AI for Document Search
Enterprise document search using RAG over internal wikis, contracts, HR policies, and technical documentation. Surface accurate answers with source citations in seconds, not hours of manual digging.
Quick answer
The best enterprise document search stack uses a RAG pipeline: ingest documents into a vector database (Pinecone, Weaviate, or pgvector), embed with text-embedding-3-large or Voyage AI, and generate cited answers with claude-sonnet-4 or GPT-4o. Deployed on-premise or in your VPC, expect 90%+ answer relevance on internal queries at $20–$40 per seat per month all-in.
The problem
Enterprise employees spend an average of 1.8 hours per day searching for internal information — that's 22.5% of a knowledge worker's day, costing a 500-person company over $6M annually in lost productivity. Keyword search tools like SharePoint and Confluence return lists of documents rather than answers, with a user satisfaction rate below 40%. New employees take 3–6 months to become proficient because institutional knowledge is buried across hundreds of disconnected repositories.
Core workflows
Document Ingestion and Chunking
Parse PDFs, Word docs, Notion pages, and Confluence articles. Apply semantic chunking (512–1024 tokens with 15% overlap). Index into a vector store with rich metadata (author, date, department, access level). Ingestion pipelines process 100,000-page corpora overnight.
Semantic Question Answering with Citations
Accept natural language questions, retrieve top-K relevant chunks, generate grounded answers with inline source links. Reduces time-to-answer from 20 minutes of manual search to under 15 seconds.
Contract and Policy Clause Lookup
Answer specific legal and policy questions ('What is our standard indemnification cap?', 'Which contracts expire in Q3?') with exact clause references. Reduces legal team research time by 60–80%.
Access-Controlled Search
Enforce row-level security so employees only retrieve documents they have permission to view. Integrates with Okta, Azure AD, and Google Workspace for group-based filtering at query time.
Slack and Teams Search Integration
Surface document answers directly in Slack or Teams channels via a bot. Answer '@bot What is our PTO policy for contractors?' without leaving the conversation. Reduces context-switching overhead for common questions.
Document Freshness Monitoring
Track document versions and re-index when source content changes. Detect when answers may be stale (document modified after last index) and surface a freshness warning. Critical for HR policies and compliance documents.
Top tools
- Glean
- Guru
- Pinecone
- Weaviate
- LlamaIndex
- Notion AI
Top models
- claude-sonnet-4
- gpt-4o
- text-embedding-3-large
- claude-haiku-3-5
FAQs
What chunk size should I use for enterprise document RAG?
For most enterprise documents, 512–768 tokens per chunk with 15–20% overlap performs best. Shorter chunks (256 tokens) improve precision for dense technical documentation and legal agreements where individual clauses are the unit of meaning. Longer chunks (1024–2048 tokens) work better for narrative documents like policy guides where context across paragraphs is critical. Always benchmark chunk sizes against your specific document corpus — a 10% change in chunk size can move retrieval accuracy by 5–15 percentage points.
How do I prevent the AI from hallucinating information that isn't in the documents?
The key controls are: (1) strict grounding prompts that instruct the model to answer only from retrieved context and say 'I don't have information on this' when context is insufficient, (2) confidence scoring on retrieved chunks — refuse to answer when top-1 similarity score is below 0.75, (3) inline citations that users can click to verify, and (4) human-readable source attribution in every response. Models like claude-sonnet-4 are particularly good at refusing to speculate when properly instructed. Hallucination rates below 2% are achievable with these controls in place.
How do I handle document access controls in a RAG system?
Implement access control at two layers: (1) ingestion filtering — tag each document chunk with permitted user groups from your identity provider (Okta, Azure AD), (2) query-time filtering — filter the vector search to only retrieve chunks the requesting user is authorized to see. Never retrieve first and then filter — this can expose document existence to unauthorized users. Tools like Weaviate and Pinecone support metadata-based access filters natively. Test your access control implementation with adversarial prompts (prompt injection attempts to retrieve forbidden documents).
What's the difference between Glean, Guru, and building your own RAG?
Glean is a turnkey enterprise search SaaS (150+ connectors, SSO, access control built-in) that works in 2–4 weeks but costs $25–$50 per seat per month and has limited customization. Guru focuses on curated knowledge bases with human-verified cards, better for compliance-sensitive content but requires ongoing curation effort. Building your own RAG (LlamaIndex + Pinecone + Claude) takes 4–8 weeks of engineering but gives full control over chunking strategy, models, UI, and cost. Choose Glean/Guru for rapid deployment across a large organization; build your own when you have specific document types, access control complexity, or cost requirements that off-the-shelf solutions can't meet.
How do I keep the search index fresh when documents change frequently?
Implement a change detection layer: poll source systems (SharePoint, Confluence, Google Drive) for document modification events via webhooks or scheduled diffs. On change, re-chunk and re-embed only the modified document, delete old vectors by document ID, and insert new vectors. For Notion and Confluence, their native webhooks fire within seconds of edits. For SharePoint, Microsoft Graph webhooks are reliable. Set a staleness warning if the last index timestamp for a retrieved document is older than 7 days — this covers 95% of policy and process documents that change on a weekly cadence.
What's a realistic timeline to deploy enterprise document search?
Using a managed solution like Glean: 2–4 weeks (connector setup, SSO integration, user onboarding). Building on LlamaIndex/Pinecone: 6–10 weeks for a production-ready system (document ingestion pipeline, embedding, vector store, API, Slack bot, access control). Add 2–4 weeks for each major document source with non-standard formatting (legacy SharePoint, custom ERP exports). Budget $30,000–$80,000 in engineering time for a custom build covering 5–10 document sources with full access control.