The GTM Engineer bridge.
Norm Ai closed the $50M Series C with Blackstone in November, and the RevOps Manager seat is open. The pattern I see: COO absorbs the GTM routing and scoring work until the hire lands, which at regulated-vertical AI scale compounds fast. Concretely tied to my AssetWatch delivery model (predictive-maintenance SaaS, leadership-facing SFDC research agent, 4 weeks): ship the system, document it, hand it back. I run the GTME function in parallel so your incoming hire walks into a working system.
The RevOps gap at a Series C compliance company is an operating-cadence problem.
When RevOps is open, every SFDC routing patch, every stage-definition drift, and every scoring exception lands on the COO. That is forecast calls run on inconsistent inputs, QBRs that debate methodology instead of strategy, and an AE team that hedges because they do not trust the pipeline view. The fix is the multi-buyer routing and stage taxonomy underneath SFDC, built before the RevOps hire ramps. I do that build in 4 weeks, the same way I shipped the AssetWatch leadership SFDC research agent.
Three things only an internal builder can fix.
Forecast inputs drift across reps
Without enforced stage definitions for a CCO, Legal, and RevOps committee deal, what one rep calls Stage 3 and what the dashboard calls Stage 3 are different things. The forecast you take to the board is a weighted average of inconsistent inputs.
Manual routing breaks at Series C volume
Blackstone-backed deal flow accelerates fast. Routing rules patched in Slack and tribal knowledge in AE heads do not survive a tripled pipeline. The system you have today is the ceiling on next quarter.
RevOps ramps for 2 quarters after they land
Even the right hire needs 90 days to learn the regulated-vertical motion and another 90 to ship the first real build. The next 2 board cycles run on the system that exists, or the system someone builds while they ramp.
A working RevOps infrastructure layer your incoming hire inherits on day 1.
- Weeks 1 to 2
Audit the SFDC stage model against committee-deal reality
Map how compliance committee deals actually move vs. how the current stage model assumes they do. Identify the 3 to 5 stage drifts and routing breaks costing forecast confidence today. Same audit discipline I ran at AssetWatch on 26 SFDC objects and 3,800+ fields.
- Weeks 3 to 4
Ship multi-buyer routing, scoring, and sequencing
Distinct stage models per committee lane, enforced stage definitions, automated handoffs, and sequencing tuned to compliance-buyer signals. RevOps Manager starts day 1 reading from a system, not a Slack channel.
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.