The GTM Engineer bridge.
LlamaIndex closed $19M Series A in March 2025. You sell multi-signal agent pipelines to enterprise AI teams. The GTM layer underneath your own funnel does not yet run on that thesis, which is the loudest signal your buyers will read. I run the GTM Engineer function while you hire permanently so the system selling LlamaIndex looks like the system LlamaIndex sells.
The pipeline that closes enterprise AI deals should be built on the product you sell to enterprise AI teams.
At 44 people with no dedicated GTM engineer, LlamaCloud and LlamaParse usage signals likely never reach HubSpot as routable pipeline. That means your highest-fidelity intent data, actual product behavior from enterprise teams, sits unused while reps prospect on firmographics. For a CEO whose category is agent data infra, that gap is a credibility tax on every enterprise call. The fix is the signal layer underneath HubSpot, which is what your permanent GTME hire will own. I build it now so the demo your reps walk into is the demo your product enables.
Three things only an internal builder can fix.
Product usage never reaches the CRM
Enterprise teams hitting LlamaCloud and LlamaParse thresholds are the highest-intent signal LlamaIndex has. If those events do not write to HubSpot account records, reps prospect blind while real expansion candidates sit in product analytics.
Founder-led pipeline does not transfer to ramp
The intuition you and the team use to qualify enterprise interest does not document itself. Until the signal layer is codified, every new AE rebuilds your judgment over 6 months on $75K+ ACV cycles.
The GTME hire is 2 quarters from impact
Sourcing plus ramp plus first shipped build is realistically 6 months. That is 2 quarters of enterprise pipeline running on the system you have today, not the system the FTE will eventually build.
A HubSpot signal layer that turns LlamaCloud usage into routable enterprise pipeline.
- Weeks 1 to 2
Map LlamaCloud and LlamaParse events against HubSpot account stages
Audit which product signals fire automatic stage changes vs. which ones die in analytics dashboards. Identify the 4 to 6 usage thresholds that would have triggered enterprise expansion conversations last quarter and did not.
- Weeks 3 to 4
Ship the evidence-chain enrichment + Slack alert layer
High-fidelity usage signals auto-update HubSpot account records, ping the right rep in Slack, and pre-qualify inbound against an ICP classifier. Same 4-tier evidence chain I shipped for Daylit, 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.