AI Ventures · Applied, not theoretical

I don't advise on AI from slides. I build it, test it and measure it against real operating problems.

Three ventures, each started from an operational problem I could state in one sentence. Below: the problem, the product, the evidence so far, and the next responsible step.

From enterprise foundation to applied AI.

Where that foundation was built

Six conditions decide whether an AI initiative survives contact with an operating business — and every one of them is enterprise-systems work, not model work.

Governed data Secure system access Reliable integration Human accountability Measurable outcomes Adoption in the workflow

Experience proves

Enterprise architecture and delivery judgment — security models, integration estates and release governance at Fortune 500 scale.

AI Ventures proves

Practical experimentation — building, evaluating and stress-testing AI workflows against problems real people actually have.

Together

An advisor who can look at the systems you run today and tell you honestly where AI belongs next — and where it does not.

Venture dossiers

Problem · product · evidence · next step

ScanAssist · Pilot

ScanAssist

digital product passport

Languages

11

Channel

SMS / Twilio

Stage

Manufacturer pilots

Problem

Paper manuals get lost; the helpdesk answers the same questions all day.

Product

Scan the QR, text a question, get an answer drawn from that product’s own manual — low confidence escalates to a human.

Evidence

End-to-end path working in 11 languages against a fixed question set. No deflection rate published yet.

Next step

One manufacturer, one product family, call volume measured before and after.

Talk about a pilot
RepFlow · In build

RepFlow

field capture to clean record

Surface

Mobile-first

Pattern

API-first

Stage

Pre-pilot

Problem

Field work arrives as photos and voicemail, then someone re-types it a day later.

Product

Capture naturally on site; the model structures and routes it; a named human confirms before it commits.

Evidence

In build. No adoption or accuracy data yet — stated plainly rather than dressed up.

Next step

One operator, one workflow: re-keying time removed against errors introduced.

Ask about RepFlow
AreaIQ · Live report

AreaIQ

a place report you can check

Cities scored

17

Coverage

Any U.S. ZIP

Output

Six-page PDF

Problem

Place decisions run on marketing and opaque scores that cannot be audited.

Product

Public data scored per dimension and re-weighted around the reader — every claim carries its source and a confidence band.

Evidence

A full Austin-metro report is generated and viewable, 17 cities scored.

Next step

More metros, with source-quality flags per region.

Open the Austin report

Build log

Working artefacts from the three ventures — what was actually built, what it runs on, and how far along it is. Each entry links to the thing itself where there is something to open.

ScanAssist product passport walkthrough

ScanAssist · Manufacturer pilots

Manufacturer onboarding kit

Onboarding flow that turns a manufacturer’s PDF manuals and ticket history into a live SMS support agent — ingestion, evaluation set, QR label pack and escalation rules.

n8n · Twilio · Vector search · Claude

RepFlow · Demo

RepFlow walkthrough

Two-minute walkthrough: a rep checks in with GPS, logs the visit outcome by typing or by WhatsApp voice, and the daily call report writes itself — no end-of-day paperwork.

Mobile · Intake · AI assist

Advisory

Planning where AI should — and should not — enter your HR operating model? Let's map the next move.

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