Brian D.

Most automation breaks the moment a system has no usable API.

I build AI employees that operate the real software your team already uses, and verify every step, so you know it worked instead of hoping.

Bring me the role you need covered: I map what it can own, what stays human, how fast it starts, and what it costs.

Why most AI projects stall

Plenty of companies have AI demos that look impressive and then fail on real users, messy data, edge cases, permissions, and the way work actually flows through a business. The gap is never the model. It is the integration, the verification, and the approval path.

So I build the unglamorous parts: the backend, the data flows, the evaluations, the audit trails, the human approval loops, and a success assertion on every automated step. A step that cannot prove it worked does not ship.

Recent proof

A recent technical assessment ran $6,250 and I delivered it in six days. The client wrote that the report "referenced specific code locations with clear remediation guidance."

Other systems I have built and run in production:

• A multi-agent system that audits trade confirmations for a mortgage capital markets firm
• Automations built directly from a client's written operating procedures, routing daily order reports between their credit office and warehouse
• A self-hosted company brain: an orchestrated agent connected to internal tools, with per-user memory isolation, audit logging, and hardened deployment
• Governance enforcement on LLM calls, aligned to ISO/IEC 42001 and the EU AI Act

Where I am the right call

• A person on your team repeats the same process every week and you want it covered
• The software involved has no usable API, so ordinary automation cannot reach it
• A mistake is expensive, so the automation has to prove each step rather than assume it
• You are not sure what should be automated yet, and you want that mapped before anything is built

Where I am not

If you need a chatbot prototype, a one-off script, or the cheapest available hands, there are better fits than me. I work on processes where getting it wrong costs more than getting it built.