A documented 12-month conversion from tribal knowledge to operational intelligence at a growing defense electronics manufacturer — and the research on where this market, and this technology, go next.
Read the case studyAn ITAR-registered defense electronics manufacturer was growing 30% a year on a $93M backlog — powered by heroes, spreadsheets, and a 48-to-72-hour Clear-to-Build cycle. Twelve months later it runs on an operational intelligence platform inside its own compliance boundary. Here is how, in three moves.
A complete CUI classification table, a written AI governance policy, and a GovCloud architecture the CISO signed off on — before any model touched company data. Within three weeks of the policy publishing, employee AI experimentation jumped. The barrier was never interest. It was uncertainty.
AP matching automated in 45 days. A Clear-to-Build intelligence layer wired ERP, MES, and procurement into one always-current readiness view — surfacing supply-chain gaps 18 to 21 days before they reach the floor and eliminating the daily analysis cycle entirely.
A go/no-go model built from the company's own win/loss history scores any inbound RFQ in under 20 minutes. Proposal labor on losing bids was cut in half; win rate nearly doubled — because the company only chases programs where the fit is real.
The platform reads from systems of record and never writes back. AI surfaces. Humans decide. Humans act. The system learns — on the company's own data, inside its own boundary, as an asset it owns.— The architecture principle behind the engagement
Four documents. One argument: in this market, operational discipline you can prove is the most valuable asset a manufacturer can own.
The two-page version: the challenge, the three workstreams, the results table, and why it worked. Built for a fast read by an operator or a board.
Get the short case studyThe full account: the situation under the growth, the architecture decisions, workstream-by-workstream results, the 31-to-86 scorecard method, and what it means for a buyer in diligence.
Get the detailed case studyWhat is actually happening in the defense industrial base: the CMMC arithmetic that doesn't close, the primes enforcing early, capital paying certified premiums — and why the next 28 months re-price the whole base. Fully sourced.
Read the opinion paperWhy AI agents interacting with software — and each other — is now a certainty: five classes of proof, six honest obstacles, and the ten signals to watch next. Fully sourced.
Read the white paperThe client is in an active strategic process. The engagement is documented end-to-end — systems, workstreams, and outcomes — and the company is named generically to protect its transaction posture. Qualified parties can request more under NDA.
No — and that distinction is the whole point. Most enterprise AI pilots fail to show P&L impact because they bolt tools onto ungoverned data with no baseline. This engagement started with the compliance boundary and the measurement discipline, which is why the results are legible to a CFO — and to a buyer.
It was designed for diligence: read-only architecture, full audit trails, role-scoped access, and metrics a financial buyer already tracks — close cycle, delivery performance, AP efficiency, pipeline discipline, margin quality.
Everything. The same boundary that CMMC forces you to build is the safest place to run AI on controlled data. Companies that treat certification as an asset purchase get both: contract eligibility and a compliant home for owned intelligence. The opinion paper makes the full argument.
Improvement you cannot prove is improvement a buyer will not pay for. The first step is a measured baseline of your operations — before the wall, and before the next platform decision.
Request a baseline conversation