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Evidence-Based RAG & AI Integration

Large language models are only as reliable as the information they draw on — and general-purpose document retrieval is rarely sufficient for knowledge-intensive domains. Our MCP-server solution exposes your full organisational knowledge stock as structured, typed tool endpoints for any LLM (Claude, OpenAI, Gemini, or self-hosted).

Unlike generic document RAG (Retrieval Augmented Generation), answers are grounded in formally typed models, enabling the system to combine static domain knowledge with live operational state — answering questions like “Which of our registered data sources meets the retention requirements for this new reporting workflow?” All AI-generated content passes through the same review and approval workflow as any manual change: every output is auditable and revisable by a responsible person before it takes effect.

Knowledge sources

  • Business and research reports, governance models, data lineage, and technical documentation
  • Domain-specific schemas, regulatory documents, and audit history

Supported LLMs

Any LLM via the open Model Context Protocol (MCP) standard — tested with Claude, OpenAI, Gemini, and self-hosted models (Ollama, vLLM).

Key properties

  • Grounded answers — responses are based on formally typed models, not unstructured document chunks
  • Access control — LLM sessions operate within the permissions of the authenticated user (Keycloak RBAC)
  • Full auditability — all LLM-generated outputs can pass through the standard governance workflow with audit trail via the Digital Notary
  • No additional vector database — uses the same Lucene KNN indexing as the model registry

Typical use cases

  • Regulatory document intelligence with precise article and page citations
  • Technical onboarding: engineers query the model registry for existing schemas before building a new connector
  • Governance queries combining live operational state with formal domain knowledge
  • Structured, evidence-backed analysis and reporting

Technical details on the MCP Server implementation are available on the Development overview page.

Looking for strategic guidance on how AI integration fits your specific system landscape? Our consulting offering covers AI strategy, architecture, and implementation.

Get in touch

Interested in this solution? Write to info@datainmotion.com.