Nothing reaches a buyer without approval
A named person on your team approves what buyers see.
Your team leaves running one play at your costliest buyer decision. Most GTM work is measured in meetings booked. We measure the buyer's decision, against accounts that didn't get the play.
Where your buyers stall, what it costs you in revenue, and what the buyers who did advance had in common, from your own data.
Picked with you in week 2, with a hypothesis your CFO could check: if we do this for these buyers, this number moves by this much, against accounts that didn't get the play.
Live in your team's AI workspace and the tools they already work in, rebuilt with them through build week until they approve what it produces.
What the play should move and the accounts it's compared against, agreed before launch, on one page you can take to your next QBR. It's judged on two things: did the number move, and does your team use it.
The play, its tests, the data it reads and a runbook, all in your accounts. We train the person who'll own it.
The next buyer decisions, ranked by the revenue at stake, and one proposed next step if the play earns it.
A named person on your team approves what buyers see.
If you stop after four weeks, you keep the diagnosis, the data model, the skills and everything we built, in your accounts.
If AI isn't the right fix for your bottleneck, the diagnosis says so.
What the play should move, and what it's compared against, is written down before launch.
If you continue, it's credited toward Implementation.
It's built on your own deals and calls. Every signal is checked on past deals it wasn't built from, the people who'll use the play build it with us, and we measure the lift against accounts that didn't get it.
The diagnosis in week 1, the decision and the prediction in week 2, and a working play in your team's hands by week 4. The revenue number follows in the weeks after.
Four of your people: a sponsor, the person who'll run the play, someone to review what it writes, and whoever handles security. Read-only access and two sessions a week, then daily in build week. We do the building, and there's nothing new to log into.
$5,000 for the first five teams, then $10,000, for all four weeks. The $5,000 is credited toward Implementation if you continue, and everything we build is yours.
We get read-only access, map the buyer journey from your data, find where buyers stall and check the baseline.
Together we pick the decision, write the revenue hypothesis and agree the metric and the comparison accounts.
A focused build of two to five days, with daily sessions alongside the people who'll use it. Each day we ask: What context does it not have? What outputs do you disagree with? We rebuild until they approve what it produces.
Your team gets the play, its tests and a runbook, in your accounts. Your owner shows they can run it and stop it without our logins, and you get the first early numbers. Our process
Scott has spent years giving product advice to GTM software vendors, so he knows how the tools under your play work. He's in Nashville and takes on GTM teams only.
AboutSits in the build-week sessions with your people, with coding agents handling configuration and first drafts.
Only what your first play needs: the GTM data warehouse tables it reads, the diagnosis from AI reporting and analysis, the argument from GTM strategy, custom signals back-tested on your past deals, the skills your team's AI follows, and an AI workspace for the people running it.
| Length | Price | What you own | After |
|---|---|---|---|
| 4 weeks: Diagnose, Choose, Build with your users, Handoff | $5,000 for the first five teams, then $10,000. Credited toward Implementation if you continue. | Everything we build: data model, code, skills, prompts | One next step, proposed at handoff from what the play showed, often Implementation. Or your team runs it from the runbook. |
That the play works for your team on your data, and what we predict it moves. We write the prediction down before launch, and the revenue number follows in weeks.
Then you'll know early, and you'll know why. The prediction is in writing before launch, so nobody argues afterwards about what happened. If AI isn't the right fix for your bottleneck, the diagnosis says so. Either way you keep the diagnosis, the data model, the skills and everything we built.
No. We connect what the play needs during the bootcamp, and exports are fine. 10 to 30 deals that did and didn't advance are enough to map the moment. Signal back-tests use your full history where it exists.
Yes. Senior people make the calls and coding agents do the configuration and first drafts, so a team of two ships in weeks. We limit how many bootcamps run at once.
We'll walk your security team through access before kickoff.
Then you'll get the most from the diagnosis: which decision to work on, and whether your agents are moving it, read against a comparison. For GTM engineering teams
Thirty minutes. Bring the number you're missing, and we'll tell you what we'd look at in week one.
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