A private, citation-grade intelligence layer over the documents your organization already owns — deployed on infrastructure you control.
Interface portraits. Generic data shown — real deployments index your corpus, your contracts, your customers.
Illustrative interface portraits · All names, agencies, programs, and contract numbers are fictional.
Most organizations are sitting on five to fifteen years of decisive material — won and lost proposals, executed contracts, technical specifications, customer correspondence, engineering reviews, supplier quotes, scanned drawings — and can't get a clean answer out of it.
The material is scattered across SharePoint, OneDrive, Outlook archives, network shares, NAS volumes, and a few hundred PDFs on someone's desktop. Search returns thousands of hits and no answer. The institutional memory is real. The retrieval cost is the problem.
Public LLMs are not the answer. The material is sensitive — pricing, customer identities, technical IP, deal positions, internal disagreements. Most of it is not posture you want sitting in a vendor's training pipeline. And generic LLMs hallucinate citations, which is fatal in any environment where a wrong answer carries contractual or program risk.
The bearing: an intelligence layer that lives inside your perimeter, reads every document in your corpus, answers questions with grounded citations, and gets sharper the longer it runs.
Pulls in PDFs, Word docs, slide decks, Outlook archives, scanned engineering drawings, image-bearing technical reports. Preserves document structure — tables, headings, multi-column layouts. Layout-aware OCR for scanned material. Watch-mode for new arrivals.
Three parallel indexes over the corpus: a dense semantic index (what does this passage mean), a sparse keyword index (what does it say), and an entity graph (which programs, agencies, customers, technologies connect to which). Hybrid retrieval fuses all three.
Given a question, finds the right passages across the corpus with reciprocal rank fusion, then re-ranks for relevance to the specific question. Returns ranked source chunks — not vague summaries.
Routes the question to specialized sub-agents (business development, technical, contracts, compliance), each operating under a persona-specific prompt. A final synthesis layer reconciles their reports into a single answer with citations preserved at the passage level.
Every answer is grounded in cited source documents, viewable inline. Overnight cron pulls in adjacent public-domain signals — federal opportunities, contract awards, market activity in your NAICS codes — and produces a morning briefing. Briefings, queries, and source documents are all browsable and searchable.
The platform is private. No document, query, or generated answer leaves your infrastructure unless you wire it that way. The corpus stays in your VPC or on your hardware. The LLM calls themselves can be routed to enterprise-grade Anthropic or OpenAI endpoints under business associate agreements.
State-of-the-art document AI (Docling, with vision fallback for image-heavy PDFs) parses incoming material into structured chunks. Tables stay as tables. Multi-column layouts get untangled. Scanned drawings get OCR'd with layout preservation. Email archives get parsed with headers, threads, and attachments intact. Every chunk gets metadata: source file, page, section heading, ingestion date, classification.
Vector database holding hybrid named vectors (dense semantic + sparse keyword) in the same record. Graph database for entities, relationships, and provenance. Postgres for metadata and audit. Object storage for the source documents themselves. The same architecture scales from a single NAS handling a hundred thousand documents to a multi-region deployment handling millions.
A query hits the hybrid retrieval layer, which runs three searches in parallel: dense vector similarity, sparse BM25 keyword match, and graph expansion around any entities mentioned in the query. Results are fused with reciprocal rank fusion, then re-ranked by a cross-encoder. Persona filters narrow the retrieval window — a technical question from an engineer returns engineering reviews ahead of marketing slides.
A research orchestrator routes complex questions to specialized sub-agents. Each gets a persona-tuned prompt and a filtered slice of context. Sub-agent reports flow back to a final synthesis layer that reconciles them and produces a single answer. Citations are structured pointers to exact passages in source documents — validated against the actual indexed material. The model cannot cite a source that does not exist.
Three interfaces, one backend. A web dashboard for the team — search, research, sources, briefings archive. An MCP server so anyone using Claude Code or Claude Desktop can query the corpus from their existing tooling. An autonomous briefing pipeline that fuses corpus context with public market signals into a morning email or shared-drive drop. Authentication is enterprise-standard: Cloudflare Access or SAML, service tokens for backend-to-backend, no public credentials.
Audit the corpus — where it lives, what formats, what volume, what's sensitive. Identify three to five canonical questions per persona. Pick a pilot scope large enough to be useful, small enough to deliver in weeks.
Stand up infrastructure (your hardware, a cloud VM, or a NAS). Ingest the pilot corpus. Build baseline retrieval and synthesis. Validate against canonical questions with a written eval harness — thresholds defined up front, not after the fact.
Expand corpus to full scope. Sub-agent orchestrator with persona prompts for actual roles. Team dashboard with single sign-on. Overnight briefing pipeline. CRM and contract-system integration where it adds leverage. Runbooks in your team's hands.
The system gets sharper the longer it runs. New documents flow in. The eval harness catches regressions. Persona prompts get tuned based on real usage. The aim is not a static deliverable. The aim is an institutional intelligence layer that compounds.
Your team has the engineering capacity to build this. We architect, set the eval bar, review the implementation at phase gates, and stay close enough to course-correct before the architecture commits the team to a dead end. Best fit: mature ML and platform teams.
Your team has some capacity but not the right specialization. We run the build in partnership with your engineers — doing integration and architecture — while your team owns operations and the long-term roadmap. Best fit: federal-adjacent organizations that need the deliverable to be operable internally after handoff.
Your team is full-up, or this is too far outside the existing skill set. We deliver the platform, the runbooks, and the eval harness, and stay engaged on a defined retainer to keep the system healthy while you decide who owns it long-term.
One conversation, one written summary, no commitment to build. The bearing comes first.
Schedule a call — 30 min