The GTM Engineer bridge.
LlamaIndex closed $19M Series A in March 2025. You are the first marketing hire at a 44-person company with no GTM engineer, which means the layer between LlamaCloud usage and a routable lead lives on your desk by default. I run the GTM Engineer function while you hire permanently so demand-gen reporting stops being a manual rebuild every quarter.
Marketing in dev-tool categories lives or dies on whether product usage reaches the CRM.
When LlamaCloud and LlamaParse usage events do not flow back to HubSpot, every campaign report you ship rolls up on form-fill attribution while the real intent signal sits unused. That hides which channels actually drive enterprise expansion vs. which ones drive low-fit signups. For a category as developer-led as agent data infra, the highest-intent buyer behavior is product usage, not gated content. The fix is the signal plumbing underneath HubSpot, which is what your eventual GTME peer will own.
Three things only an internal builder can fix.
Form-fill attribution is the wrong unit
Enterprise AI teams do not raise hands by downloading a whitepaper. They raise hands by hitting LlamaCloud thresholds. If your dashboards cannot read those events, you optimize spend toward the channel that reports cleanly, not the channel that drives revenue.
Nurture cannot adapt to product behavior
A developer who deployed 3 LlamaIndex agents should not get the same nurture track as a marketing-qualified lead. Without the signal layer, lifecycle plays run on persona alone, and the strongest demand signal you have never shifts a sequence.
Inbound triage is invisible to Marketing
Without an evidence-chain ICP classifier, the inbound that hits the team is undifferentiated. Marketing gets blamed for lead quality the system has no way to score.
A HubSpot signal layer + ICP classifier that lets Marketing report on real intent.
- Weeks 1 to 2
Audit LlamaCloud and LlamaParse usage against HubSpot lead scoring
Map which product events should shift a lead score, fire a nurture track, or trigger an AE handoff and currently do not. Output the 4 to 6 highest-value gaps where Marketing motion lags behind product behavior.
- Weeks 3 to 4
Ship the enrichment + ICP scoring layer
Product usage writes to HubSpot account records. Lifecycle tracks adapt to behavior. A 6-signal evidence-chain ICP classifier pre-qualifies inbound before it hits sales. Same pattern 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.