// SERVICE
Document Intelligence Platform
A queryable layer over your documents — contracts, filings, research, PDFs, Slack history, email archives — that returns accurate, cited answers instead of stale keyword searches. Built on RAG architecture, deployed on your infrastructure, measured for accuracy rather than vibes.
The problem with enterprise search in 2026
Most organizations have more documented knowledge than any human can reasonably retrieve: SharePoint archives from the last decade, Confluence wikis, contracts in DocuSign, emails in Outlook, Slack threads, Google Drive, S3 buckets of PDFs. Full-text search returns 400 hits; Ctrl-F works only if you already know where the file lives. The actual question — "what did we tell the Smith account about indemnification in 2024?" — is answerable only by the person who happened to have touched that deal.
Document intelligence is the architecture that fixes this: a single queryable layer over every document the organization owns, returning accurate answers with citations back to the source.
What a production document intelligence platform includes
Unified ingestion
Connectors for the formats and systems you actually have: SharePoint, Confluence, Notion, Google Drive, Dropbox, S3, Exchange/Outlook, Slack, Jira, Box, email PST archives, scanned PDFs via OCR, audio/video transcripts. We handle the messy stuff: tables split across pages, footnotes, nested headings, scanned documents with handwritten annotations, inline spreadsheets.
Structure-aware processing
Documents aren't flat text. A contract has clauses and exhibits; a financial filing has sections and tables; an email thread has history and attachments. We preserve structure during chunking so a retrieval about "termination" finds the termination clause with its surrounding section context, not a sentence fragment from the middle of a lease.
Access control that survives audit
Permissions in the source documents carry through to retrieval. If a user can't access a SharePoint folder, they can't retrieve from it in the AI interface. Row-level security for regulated-industry data. Full audit trail of every query, every result, every document accessed.
Citation-enforced answers
Every claim in an answer links to a source chunk with document name, page, and author. Citation-validator middleware blocks answers whose claims don't trace to retrieved content. Lawyers, analysts, and auditors have something to verify. See our deeper RAG pipeline page.
Admin, analytics, and feedback loop
Which documents get retrieved most? Which questions get "I don't know" responses? Which users have the highest satisfaction scores? The admin dashboard surfaces this so the content team knows what to update, what to consolidate, what to deprecate.
Common document intelligence deployments
- Legal knowledge management — firm-wide search over briefs, memos, prior matters, research archives. See AI for legal firms.
- Contract intelligence — clause extraction, comparison against playbook, obligation tracking across an entire contract portfolio.
- Research and analyst support — synthesis across internal research plus authorized external sources (filings, industry reports, news). See AI for financial services.
- Clinical protocol and guideline lookup — cited answers over internal protocols, order sets, drug interactions. See AI for healthcare.
- Engineering knowledge base — runbooks, design docs, postmortems, vendor documentation, codebase semantic search.
- Customer-facing knowledge — powering support AI with your product docs. See customer support AI.
Measurable accuracy, not vibes
Every deployment ships with an evaluation harness: a test set of real questions with known-good answers, scored on every model change, prompt edit, or retrieval tweak. You see retrieval accuracy, answer grounding, refusal correctness, and response-quality numbers on a dashboard. If the metrics move the wrong way, we see it before your users do.
Pricing
POC ($25K–$60K, 4–6 weeks): one document corpus, one interface, one well-defined use case, real evaluation. Production build ($75K+, 8–16 weeks): multi-source ingestion, access controls, admin UI, analytics, 30-day stabilization. Local by default — your documents never leave your infrastructure.
Frequently asked questions
How is this different from SharePoint search or Google Workspace search?
Native platform search is keyword-based — it returns documents that match your words. Document intelligence is semantic: it returns *answers*, with citations, based on meaning rather than exact word matches. A user asks "how do we handle indemnification for SaaS deals?" and gets a synthesized answer with links to the specific contract sections, not 80 documents containing the word "indemnification".
What happens to user permissions and access controls?
Source-system permissions carry through. If you can't access a document in its native store, you can't retrieve from it in the AI interface. For regulated industries we add row-level security, audit logging of every query, and the ability to show a compliance officer exactly what any given user has accessed.
Can it handle scanned PDFs and image-heavy documents?
Yes, with OCR in the ingestion pipeline. Image-heavy documents (engineering drawings, medical imaging) need additional vision models when the images themselves need to be searchable — we handle that when the use case requires it.
How long does initial ingestion take?
Depends on corpus size. A 100,000-document archive typically processes in 12–48 hours on a single GPU. Incremental updates after that are near-real-time. We'll size in discovery based on your actual volume.
Can users also upload documents directly, or is it system-sourced only?
Both. Many deployments include a "drop a document in and ask questions about it" mode alongside the persistent corpus search. Uploaded documents can be private to the user, shared with a team, or promoted to the shared corpus — whatever the governance model calls for.
Ready to start?
Book a free 30-minute AI Readiness Assessment. No pitch deck. No retainer ask. Just a working session to map your stack and surface the two or three highest-ROI AI interventions for your situation.