What you've been told about your AI and what's actually true are not the same thing.
Independent scientific review of AI in the enterprise — from executive briefings to line-by-line system audits. Whatever depth the situation calls for, the job is the same: close the distance between what your organization believes and what is real.
The same gap, at three depths.
There is a distance between what an organization believes about its AI and what is true. It shows up at every level — and closing it is the work.
Your executives
…are making decisions from a picture of AI assembled by the people selling it. Vendor decks and press coverage are not a working model of the technology.
Your teams
…are using tools without a working model of what those tools actually do — so they over-trust them in some places and under-use them in others.
Your systems
…may be silently wrong in ways standard QA does not catch, because catching them requires reasoning about the behavior of something you cannot inspect line by line.
Down into the mess, then up into a category.
The same sequence closes the gap at any depth. The move that matters is the last one: taking a specific, tangled problem and lifting it into a class of system whose properties are already known.
Trace the actual chain of events.
Find the step that is the bottleneck.
Lift it into a general class of system.
Fix it using what's already known about that class.
Three depths, one judgment.
The unifying idea is verification against evidence — auditing the distance between what you've been told and what is true. That covers a room of executives and a production codebase alike. Start wherever the question is.
Briefings & speaking
What the state of the art actually is, what it does, and what it means for this organization — delivered so an executive team can act on it. Includes conference and internal-event speaking.
- conference keynotes
- executive briefings
- internal events
Advisory
AI strategy, architecture review, and build-vs-buy — plus helping teams form an accurate working model of the tools they're already using, so they trust them exactly as far as the tools deserve.
- AI strategy
- architecture review
- build vs. buy
- team working model
Audit
Independent review, delivered as written findings. What gets reviewed is not always code. Name the object and I'll review it against the evidence.
- a vendor's claims
- a proposed AI roadmap
- a team's working model
- the production system
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I'm a scientist. Scientific training is training in a specific thing: taking a system you didn't build, that nobody can fully inspect, and determining what it is actually doing rather than what it was meant to do.
That is exactly the problem an enterprise AI system presents. I come at it from the outside, with no stake in the answer being reassuring. Two things that buys, which a delivery vendor cannot claim: classification — putting a system into a category whose behavior is already understood — and independence, which is the whole premise of an audit.
Prior enterprise delivery at Ford and CSX via Trace3.
Author; work published on arXiv.
The same method, as tools.
Notes2Tree turns raw material — notes, documents, transcripts — into a hierarchy you can navigate. Dekyon layers that structure onto a body of content so it can be learned from. Not a product line; the practice, compiled.