RAG Implementation: Making Enterprise Documents AI-Ready — Structured, Secure, and Scalable
Retrieval-Augmented Generation (RAG) is currently the most in-demand approach for making internal knowledge bases, policy documents, and contract repositories accessible to AI assistants — without passing data to external models or going through expensive fine-tuning processes. I guide companies from requirements gathering and architecture evaluation (LangChain, LlamaIndex, Azure AI Search) through chunking strategy to production-ready handover to the development team — as a Business Analyst and Project Manager focused on governance and compliance.
Typical Situations
- Employees spend significant time on manual document search — policy documents, contracts, or manuals are hard to search effectively
- IT delivered a LangChain or Azure AI pilot, but answer quality is inconsistent and the business team doesn't understand the architecture
- The organization wants to use RAG, but data protection and compliance requirements are unresolved
- Regulated environment (bank, insurer): internal policies, compliance documents, and regulatory circulars should become searchable — with traceable source references
- Chunking strategy and embedding model selection are open — nobody knows how to optimize for the specific document type
- An existing pilot has a high hallucination rate — retrieval quality assurance and evaluation concept are missing
- Management has approved budget for RAG, but no structured requirements document exists for procurement or development
Deliverables
Steering & Governance
RAG Project Steering: Structured steering of RAG projects — from document selection and architecture decisions through pilot operations and quality assessment to go-live approval. Decision papers for stakeholders, IT governance, and compliance committees. Clear milestones instead of endless iteration without direction.
Retrieval & Prompt Governance: Versioned prompt library, documented chunking and embedding decisions, retrieval quality metrics, and monitoring concept. The foundation for transparent and maintainable RAG solutions — especially important when new documents can change system behavior.
Compliance & Data Protection Documentation: Documented data flows, model selection rationale, GDPR compliance (DPIA, processing register), and EU AI Act risk classification. Audit-ready documentation for Data Protection Officers, compliance teams, and auditors — standard practice in regulated industries.
Data Protection & Regulatory Requirements
RAG systems often process sensitive internal documents. In regulated industries, additional requirements apply. I work closely with compliance and data protection teams to:
- Document data flows transparently: which documents are indexed? Where are embeddings stored? Which data is sent to external model providers?
- Clarify GDPR requirements: processing purpose, legal basis, DPIA for new processing activities (Art. 35 GDPR)
- Document model selection and provider governance transparently (EU AI Act, BAIT, VAIT for FinServ)
- Integrate hallucination risks and source reliability into the quality assurance concept
- Address DORA requirements for AI-powered information systems in financial institutions
Project contexts are anonymized. Roles and outcomes are accurately described; details available under NDA.
Project Examples (Anonymized)
Insurance Company: Policy Document Search with RAG and LangChain
DACH Insurance Company — Knowledge Management
Challenge: Thousands of pages of insurance terms and internal policies were not efficiently searchable. Employees spent an average of 20 minutes per query on manual document search — a significant efficiency gap.
Role: Technical Business Analyst: RAG concept, LangChain stack evaluation, chunking strategy, and pilot requirements; handover to development team
Results:
- LangChain evaluated against LlamaIndex and Azure AI Search — documented decision matrix with 8 evaluation criteria
- Chunking strategy and embedding model selection for insurance documents (hierarchical chunking) specified
- Pilot requirements with 30 defined test cases and quality criteria handed over
- Governance framework for prompt versioning and model updates established
Note: Project contexts are drawn from previous consulting and industry roles. Content is anonymized; roles and results are accurately described.
Financial Institution: RAG-Based Compliance Document Search
German Financial Institution — Regulatory Intelligence
Challenge: The compliance team regularly needed to search regulatory circulars, internal policies, and regulatory requirements. Classical full-text search returned too many irrelevant results; response time was too slow for time-critical compliance decisions.
Role: Business Analyst and Project Manager: requirements gathering, architecture review (Azure AI Foundry), GDPR clearance, and structured handover
Results:
- Requirements specification for semantic search with source attribution and confidence scoring
- Architecture decision Azure AI Foundry vs. LangChain documented and aligned with IT governance
- GDPR data flow documentation: indexing, embedding storage, external model requests
- Evaluation concept with 25 test cases derived from real compliance queries
Frequently Asked Questions
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Last updated: February 2026