Building an Enterprise Knowledge Base with DeepSeek + RAG

A sound DeepSeek + RAG knowledge base splits roles clearly: DeepSeek handles constrained generation and summaries; the RAG pipeline owns parsing, retrieval, permissions, and citations. Fit for internal policy/wiki Q&A, sales playbooks, and support policy lookup where answers must be traceable. GeonAI can wire DeepSeek (or other models) into a bespoke KB Agent under your compliance rules; see /agents/knowledge-base-manager and /agents as capability references only (not a public trial of all 363+ presets). Email [email protected], subject “Enterprise Agent inquiry”. General RAG architecture: /blog/enterprise-rag-knowledge-base-agent. DeepSeek scenario overview: /blog/deepseek-enterprise-use-cases.

Why DeepSeek + RAG—not “just swap the model”?

DeepSeek often shines by lowering Q&A unit cost under strict citation rules, making “ask for everyone” affordable. It does not replace parsing quality or pre-retrieval ACL. Multi-model routing: /blog/multi-model-selection-guide.

Document parsing: read correctly before embedding

1. Source formats: prefer wiki/Markdown/exports; structure PDFs; OCR scans with human spot-checks on critical pages
2. Tables and images: chunk tables separately or as summary rows; never trust OCR alone for policy numbers in screenshots
3. Version metadata: doc ID, owner, effective/expiry dates, classification, language—on every chunk
4. Incremental sync: webhooks or differential jobs; avoid full rebuilds that stall the index
5. Gold set: 20–50 core docs + ~20 labeled Q&As (including must-refuse cases) before the pilot

Chunking and indexing: noise hurts more than the model

• Chunk by heading hierarchy, often 300–800 tokens; too large adds noise, too small loses context
• Keep keywords for SKUs, error codes, and jargon to enable vector + BM25 hybrid retrieval
• Embedding and DeepSeek generation can be chosen separately; if recall is weak, fix hybrid/rerank before swapping the LLM
• Mark expired docs as delisted so they never enter Top-K

Retrieval and rerank: where DeepSeek sits

Production prompts should require: refuse without chunks; citations with doc name/section/updated-at; no invented refunds, prices, or statutes. For high-liability tasks, route generation to a more conservative model while DeepSeek still helps with classify/draft via a multi-model gateway.

Access control: filter at retrieval, not after generation

• SSO/AD groups → visible collections or labels (public / internal / confidential)
• Queries carry user identity; ACL tests must block 100% before go-live
• Audit: user, hit doc versions, refuse/handoff flags
• Minimize PII in logs/prompts; never put secrets in the prompt

Deployment: API, private cloud, or on-prem?

Vectors and source text often must reside in environments you control; DeepSeek inference may use public API, private cloud, or on-prem—decide by compliance sign-off, not the myth that “a Chinese model means data never leaves.” Cost and ops for private deployment: /blog/deepseek-private-deployment-guide. Common hybrid: private retrieval + governed external generation, or in-network inference + private index.

6–10 week pilot checklist

1. One department + one doc class (e.g., IT FAQ or public-tier HR policy)
2. Run ingest → hybrid retrieve → DeepSeek cited generation → refuse path end-to-end
3. Metrics: accuracy, citation rate, correct-refuse rate, P95 latency, zero ACL breaches
4. Model gateway: timeouts, retries, fallback model; weekly error reviews
5. Only then expand partitions and external support channels

GeonAI delivery and what to prepare

Bring: document sources and update cadence, classification + SSO notes, ~20 gold Q&As, must-refuse list, latency/cost budget, deployment preference (API / private cloud / on-prem). Delivery follows our 4-step playbook; pricing factors /blog/custom-ai-agent-pricing-factors. Contact [email protected], /pricing, or Live chat. Selection pitfalls: /blog/ai-agent-selection-checklist.