AI Risk Management System with Enterprise RAG
The Problem
As organizations adopt LLMs, they face critical challenges: hallucinations from incomplete or incorrect context, prompt drift and inconsistent outputs, lack of traceability in AI decisions, inability to audit or reproduce responses, and regulatory and compliance exposure.
Traditional "prompt + model" setups are insufficient for high-stakes environments such as risk, compliance, legal, or finance. The client needed a controlled AI system that can reason, retrieve, decide, and explain every step it takes.
In regulated industries, the question isn't just "what did the AI say?" — it's "why did it say it, what data did it use, and can we prove it?" Every response must be traceable back to its source and decision path.
Context
Enterprise AI adoption is accelerating, but most implementations treat LLMs as black boxes — input goes in, output comes out, and nobody can explain what happened in between. For organizations in risk management, compliance, and finance, this opacity is a non-starter. Regulators demand explainability, internal teams need reproducibility, and leadership needs confidence that AI decisions are grounded in verified data.
The client needed more than a chatbot with a knowledge base. They needed a governed AI reasoning engine with full audit trails.
Approach
Decision-Tree-Driven AI Reasoning
We implemented an agentic RAG architecture where AI behavior is governed by explicit decision trees instead of free-form prompts. The AI decides which tool to use based on context, retrieval is constrained to approved data sources, each decision step is logged and reproducible, and responses are grounded in verified enterprise data. This replaces opaque prompt chains with structured, auditable reasoning flows.
Prompt Governance & Risk Controls
Rather than static prompts, the system uses modular, versioned prompt templates. Environment-aware prompts support risk, compliance, and analysis modes. Base reasoning is separated from complex reasoning models, with prompt versioning and rollback, guardrails against unsupported actions or data access, and explicit end-goal criteria for AI decision termination. This ensures outputs remain consistent, explainable, and policy-aligned over time.
Balancing AI flexibility with strict governance was the core engineering challenge. Too many constraints and the system becomes useless; too few and it becomes unauditable. The decision-tree architecture threads this needle by allowing powerful reasoning within defined boundaries.
Secure Retrieval-Augmented Generation
The platform integrates with enterprise vector databases to ensure responses are grounded in approved datasets only. Controlled retrieval from structured and unstructured sources, collection preprocessing with schema awareness, context window optimization to reduce noise, and retrieval metadata attached to every response. Teams can audit exactly why an AI said what it said — from answer to source to decision path.
Architecture
Designed for security, extensibility, and enterprise deployment:
- Backend: Python + FastAPI with an agentic execution engine driven by decision trees
- AI & RAG Layer: Modular decision-tree agents, tool-based reasoning, and enterprise-ready vector retrieval integration
- Database: PostgreSQL with vector database integration, conversation and state persistence
- Infrastructure: Dockerized services with production-ready deployment and environment isolation
- Security: API key isolation, environment-based permissions, encrypted data handling, and audit-friendly logs
Results
The platform delivered a production-ready AI risk management system that eliminated black-box AI behavior through structured reasoning. Every AI output is now auditable and explainable, hallucination and prompt-drift risks are significantly reduced, and the system creates a foundation for compliant AI adoption at scale. It demonstrates how enterprise AI can be powerful without being dangerous.
"Ahmed and the team at Texagon did excellent work on our RAG system. The development team was fast and thorough, and clearly communicated their weekly goals and project updates. They were responsive to feedback and offered useful suggestions that demonstrated clear understanding of the project goals. Our project manager, Iqra, was quick to reach out with questions and readily available to discuss concerns. The lead developer, Ziyan, was well-versed in the latest cutting-edge AI technology. In just a few months, we were able to roll out Phase I of the system. We would highly recommend Texagon to anyone looking for a robust, professional AI system." — Client, Enterprise AI Risk Management
Next Steps
The client is expanding the platform into Phase II, incorporating additional data sources and more complex multi-step reasoning chains. The roadmap includes broader tool integrations, fine-tuned domain-specific models, and scaling the system across additional business units and regulatory frameworks.