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AI Engineering

RAG & Enterprise Knowledge AI

We design ingestion, chunking, metadata, embeddings, vector/hybrid retrieval, reranking, citations and evaluation so RAG quality is measurable and continuously improvable.

Measurable outcomes

  • Answers grounded in company knowledge
  • Lower hallucination risk
  • Access control enforced during retrieval
  • Measurable correctness and groundedness

Deliverables

  • Ingestion pipeline
  • Retrieval architecture
  • Access filters
  • Evaluation set
  • Tracing and quality dashboard

Delivery process

  • 01 — Knowledge audit
  • 02 — Retrieval baseline
  • 03 — Evaluation
  • 04 — Reranking/access hardening
  • 05 — Production iteration

Best suited for

  • Knowledge bases
  • Support assistants
  • Document search
  • Internal copilots
  • Policy and technical Q&A
Frequently asked questions

Common questions about RAG & Enterprise Knowledge AI

Is a vector database enough?

No. RAG quality depends on chunking, metadata, query transformation, filtering, hybrid retrieval, reranking and evaluation. The vector store is only one component.

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Ready to discuss RAG & Enterprise Knowledge AI?

Share the problem and expected outcome. We will recommend the architecture, delivery phases and practical next step.

Submitting this brief does not create a binding order; scope, risks and delivery approach are reviewed first.