Built to run
Stuck in a pilot, sitting on AI tools nobody uses, or haven't started? The model was never the problem — shipping it into the real workflow is. I build your first production agents into your operation in 30 days.
MIT studied 300+ deployments and found the bottleneck is adoption and integration, not the model. Four failure modes separate a pilot that dies in the demo from one that survives a Tuesday in production.
A general assistant nobody's required to use. We embed the agent inside a job people already do.
"Explore AI" can't be defended at budget time. We tie every agent to hours saved or errors caught.
No evals, no human-in-the-loop on high-stakes steps. One bad output and trust is gone.
A science project on the side of someone's desk. We ship with a champion and a number on the board.
High-frequency, document-heavy workflows where an agent earns trust fast — and the P&L moves in weeks.
RAG over product specs, SKUs, pricing, and availability data. "What's the lead time / spec / stock status on X?" answered in seconds, not an email chain.
Saves hours/week of merchandising & customer-service lookups
Reviews incoming orders and quotes for wrong configs, pricing errors, missing fields — flags them before they ship and become rework.
Cuts costly downstream errors
Pulls from ERP + BI to draft the weekly ops review and flag exceptions — late jobs, margin slips, at-risk orders — so the meeting starts at the answer.
Saves a day of analyst prep
Handles "where's my order," tier-1 questions, routes the rest with context — off the CSR's plate, human-in-the-loop on anything sensitive.
Deflects routine ticket volume
Natural language over planning and inventory data. "What's at risk of stockout next month? What's overstocked?" — answered without waiting on a report.
Speeds planning decisionsThe full breakdown of which agents to build first, why these win trust fastest, and how to scope each one. Read it before you decide anything.
Read the playbook →Pick the workflow. Write the success metric before any building.
Wire the data, build the agent, test against real historical cases — not toy prompts.
Human-in-the-loop on high-stakes steps, evals on real cases, embedded in the tool people use.
Track adoption + the metric, fix what drags, then start agent #2. Now it's repeatable.
You don't need an "AI strategy." You need one agent live and used by Friday, a number on the board, then the next one. Start with a diagnostic; scale into a sprint and a standing agent-ops function.
Not a chatbot on the side of someone's desk. Production agents — embedded in the tools your team already uses, governed like workers, measured against one number. Champion program, prompt libraries, a weekly hours-saved dashboard to the sponsor.
Two weeks. Telemetry audit, top-10 use-case map, board-ready plan. Money-back — and credited in full if you upgrade.
30 days. Five workflows turned into live, governed agents. Champion program, prompt libraries, a weekly hours-saved dashboard to the sponsor.
The standing function. New agents, evals, monitoring, governance — and a monthly ROI report you forward to the board.
As VP of AI at Ashley Furniture, I greenlit pilots that died in the demo. Slick in the room, dead in production. The fix was never a better model — it was wiring the thing into the workflow people already use, so it survived contact with a Tuesday.
That's the whole game, and it's all I do now: pilot-to-production AI for consumer & retail companies your size — the ones too busy for a science project and too small for a Big-4 retainer.
Send me one workflow your team wishes ran itself. I'll build a working agent on it and screen-record the result, so you see exactly what "in production" looks like before deciding anything.