The GTM Engineer bridge.
LlamaIndex closed $19M Series A in March 2025. You hold both sales and ops with no GTM engineer underneath, which means enrichment, routing, and stage hygiene compete for the same hours you owe to closing. I run the GTM Engineer function while you hire permanently so the forecast you take to Jerry stops drifting on plumbing nobody owns.
Revenue ops debt at Series A compounds faster than the sales team can outrun it.
When LlamaCloud and LlamaParse usage signals do not write to HubSpot, AEs prospect on firmographics while the real expansion candidates sit unrouted. Add stage definitions that drift across reps and inbound triage that runs on intuition, and the forecast becomes a weekly reconciliation exercise. For a VP Revenue carrying both seats, every hour spent on that work is an hour off pipeline. The fix is the signal and routing layer underneath HubSpot. That is what your permanent GTME hire will own. I run it now so the system catches up to the motion.
Three things only an internal builder can fix.
Enrichment lives in product analytics, not the CRM
Enterprise teams hitting LlamaCloud usage thresholds are the highest-intent signal LlamaIndex has. If reps cannot see those events on the account record, the pipeline they build is a fraction of the pipeline that exists.
Routing on firmographics caps conversion
Inbound triaged on company size and industry alone misses the buyer behavior that actually predicts close. An evidence-chain ICP classifier scoring on usage + firmographics would lift conversion before any new rep ramps.
The GTME hire is 6 months from impact
Sourcing plus ramp plus first shipped build is 2 quarters. Until then, the routing and enrichment layer sits on your desk while you are also running the quarter.
A HubSpot enrichment + evidence-chain lead scorer that fires before pipeline hits the AE.
- Weeks 1 to 2
Audit the gap between LlamaCloud usage and HubSpot pipeline
Map every product signal that should move an account stage or trigger a sequence and currently does not. Identify the 4 to 6 deltas that would have caught last quarter's expansion candidates before close cycles flipped.
- Weeks 3 to 4
Ship the enrichment + scoring layer with Slack alerts
Product usage auto-writes to HubSpot. A 4-tier evidence-chain ICP classifier pre-qualifies inbound and outbound. High-fidelity hits ping the right AE in Slack. Same play I shipped for Daylit in 2 weeks, tuned for enterprise AI platform buyers.
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 LlamaIndex. 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.