Four steps, run on revenue impact
Every engagement runs the same loop. We pick the work by what it's worth to your number, and we don't stop at setup.
The loop
- Step 011
Connect your data
Every touch and outcome in one place you own.
- Step 022
Prioritize by revenue impact
The decision losing the most revenue, sized, with a prediction written first.
- Step 033
Ship into the tools your team uses
A play your team runs in its own tools. A named person approves what buyers see.
- Step 044
Keep improving
The lift on each play, then keep, change or retire it. The next decision is already queued.
Most GTM engineering stops at shipping
The work that decides whether a play moves revenue is everything before it, and the read after it. AI moves revenue when it improves the buyer's experience. So steps two and four get the most care.
How we decide what to build
- Start with the tool they sell
- Build what you asked for
- Hand off at go-live
- Start with where revenue is lost
- Build what's worth the most to this quarter's number
- Stay until it's measured, then move to the next costliest decision
All your GTM data in one place you own
Your AI can only be as good as what it can see. We pull your CRM, sequencer, dialer, calls, website and product data into a warehouse you own, with each stage and metric defined the way your best operator would. We start read-only, with what the first play needs. This is the base of your GTM brain.
Built here: GTM data warehouse · Agentic tool implementation
What we pull in week one
Illustrative. Companies, people and numbers are fictional. Northwind's first play targets new VPs of Operations before peak season.
| Source | What we pull | Access | First play needs it? |
|---|---|---|---|
| CRM | Accounts, contacts, deals and every stage change | Read-only API | Yes |
| Sequencer | Enrollments, replies and why each sequence ended | Read-only export | Yes |
| Call recorder | 25 won and lost first calls | Read-only | Yes |
| Website | Pricing-page visits by account | Read-only | Yes |
Work on the thing worth the most to your number
We look at your funnel the way a lean operator looks at a factory floor: where does work pile up, where do good leads go to die, where is your best rep doing something nobody else does? We find the stage where the most revenue is lost, line up the accounts that advanced against the ones that stalled, and test which signals came first. Then we write one sentence a CFO could check: "If we do this for these buyers, this number moves by this much, against this comparison."
The test: if it doesn't change which buyers you focus on and what they see, it doesn't matter.
Built here: AI reporting and analysis · GTM strategy · Custom signals and intent
Where Northwind loses revenue
The loss tree. Illustrative. Companies, people and numbers are fictional.
| Stage | Converts now | In plan | Revenue lost a quarter |
|---|---|---|---|
| Target account → first meeting | 1.4 per 100 | 2.4 per 100 | $1.1M, the stage that binds |
| First meeting → opportunity | 38% | 40% | $180K |
| Proposal → closed won | 28% | 27% | None |
Which signals held up
Each signal is checked on past deals, then on accounts the analysis never saw. Illustrative.
| Signal | Meeting rate vs baseline on past deals | Held up on unseen accounts? |
|---|---|---|
| New VP Operations in the last 90 days | 3.1× | Yes |
| 2+ pricing-page visits in a week | 2.4× | Yes |
| Third-party intent topic | 1.1× | No |
The fix shows up where your team already works
The play goes into Claude, ChatGPT, Slack, and your CRM, sequencer and dialer. Agents read before they write, and every agent action is logged. Working versions arrive in days, rebuilt with your users until they work for them.
Built here: Skills · Agents · Automated workflows · AI workspace · Custom apps and MCPs
Approvals go where the risk is
A named person approves what a buyer sees. If approving takes longer than writing, people stop approving.
| Reach | Who approves what |
|---|---|
| One to one | The rep approves the account argument and every first touch. |
| One to few | One approval per cluster, plus a spot check of rendered drafts. |
| One to many | The argument once, checks on every row, and failures wait for a person. |
| Always one by one | Anything in an executive's voice, a first touch to a tier-1 account, a new claim, and anything that spends money. |
Go-live is the start, not the finish
A play has to pass two tests: the number went up, and your reps actually use it. We measure the lift at the buyer's decision, against accounts that didn't get the play. Accounts are split at random, the same reps work both halves, and the read covers the same weeks. Then we keep, change or retire the play.
Built here: GTM project management · AI telemetry (in progress) · AI self-improvement (in progress)
Three levels we could count
Only the last one is a result. Illustrative. Companies, people and numbers are fictional.
| Level | Example | What it tells you |
|---|---|---|
| Task done | 1,300 accounts researched, 1,300 emails drafted | The play ran |
| Reached the buyer | 1,190 emails delivered, 134 replies, 20 calls connected | The buyer saw it |
| Buyer decided | 42 meetings held, against 18 on the 1,300 accounts held back | What the buyer did |
What a play readout looks like
Illustrative. Companies, people and numbers are fictional.
| Play | Got the play | Comparison | Decision |
|---|---|---|---|
| New VP Operations email | 42 meetings on 1,300 accounts | 18 on 1,300 | Keep. The gap clears chance and held for 8 weeks. |
| Pricing-page follow-up | 4 meetings on 90 accounts | 3 on 85 | Inconclusive. Read again in 6 weeks. |
| Webinar nurture | 5 meetings on 400 accounts | 5 on 410 | Retire. |
GTM AI Bootcamp
The bootcamp runs this loop once, on one decision, in 4 weeks: Diagnose, Choose, Build with your users, Handoff.
Before you book
Do we have to do all four steps?
Yes, at the size of one decision. Step one connects only what that play needs, on the warehouse you have if there is one.
You're two people. How do you keep up?
Senior people make the calls, and coding agents do the configuration and first drafts. We take on a small number of teams at once.
What comes after the bootcamp?
Implementation takes the play to production in 12 weeks. The AI operating partnership runs the loop with your team for 6+ months.
Run the loop on your costliest decision
30 minutes. We'll tell you which decision we'd start with and what we'd look at first.
Book a call