The GTM Engineer bridge.
Norm Ai closed the $50M Series C with Blackstone in November. The pattern I see at regulated-vertical AI companies at this stage: enterprise compliance deals get closed while the SFDC routing layer is held together manually. Concretely tied to my AssetWatch delivery model (predictive-maintenance SaaS, leadership-facing SFDC research agent, 4 weeks): ship the system, document the maintenance, hand it back. I run the GTM Engineer function while you hire so the next 2 quarters of pipeline do not depend on the RevOps seat filling.
Regulated-vertical enterprise pipeline lives or dies in the SFDC committee routing.
Compliance deals at Norm Ai's stage are committee deals. CCO, Legal, and RevOps each enter the cycle on different signals and need different proofs. If SFDC routes them all as one lead and the stage taxonomy does not distinguish the lanes, the AE writes to the lowest common denominator and the cycle stalls in legal review. The fix is the multi-buyer routing and stage model underneath, which is what your RevOps hire will eventually own. I build it now so your board update reads from pipeline that actually moves.
Three things only an internal builder can fix.
Committee buyers flatten into 1 lead record
When CCO, Legal, and RevOps come in on the same opportunity, SFDC usually treats them as one buyer. The AE writes a generic deck and the cycle stalls in whichever lane was undersupported.
Routing built by hand does not survive enterprise volume
At Series C with Blackstone, the deal flow accelerates fast. Manual routing rules patched in Slack do not hold once the pipeline triples. The system you have today is the ceiling on next quarter.
The RevOps hire is 2 quarters from impact
Sourcing, hiring, ramping, and shipping the first real build takes 6 months at this scope. Board confidence in the Series C number cannot wait that long without a system underneath it.
A committee-aware SFDC routing layer your RevOps hire inherits, not rebuilds.
- Weeks 1 to 2
Map Norm Ai compliance-buyer signals to SFDC stage model
Audit how CCO, Legal, and RevOps currently move through the pipeline. Identify where the lanes flatten and where deals stall in handoffs. Same audit discipline I ran at AssetWatch when mapping how 26 SFDC objects and 3,800+ fields actually behave in production.
- Weeks 3 to 4
Ship multi-buyer routing, scoring, and sequencing
Distinct stage models per committee lane, automated handoff triggers, and sequencing that fires on the right signal for the right buyer. Your incoming RevOps Manager starts day 1 with a working system.
Salesforce in plain English, shipped in 4 weeks.
AssetWatch leadership wanted natural-language access to pipeline, accounts, demo outcomes, and work orders without filing a RevOps ticket for every question. I shipped a custom GPT in ChatGPT Enterprise that translates English to SOQL and queries production Salesforce live. Two Knowledge files made it work: an auto-generated schema catalog covering 26 objects and 3,800+ fields, plus a hand-curated semantic layer encoding AssetWatch tribal knowledge, so "who owns this deal" returns the Solution Architect and "deal size" returns ARR, not the raw admin fields. Read-only, leadership-facing, 4 weeks. Tyler's team owns the maintenance now.
Same play I would run for Norm Ai. 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.