP Top 10 vulnerabilities for LLM applications. Intelligence caching reduces costs whilst maintaining security. Human review tags ensure that AI-generated compliance assessments are validated before being used for regulatory reporting. And comprehensive logging provides audit trails that demonstrate how compliance decisions were made.

The Business Impact

The business case for ComplyChain AI is straightforward. Manual AI compliance management is expensive, slow, and doesn't scale. Organisations spend thousands of hours maintaining AI inventories, researching regulatory requirements, and conducting compliance assessments. ComplyChain AI reduces this manual effort by 85%, allowing compliance teams to focus on high-value activities like policy development and risk mitigation rather than data collection and documentation.

The cost savings are significant. A mid-sized organisation with 50 AI systems might spend 10-20 hours per system per year on compliance activities—500-1,000 hours annually. At typical compliance specialist rates of £75-150 per hour, that's £37,500-150,000 in annual costs. ComplyChain AI reduces these costs to £2,000-3,000 per month (£24,000-36,000 annually) whilst improving accuracy and providing continuous monitoring rather than point-in-time assessments.

The risk mitigation value is even more compelling. Non-compliance with AI regulations can result in fines up to €35 million or 7% of global annual turnover under the EU AI Act. Reputational damage from biased AI systems can cost far more. ComplyChain AI helps organisations avoid these risks by providing continuous visibility into AI compliance status and early warning when systems drift out of compliance.

The RegXperience Demo

To demonstrate ComplyChain AI's capabilities, we built RegXperience—an interactive web application that showcases all three agent iterations working together.

The Asset Discovery Dashboard visualises AI systems across the organisation, categorising them by risk level and compliance status. Users can drill down into individual systems to see detailed metadata, ownership information, and compliance findings. The dashboard provides the executive-level visibility that boards and regulators increasingly demand.

The NIST AI RMF Chatbot offers an interactive interface for asking compliance questions. Users can query specific requirements—"What documentation is required for high-risk AI systems?"—and receive contextual answers grounded in the actual regulatory text. The chatbot includes citations, allowing users to verify that answers accurately reflect regulatory requirements.

The Compliance Assessment interface allows users to trigger assessments of individual AI systems or portfolios of systems. The assessment agent evaluates each system against applicable regulatory requirements and generates a detailed report. Findings are categorised by severity, with specific remediation recommendations for each gap identified

The Compliance Reporting capability generates executive summaries suitable for board presentations and detailed technical reports suitable for regulatory submissions. Reports include evidence documentation, demonstrating not just that systems are compliant but providing the audit trail that regulators require.

Lessons from the Agentic AI Cohort

ComplyChain AI was developed as a capstone project for an Agentic AI cohort in June 2025. The cohort focused on building autonomous, multi-agent systems that could perform complex tasks with minimal human intervention. The experience taught us several lessons about designing effective AI agents.

First, start with clear boundaries. Early iterations of ComplyChain AI attempted to do too much—discover assets, interpret regulations, conduct assessments, and generate remediation plans all in a single agent. This created a system that was complex, difficult to debug, and prone to errors. By breaking the problem into three distinct agents, each with a clear responsibility, we created a system that was more reliable, more maintainable, and easier to extend.

Second, human-in-the-loop validation is essential. Whilst AI agents can automate much of the compliance process, critical decisions still require human judgment. ComplyChain AI implements validation checkpoints where compliance specialists review AI-generated findings before they're used for regulatory reporting. This hybrid approach combines the efficiency of automation with the judgment of human experts.

Third, grounding is critical for regulatory applications. Early versions of the Intelligence Agent sometimes generated plausible-sounding but inaccurate interpretations of regulatory requirements—a phenomenon known as hallucination. By implementing RAG architecture that grounds responses in actual regulatory text and adding verification mechanisms that check accuracy, we dramatically reduced hallucinations and improved user trust in the system.

Fourth, cost management matters. AI agents that make frequent LLM calls can become expensive quickly. We implemented intelligence caching that stores common queries and responses, reducing redundant API calls. We use smaller, faster models for simple queries and reserve larger models for complex analysis. These optimisations reduced our monthly operating costs by over 60% without sacrificing accuracy.

