A read on where AI fits in the AmePower stack. And where a rule engine or a spreadsheet is still the right answer.
High-power converter design, retrofit, modernization, and manufacturing. Twenty-plus years in Miami. Nine defense sub-applications from directed energy through submarine propulsion. SBA Florida Business of the Year 2023. An engineering-first team pushing deeper into defense is now running CMMC 2.0 readiness, ITAR documentation, and the AMEDATA software roadmap in parallel. This page is a short read on where an outside methodology partner would help.
Most organizations automate the wrong things.
About 60 percent of what a team does every day is traditional code and database work. About 30 percent is rule-based logic. Around 10 percent is a real AI problem. The work is sorting those three into the right layers before anything is built.
Applied to a specialty power-electronics manufacturer moving deeper into defense, the map resolves quickly. The retrofit lessons archive, proposal library, compliance evidence pack, and AMEDATA telemetry store are database and code work. ITAR export-control gates, CMMC control mapping, DFARS clause selection, and AMEDATA alarm rules are rule-based logic. Predictive maintenance on converter fleets, cross-program lesson extraction, and retrofit-spec pattern matching are where AI actually earns its keep.
Sorting those layers before anyone writes a line of AMEDATA code is the first conversation.
A regulated, document-heavy, long-cycle pursuit environment with tribal knowledge trapped in individual operators. The orchestration pattern collapsed 160 plus hours of manual compliance work to under 5 minutes per cycle. The operating shape is the one that matters for a power-electronics manufacturer running CMMC 2.0 and retrofit work.
- DRC mining compliance documents CMMC 2.0 evidence stack and ITAR documentation
- Multi-agent compliance workflow Defense-pursuit proposal library (9 sub-applications)
- Operator tribal knowledge Retrofit engineering lessons (LA Metro, future defense)
The methodology is published.
Interpretable Context Methodology: Folder Structure as Agent Architecture, submitted to ACM TiiS. Agent context organized as a layered filesystem. Open source under MIT license. A hardware-first company can adopt the filesystem discipline without committing to any specific AI layer up front.
Repo: github.com/RinDig/Interpretable-Context-Methodology-ICM-
Who is on the other side of the table.
- 01Jake Van Clief, founder. Marine Corps veteran, eight years on cryptographic systems and F-35 and F-18 avionics. MSc Future Governance, University of Edinburgh.
- 02Published in ACM TiiS (Interpretable Context Methodology) and arXiv (Ethics Engine).
- 031,500 plus enterprise learners trained since May 2025 across Correlation One (Pacific Life, Colgate-Palmolive) and KPMG UK, one of the Big Four.
- 04Eduba partners with NLP Logix for work that sits below the orchestration layer. NLP Logix has been in machine learning since 2011 and runs over 150 data scientists.
Thirty minutes with Matt Van Clief.
Bring one compliance or retrofit document you wish had written itself. We walk through the 60 / 30 / 10 map live and scope the first sprint from there.
If the conversation lands somewhere else, we say so on the call.