AI implementation · the lead practice

Your AI pilot is
still a pilot.

~95% of enterprise AI pilots never reach the P&L. The model was never the problem — shipping it into the workflow on the floor is. We build five production agents into your operation in 30 days.

Book a 15-min call → See what we ship
30 daysto production, not a roadmap
$250Mmanufacturer — where the operator shipped it
Fixed feeguaranteed live, or you don't pay
supplier-docs.agent● LIVE
› lead time on SKU-4471?
14 days — confirmed against PO #88231 and the September supplier cert. 2 sources
41.2hrs
saved this week
~95%
of enterprise GenAI pilots deliver no measurable P&L impact — adoption and integration, not the model (MIT, 300+ deployments).
30 days
scope → build → ship → measure. Out of pilot in four weeks, not another roadmap.
40%
final payment held back — you don't pay it unless the agents are live and in use by day 30.
The deployment gap

Why the other 90% stall — and the 10% don't.

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 on the floor.

01 / WORKFLOW

It's a chatbot, not a workflow

A general assistant nobody's required to use. We embed the agent inside a job people already do.

02 / METRIC

No success metric

"Explore AI" can't be defended at budget time. We tie every agent to hours saved or errors caught.

03 / READINESS

No production-readiness

No evals, no human-in-the-loop on high-stakes steps. One bad output and trust is gone.

04 / OWNER

No owner

A science project on the side of someone's desk. We ship with a champion and a number on the board.

Built to run

Where the work actually happens.

What we ship

Five agents worth building first.

High-frequency, document-heavy workflows where an agent earns trust fast — and the P&L moves in weeks.

supplier docs
Agent 01

Supplier-doc intelligence

RAG over specs, POs, certs, datasheets. "What's the lead time / spec / compliance status on X?" answered in seconds, not an email chain.

Saves hours/week of purchasing & engineering lookups
order hygiene
Agent 02

Order & quote hygiene

Reviews incoming orders and quotes for wrong configs, pricing errors, missing fields — flags them before they hit the floor and become rework.

Cuts costly downstream errors
ops review
Agent 03

Ops & QBR prep

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
service triage
Agent 04

Order-status & service triage

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
demand Q&A
Agent 05

Demand & inventory Q&A

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 decisions
the playbook
Free playbook

The first 5 agents

The 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 →
The 30-day path out of pilot

Pilot to production in four weeks.

WEEK 1 / SCOPE

Scope

Pick the workflow. Write the success metric before any building.

WEEK 2 / BUILD

Build

Wire the data, build the agent, test against real historical cases — not toy prompts.

WEEK 3 / SHIP

Ship

Human-in-the-loop on high-stakes steps, evals on real cases, embedded in the tool people use.

WEEK 4 / MEASURE

Measure

Track adoption + the metric, fix what drags, then start agent #2. Now it's repeatable.

The engagement

Priced on the outcome, not the hours.

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.

Pilot → Production · 30 days

Five workflows, live and governed.

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.

The guarantee: five agents live and in use by day 30 — or you don't pay the final 40%.
Start a Sprint →
What you get — 5 production agents
01Supplier-doc lookup — specs, POs, certs, lead times in seconds
02Order & quote hygiene — catch wrong configs before they hit the floor
03Ops-report prep — the weekly review drafted, exceptions flagged
04Order-status & service triage — off the CSR's plate, human-in-loop
05Demand & inventory Q&A — what's at risk of stockout, answered
Start small

Adoption Diagnostic

$4,500

Two weeks. Telemetry audit, top-10 use-case map, board-ready plan. Money-back — and credited in full if you upgrade.

Most popular

Pilot-to-Production Sprint

$25–45K

30 days. Five workflows turned into live, governed agents. Champion program, prompt libraries, a weekly hours-saved dashboard to the sponsor.

Keep it running

Agent-Ops Retainer

$3–8K/mo

The standing function. New agents, evals, monitoring, governance — and a monthly ROI report you forward to the board.

Why me

The operator stuck at 95% — then I built the way out.

As VP of AI at a $250M furniture manufacturer, I greenlit pilots that died in the demo. Slick in the room, dead in the plant. 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 manufacturers your size — the ones too busy for a science project and too small for a Big-4 retainer.

Book a 15-min call →
operator on the floor
匠
Start here

See it on your own workflow — free.

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 "out of pilot" looks like before deciding anything.

Book a 15-min call → Read the playbook