Forward Deployed AI Engineering

Everyone has the model. Almost no one has it in production.

I embed with your team, audit one real workflow, build an agent for it, and prove it against your own historical data before anything touches production. It runs on the systems you already have. No migration. No slideware.

~33%
of enterprise AI pilots ever reach production. The gap isn't the model. It's everything around it.
01Method

Three phases. Each one earns the next.

Most pilots die because somebody built the demo first and went looking for evidence later. This runs in the other order. Each phase has to prove itself before I'll sell you the next one.

01
Audit

Shadow the real work

The documented process and the one people actually run are never the same thing. I follow the real one, including the exceptions nobody wrote down, and map where the time and the errors actually go.

02
Evals

Score it before you trust it

A golden dataset built from your own history, with the right answers hand-labeled. The agent gets graded against it: pass rate, and every failure mode worth naming. You see the number before you have to trust anything.

03
Deployment

Ship on the stack you have

It goes live in a sandbox first, wired into the tools your team already opens every day. Autonomy expands when the numbers say it can. Nothing else moves it.

02Focus

No case studies yet. I'm not going to invent any.

Disclosure — Sketchplay is mine. It's the first client because it's the one I can hold to the standard.

The first builds go into youth sports operations through Sketchplay, the league and club management company I own: registration, scheduling, and support, the work clubs, leagues, and tournament operators run every season. Real data, real deadlines, and nobody to blame but me.

Youth sports operations are dense, judgment-heavy, and run by small teams with no engineers of their own. A season has real money and real parents in it. Get an agent working under those conditions and the method carries into any business built on the same kind of operational complexity.

Inbound that repeats
Club and league admins answer the same questions every season, which leaves a pile of historical tickets with known right answers to evaluate against.
Scheduling under constraints
Facilities, age groups, blackout dates, coach availability. Season scheduling is a constraint problem that most software treats like a data entry screen.
Communication load
Getting the right message to the right parents and coaches, at the volume a season actually generates, without a phone tree or a mass blast nobody reads.

The vertical is where I'm starting. It isn't the business.

03Engagement

Fixed fees, one phase at a time.

Each tier is one phase of the method above, bought on its own. You can stop after any one of them. Some engagements should.

Engagement
What you get
Entry
01
Audit Sprint
Fixed fee · 1–2 weeks
One workflow, shadowed end to end. You get an operating map: what happens today, where it actually breaks, and a straight answer on whether an agent belongs in it. Sometimes the answer is a better form and no agent at all.
Every engagement starts here
02
Pilot Build
Fixed fee · 6–10 weeks
A working agent, live in a sandbox, graded against a golden dataset built from your own historical examples. You see the pass rate and the failure modes before anything gets pointed at production.
Scoped after the audit
03
Deployment Retainer
Monthly · ongoing
Once the pilot has earned it, the agent goes live and I keep it that way: monitoring, drift, cost, accuracy, and the next workflow after this one.
Scoped after the pilot
No retainer before a pilot. No pilot before an audit.
Start Here

Name one workflow that's slower or shakier than it should be.

I'll tell you whether an agent belongs there/I read these myself — no funnel, no SDR/If it's not a fit, you'll hear that in the first reply/Answered within one business day
or write to lance@swevendigital.com