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A private RAG assistant retrieves from your knowledge base with embeddings and vector search, cites sources, refuses out-of-scope questions, and respects role-based access—designed for production, not a demo upload box.
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Employees waste time searching wikis, PDFs, and tickets. Leadership wants ChatGPT-like answers but security requires controlled data and auditability.
A structured delivery path—not vague promises.
Classify sources, permissions, and retention rules.
Ingestion, retrieval, citation UI, evaluation.
Expand corpora with update/delete workflows.
Balanced guidance—not one-size-fits-all answers.
RAG updates with documents; fine-tuning needs retraining cycles—see our AI cost guide.
Primary capability pages for this topic.
Add production-ready AI capabilities to an existing mobile app, web platform, SaaS product, or internal system without rebuilding the entire product. We integrate intelligent search, assistants, automation, recommendations, document intelligence, and AI agents with your current architecture, data, APIs, authentication, and workflows.
Production AI agents connected to your business tools, APIs, and approval workflows—with permissions, audit trails, cost controls, and human checkpoints.
Evaluation framework for production RAG systems—measuring retrieval accuracy, answer faithfulness, permission enforcement, citation quality, and content freshness.
Technical guidance for integrating the Model Context Protocol (MCP) in enterprise environments—authentication, server hardening, data boundaries, and operational controls.
Build an evaluation set of real questions, score relevance of retrieved chunks, and track answer faithfulness separately from fluency. See production RAG evaluation.
Filter retrieval by the same authz rules as the source systems—never retrieve documents the user cannot access.
Enforce tenant isolation in indexes and queries. Shared corpora need strict metadata filters and tests that prove cross-tenant denial.
Return citations to source documents/chunks so users can verify answers. Citations are a trust requirement, not a nice-to-have.
Define re-ingestion when sources change, with ownership for each corpus. Stale indexes create confident wrong answers.
Propagate deletes to the vector index and caches on a defined SLA. Soft-deleted sources must not remain retrievable.
When retrieval confidence is low, sources conflict, or the question is out of scope. Explicit refusal beats a fluent guess.
Re-run the evaluation set on corpus, prompt, or model changes. Treat quality regressions like product bugs.