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RAG & Agent Memory

Retrieval-augmented generation and persistent memory so agents use your data, not guesses.

Agents need context — your business data, past conversations, documents, and procedures. We build retrieval-augmented generation (RAG) pipelines that let agents ground their responses in your real data, not hallucinate. Agent memory persists across sessions so the agent learns your business over time.

What it includes

  • Vector database setup (pgvector, Pinecone, Weaviate)
  • Document ingestion pipelines (PDFs, docs, knowledge bases)
  • Semantic search with relevance scoring
  • Agent memory — conversation history, preferences, learned patterns
  • Knowledge base versioning and update workflows
  • Citation — agents cite their sources, not just generate text

Safety controls

How this keeps AI safe

  • Grounding — agents must cite a source before answering factual questions
  • Confidence thresholds — low-confidence retrieval triggers human review
  • Data isolation — each client's knowledge base is isolated
  • Access control — role-based retrieval (agents only see what the user can see)
  • Memory audit trail — every memory update is logged and reviewable

Which agents use this

Lead Agent — grounded in your product catalog and pricingCompliance Agent — retrieves from regulatory documents with citationsOperations Agent — remembers past decisions and workflowsField Agent — retrieves job history and client preferences

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