Most AI hands you an answer and leaves you to trust it on faith. This practice designs every system so the answer carries its evidence -- source, version, and provenance -- and so it earns its place in real, everyday work.
The session fee is credited toward any engagement you take forward.
The risk of industrial AI is not that it fails visibly. It is that it fails quietly -- a fabricated citation in a batch record, a confident wrong answer in a regulatory submission, a system no one is evaluating because it shipped as a proof of concept and never got a finish line. Most vendors do not have a plan for that. They have a demo.
When an AI system cannot trace its answer back to a source -- with version and provenance -- you cannot prove it was right, and you cannot prove it was wrong. In a GMP environment, that is a documentation failure waiting for an investigator.
Around 70% of AI pilots in small and mid-size operations stay in the experimental phase. The missing ingredient is scope, acceptance criteria, and someone accountable for the finish line.
Most AI consultants have not run a production AI system inside a regulated business. They are selling a methodology that has not been pressure-tested where a data failure is a real liability.
Most decision-makers picture a chatbot. The real value in an operation like yours is somewhere else -- retrieval over your own documents, generated paperwork, systems that monitor and check themselves. In one focused hour we evaluate your operation together, and you leave with a written, leadership-ready snapshot of where AI creates defensible value and where it does not -- whether or not we work further. Structured and run the same way every time, because a process you can see is a process you can trust.
The session points to whichever fits -- a structured assessment, a system built to spec, an ongoing technical partner, or training. Every path is scoped, reviewed, and managed to a defined outcome.
The fuller version of the session: where AI creates defensible value, the data-provenance and review-gate requirements, and what a managed build path looks like.
Design and build with traceability and evaluation in the architecture from the start. Delivered to defined acceptance criteria.
A technical partner who runs the practice on your behalf -- roadmap, oversight, and an evaluation cadence that keeps live systems accurate.
Hands-on training that gives your team the judgment to use AI safely in a quality-critical workflow -- where to trust it, where to gate it.
Good AI guidance requires two kinds of credibility, and most practitioners have one: engineering experience in regulated, mission-critical environments, and actually having shipped production AI in one of them -- not a case study you read, but a system you built, deployed, and are responsible for keeping accurate.
I built Verbatim so every answer traces back to its source because the domain demanded it. Version, hash, provenance, on every answer. That is not a feature. That is the minimum the work required.
The practice runs several AI systems in live environments right now. The best evidence that a method works is that it is running.
Descriptions follow the evidence on file. Nothing here is aspirational.
Grounded in deep technical operations experience where documentation and data accuracy are not optional.
Two decades engineering humidity and air control across food processing, pharmaceutical manufacturing, hospital operating rooms, lithium-battery dry rooms, and cold storage.
From the first paid hour to a commissioned system, the sequence is structured, reviewed, and managed -- the way a regulated build should run. You always know what comes next.
The paid hour. We map and evaluate your operation together; you leave with a written snapshot.
Opportunities evaluated against your constraints -- data quality, regulatory needs, review-gate requirements. A prioritized scope with acceptance criteria.
Design and build with traceability and evaluation in the architecture from the start, not added later.
Commission with documented baselines and a defined evaluation cadence. Someone is watching, systematically.
The scorecard and the systems get sharper with every run. The process compounds.
I am Mike Harvey -- BSEE, MBA, PMP, and founder of Harvey Consultancy LLC. I spent 20+ years engineering desiccant and hybrid desiccant-refrigeration systems in regulated and mission-critical operations: food processing, pharmaceutical manufacturing, hospital ORs, lithium-battery dry rooms, cold storage. That work taught me that a system which cannot prove its answer is not a working system. I brought that standard to AI.
I built Verbatim -- a live, compliance-grade RAG SaaS for Medicare/Medicaid billing -- not as a demonstration, but as a product with paying customers who depend on its accuracy. I also run a multi-agent AI content pipeline, a retrieval-augmented engineering knowledge system, an AI phone agent, and autonomous monitoring agents. I advise on AI the same way I built those systems: scoped, reviewed, traceable, and managed to a finish line.
Harvey Consultancy LLC -- Grand Island, NY. Clients in North America, Europe, Japan, Singapore, and Taiwan.
The State of AI Session gives you a written, leadership-ready picture of where AI creates defensible value in your operation -- and what a managed, traceable build would require. It starts with a free 15-minute fit check, and the $500 fee is credited toward any engagement you take forward.
Request a sessionmike.harvey@harveyconsultancy.com · harveyconsultancy.com