The Future of AI Compliance

ComplyChain AI represents a new approach to compliance management—one where AI systems help organisations comply with AI regulations. The irony isn't lost on us. But it's also inevitable. As AI systems proliferate, manual compliance processes simply won't scale. Organisations need automated tools that can keep pace with the rate of AI deployment.

The regulatory landscape is evolving rapidly. The EU AI Act came into force in 2024 and is being implemented in phases through 2027. The UK is developing its own AI regulatory framework. The United States is considering federal AI legislation. Each jurisdiction is taking a different approach, creating a complex patchwork of requirements that organisations must navigate.

ComplyChain AI is designed to evolve with this regulatory landscape. The vector database can be updated with new regulatory frameworks as they're published. The assessment agent can be configured with jurisdiction-specific requirements. And the reporting capabilities can generate compliance documentation tailored to different regulatory regimes.

We're also exploring extensions beyond regulatory compliance. The same multi-agent architecture could be applied to ethical AI governance, helping organisations ensure that AI systems are fair, transparent, and aligned with organisational values. It could support AI risk management, identifying and mitigating risks before they cause harm. And it could enable AI lifecycle management, tracking systems from development through deployment and eventual decommissioning.

Practical Applications for SMEs

Whilst ComplyChain AI was developed as a research project, the need it addresses is real and immediate. SMEs deploying AI systems face the same compliance challenges as large enterprises but lack the resources to address them through manual processes.

For SMEs, the value proposition is particularly compelling. A small compliance team can use ComplyChain AI to manage AI governance across the organisation, ensuring that systems are compliant without hiring additional staff. The automated inventory capabilities provide visibility that would otherwise require dedicated resources. The regulatory intelligence agent makes compliance expertise accessible to teams that can't afford full-time compliance specialists. And the assessment capabilities provide the evidence that customers and regulators increasingly demand.

The system is also designed to be accessible. The web interface requires no technical expertise—compliance teams can use it without understanding the underlying AI technologies. The API endpoints allow integration with existing governance, risk, and compliance (GRC) platforms. And the modular architecture means organisations can adopt individual components—starting with asset visibility, then adding regulatory intelligence and assessment capabilities as needs evolve

The Path Forward

AI compliance is no longer optional. Regulations are coming into force. Customers are demanding transparency. Investors are asking about AI governance. Organisations that can demonstrate robust AI compliance will have competitive advantages. Those that can't will face regulatory sanctions, reputational damage, and lost business opportunities.

ComplyChain AI demonstrates that autonomous compliance management is possible. Multi-agent systems can discover AI assets, interpret regulatory requirements, and conduct compliance assessments with accuracy that rivals human experts and speed that far exceeds manual processes. The technology exists. The question is whether organisations will adopt it before compliance failures force their hand.

At Zazo Tech, we're exploring how to take ComplyChain AI from research project to production system. The technical foundations are solid. The business case is clear. And the market need is urgent. For organisations deploying AI systems—particularly SMEs that lack dedicated compliance resources—autonomous compliance management isn't a luxury. It's a necessity.

The age of autonomous AI is here. The question is whether your organisation's compliance capabilities can keep pace. ComplyChain AI suggests that with the right tools, they can.

About the Author

Abdul Khader Abdul Hanif is the founder of Zazo Tech, a UK-based consultancy specialising in security-first digital transformation. He led the development of ComplyChain AI as part of an Agentic AI cohort in June 2025, alongside Divya Venkataraman and Dev Jadhav. He has extensive experience with AI governance, regulatory compliance, and autonomous systems, developed through work with the UK's financial regulator and major financial institutions.

Technical Note

ComplyChain AI is a research project demonstrating autonomous compliance management capabilities. The system architecture, performance metrics, and cost estimates presented in this article are based on the capstone project developed in June 2025. Organisations interested in implementing similar capabilities should conduct their own assessments of regulatory requirements, technical feasibility, and cost-benefit analysis.