The GTM Engineer bridge.
WisdomAI closed $50M (Kleiner + Nvidia, November 2025), and you landed into a blank HubSpot 6 months in. Enrichment pipeline, ICP scoring, routing logic, and stage taxonomy all need to exist before the layer you were hired to design has anything to design against. I run the GTM Engineer function in parallel so your first 30 days are spent on strategy, not on standing up plumbing that should have shipped before you started.
RevOps strategy lives on top of plumbing that is not yet built.
First RevOps hire at a 33-person AI startup is a familiar pattern. The job description says strategy, forecasting, planning. The reality of the first 90 days is data cleanup, vendor evals, HubSpot field hygiene, and figuring out which AE updated which deal stage based on which definition. That is not a Head of RevOps problem. That is a GTM Engineering problem that should have been solved before you got the offer. I run that build now so your first board update reads from a system you trust, not a spreadsheet you reconciled the night before.
Three things only an internal builder can fix.
Stage definitions drift the moment you have more than 2 AEs
Without enforced taxonomy in HubSpot, what one rep calls Stage 3 and what your dashboard calls Stage 3 are different things. Forecast variance becomes a methodology fight instead of a coverage conversation.
Enrichment + scoring is a 4-week build, not a side project
The signal layer your AEs prospect against (technographic + hiring + funding triggers) needs full-time focus to ship right. Doing it while also running pipeline reviews and building the first plan means it ships in 6 months instead of 4 weeks.
False-positive noise kills AE trust early
If the first signals you push into HubSpot are 30% wrong, AEs stop reading the alerts within 2 weeks. The evidence chain has to ship with the signal layer, not after.
A 6-signal HubSpot layer with evidence chain and deal-stage routing.
- Weeks 1 to 2
Audit + ship 6 enrichment signals wired to HubSpot account records
Crustdata + technographic + hiring + news + funding + headcount inflection, deduped through a 4-tier evidence chain. 37% false-positive catch rate on the Daylit pilot. AEs trust the signal because the system shows its work.
- Weeks 3 to 4
Wire signals to deal-stage routing + Slack alerts
High-fidelity signals auto-route to the right AE, update deal stages, and ping Slack with why-now context. You inherit a working routing layer on day 31 instead of an empty CRM you spent 90 days filling.
Six production signals, shipped in 2 weeks.
Daylit closed Series A and needed an AE-ready territory before the first NA hire ramped. I built the ICP signal layer. Six buying signals piped from raw data sources (theirstack, Crustdata, news APIs) through Anthropic evidence-chain classifiers into HubSpot, with Slack alerts on high-fidelity hits. The first AE walked into a defined territory, not a cold start. 2 weeks. Same fixed-fee discipline.
Same play I would run for WisdomAI. Different stack, same fixed-fee discipline.
$15,000, fixed. 6 weeks. One invoice.
- Signal architecture
- Account list and buying-committee map
- Sequence build, live send, and deliverability infrastructure
Documentation and handoff included, not billed. If volume justifies it after the bridge, $25,000 / 90-day retainer extends the system. Your call, not mine.
Reply if this maps to where you are.
Send me a sentence on how the pipeline reads today, and I will reply within a day with a 1-page scope and an honest read on whether this fits.