The GTM Engineer bridge.
MaintainX opened an AI GTM Engineer role for Growth Marketing on May 4th. The JD is honest about the gap: an AI-native, system-driven engine that treats industrial buyers like the distinct lanes they are. Until that hire lands and ships, your stack converts at a blended rate that hides the real performance. I run the GTME function while you hire.
Your stack performs at the average of three buyer types it cannot tell apart.
Paid, lifecycle, and content are all tuned to a blended industrial buyer that does not exist. Plant managers respond to uptime proof. Reliability engineers respond to data depth. Ops directors respond to ROI math. When the engine cannot route them, every channel posts a middle number that under-rewards your strongest motion and over-rewards your weakest. The AI GTME hire fixes this. I run the build now so your Q3 numbers reflect the real play.
Three things only an internal builder can fix.
Paid creative is averaged, not tuned
One ad set tested against three personas reports a blended CTR that hides which message actually lands for which buyer. You optimize toward the average and dampen the strongest signal.
Lifecycle nurtures on the wrong proof
A plant manager who downloaded an uptime guide gets the same drip as an ops director who downloaded an ROI calculator. Open rates look fine. Pipeline contribution is not.
Account scoring does not encode lane
Your scoring model rolls up firmographics and intent but cannot weight by buyer type. The accounts MQLing today are the easiest to reach, not the most likely to close.
A buyer-lane overlay that makes your existing stack stop averaging.
- Weeks 1 to 2
Audit Growth stack against the JD vision
Paid, lifecycle, content, scoring reviewed against the AI-native, system-driven engine described in the role. Per-lane performance estimated where data allows. Wins ranked, gaps mapped.
- Weeks 3 to 4
Ship the persona-routing acquisition layer
Plant managers, reliability engineers, and ops directors hitting the same campaign route to distinct landing pages, distinct nurture tracks, and distinct scoring. Your existing stack starts reporting per lane instead of in aggregate.
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 MaintainX. 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 your channel numbers feel averaged.
Send me a sentence on which channel you suspect is hiding its real performance. I will reply within a day with a 1-page scope and an honest read on whether this fits.