RAG Data Platform Engineering for Enterprise Knowledge

The operating problem

Most enterprises have valuable knowledge, but it is not easy to use. Documents sit in drives, records live in business applications, service knowledge is buried in tickets, and analytics logic is spread across dashboards and spreadsheets. People ask the same questions repeatedly because the answer is hard to find.

Basic search is not enough for AI adoption. RAG systems need clean ingestion, chunking strategy, metadata, permissions, freshness checks, retrieval evaluation, and a way to show sources. Without those foundations, AI assistants can sound confident while missing the right context.

The Mativyx approach

Mativyx designs the RAG platform as a data product, not a prompt experiment. The architecture starts with source inventory and content lifecycle rules. Each source is classified by owner, permission model, update frequency, business domain, and usage risk.

The platform then builds ingestion pipelines for documents and structured data, creates embeddings, stores searchable metadata, and exposes retrieval services to assistants and agent workflows. Evaluation datasets test whether the system retrieves the right source, answers with grounded context, and handles unknowns honestly.

• Lakehouse and document ingestion for structured and unstructured enterprise knowledge.
• Vector search, hybrid retrieval, metadata filters, and permission-aware access.
• Answer generation with citations, confidence signals, and escalation paths.
• RAG quality monitoring for stale content, weak retrieval, hallucination risk, and user feedback.

What changes for the business

Employees spend less time searching and more time acting. Support teams can find runbooks and previous resolutions. Project teams can locate lessons learned. Procurement teams can compare clauses. Compliance teams can retrieve policy references with evidence.

The real value is continuity. Knowledge survives team changes, scattered files become usable, and AI assistants answer from the organization's own context instead of generic internet memory.