Intelligence

A private, citation-grade intelligence layer over the documents your organization already owns — deployed on infrastructure you control.

The problem

The answer is in the corpus. Finding it has gotten more expensive than redoing the work.

Most organizations are sitting on five to fifteen years of decisive material — proposals, contracts, technical specs, customer correspondence, scanned drawings — scattered across SharePoint, OneDrive, Outlook archives, network shares, and a few hundred PDFs on someone's desktop. Search returns thousands of hits and no answer.

Public LLMs are not the answer. The material is sensitive. Generic models hallucinate citations — 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, and answers questions with grounded citations.

What it does

Five capabilities, one platform.

i.Ingests

PDFs, Word, slides, Outlook archives, scanned drawings. Layout-aware OCR. Watch-mode for new arrivals.

ii.Indexes

Dense semantic + sparse keyword + entity graph — in parallel, over the same corpus.

iii.Retrieves

Hybrid search with reciprocal rank fusion, re-ranked per question. Source chunks, not summaries.

iv.Synthesizes

Sub-agent orchestration per persona — BD, technical, contracts, compliance — with structured citations.

v.Reports

Cited answers, browsable sources, autonomous overnight briefings fused with SAM.gov, USAspending, and Congress.gov signals scoped to your NAICS codes.

How it's built

Five layers. Each replaceable. All under your control.

Ingest — Docling document AI with vision fallback, structure-preserving OCR. Storage — vector DB (hybrid named vectors), graph DB (Neo4j-style), Postgres metadata, object store for source files. Retrieval — hybrid search with RRF, cross-encoder re-rank, persona-scoped filters. Synthesis — multi-agent orchestrator routing to persona-tuned sub-agents; structured citations grounded to actual indexed passages. Surfaces — web dashboard, MCP server for native AI clients, autonomous briefing pipeline. Enterprise SSO or Cloudflare Access.

Implementation

Four phases. A kill switch at each one.

i. Diagnosis

1–2 weeks

Corpus audit, canonical question set, pilot scope.

ii. Pilot

3–6 weeks

Pilot corpus ingested, baseline retrieval, written eval harness.

iii. Production

6–12 weeks

Full corpus, persona sub-agents, dashboard with SSO, briefing pipeline, runbooks.

iv. Compounding

Ongoing

New content flows in, eval catches regressions, prompts tuned to real usage.

Engagement

Three shapes. Senior, scoped, unconflicted.

Advisory

You build. I architect, set the eval bar, review at phase gates.

Oversight build

We co-build. I do architecture and integration; your team owns operations after handoff.

End-to-end build

I deliver the platform, runbooks, and eval harness, then stay engaged on retainer until your team is ready to own it.

Next step: diagnosis. One conversation, one written summary, no commitment to build.

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