Customer Experience · AI Integration · Operator & Builder
Here's the whole trick: get every part of a company — product, operations, engineering, even the cap table — pointing at the customer, then aim AI right there too. I've spent twenty years building customer experiences people genuinely love; since 2022 I've been doing it with AI, across identity, public safety, renewable energy, and healthcare. The receipts are real — CSAT, product and brand loyalty improved, product defects diminished, new products and innovation, increased revenue, shareholder value increased — and yes, I still build and ship the AI myself.
Currently
Founder & CEO
Self-healing AI for renewable-energy infrastructure — autonomous edge agents on every device in the fleet. Under an anchor project-finance partnership.
Founder & CEO
An MSP Group consolidating managed-service providers into a single operating company.
Advisor
Computer vision AI for security and public safety. US and Middle East expansion.
01 — The Work
Most executives do one of these. A few do two. Doing all three — building the product, operating the business, and structuring the capital — is rare, and it is exactly what capital-intensive, regulated, AI-driven companies need.
Build. I build companies from the ground up — corporate structure, customer relationships, and the code itself. I architect and personally ship the product, down to the autonomous edge agents that run on every device in the field. Most CEOs in regulated infrastructure cannot ship code. That changes the economics of building.
Operate. I have run the playbook on the other side — turnarounds at AuthenticID and Ford, MSP integration at Sunset, product turnarounds at Bright MLS and Henry Schein. Quantified outcomes, not aspirational ones: 25%+ EBITDA inside twelve months, 85% defect reduction, 71% technical debt elimination, $14M in net new recurring revenue. The work is documented.
Capitalize. I structure my own deals. QSBS-eligible entities, § 1045 rollovers, ITC/PTC transferability, multi-entity stacks designed for the exit math from day one. Boards and investors usually need three outside advisors to get this right. I bring it to the table.
02 — The Numbers
$98M
Largest division P&L managed
250+
People led across divisions
20+
Years building AI-driven platforms
85%
Defects eliminated at AuthenticID in five months
71%
Technical debt cut at Ford EMEA & India
20%
ARR growth via a top-5 US bank's IDV protocol
$14M
New recurring revenue added at Henry Schein
3
Healthcare MSPs unified into one operation
03 — Writing
The renewable industry has spent twenty years building assets and almost no time building the software to operate them. The existing players are dashboards. The next layer is agentic. Why this matters now, what changes for asset operators, and the three-year window before category consolidation closes.
Read essay →· 13 min read
Trust is the binding constraint on AI adoption in renewable operations, not capability. Asset operators have been burned by analytics vendors promising self-healing and delivering CSV exports. The unlock is edge-deployed agents that act locally and explain themselves.
Read essay →· 6 min read
Every utility-scale operator owns inverters from four to seven OEMs across a portfolio. Each speaks its own protocol, portal, and alarm taxonomy. The integration layer is where AI earns its keep — not in prediction, in translation.
Read essay →· 6 min read
04 — In the Open
The fastest way to know whether an operator can actually integrate AI is to run the AI he's built. So I publish mine — free, open-source, under the BlackRaptorAI mark. It is the same discipline I bring inside a company: AI trustworthy enough to hand someone, not a demo.
A local Model Context Protocol server that gives coding agents accurate, offline, deterministic access to library documentation — so the AI writing your software is working from the real docs, not a guess. Published on npm; runs in seconds.
View on npm →A governed multi-agent system — a full development team and executive council of AI agents that build and review software under real engineering controls: signed change records, gate reviews, and an adversarial challenge protocol. The way AI belongs in a serious company.
View on GitHub →