GTM Intelligence Platform

Enterprise GTM teams were running signal monitoring, CRM enrichment, and fit assessment as disconnected workflows across separate tools. Signals surfaced companies but not who to contact. CRM data went stale within days. Nobody had a live view of what the CRM was doing.

A full-stack GTM intelligence platform: ranked signal feed, event-driven CRM enrichment, AI-assisted fit classification, decoupled Python backend, and live activity monitoring. Shipped to production for a regulated financial services client.

Enterprise GTM teams drown in signal noise, stale CRM data, and manual fit assessment. I built a platform that closes the loop in one interface: a ranked signal feed, automated contact enrichment triggered by CRM events, AI-assisted fit classification, a decoupled backend for independent iteration, and a live activity view so the team always knows what changed. Built as forward-deployed product work inside a regulated financial services environment.

1. 1.
   Signal Ingestion + Ranking: Ingest from multiple market signal sources and rank by relevance and source trust. Define what counts as a signal before building ingestion so the feed reflects analyst workflow.
2. 2.
   Event-Driven CRM Enrichment: Enrich contact records automatically when CRM events fire, rather than on a batch schedule. Write enriched and classified data back to the CRM without manual intervention.
3. 3.
   Decoupled AI Backend: Separate classification logic from the frontend via versioned API contracts so model and prompt changes ship without UI redeploys.
4. 4.
   Activity Monitoring + Alerts: Surface enrichments, re-classifications, and contact updates in a live feed. Route notifications only for events that earn an interrupt based on analyst-defined criteria.

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  Event-Driven Over Batch: Chose webhook-triggered enrichment so CRM data stays current within seconds of a new contact, accepting the engineering cost of idempotent handlers and deduplication.
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  Re-Classification on Change: Fit classification retriggers when enriched data changes, not only on first import. Activity logging captures when a contact moves across fit boundaries.
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  Contract-First AI Layer: Defined API schemas before building frontend or backend. Modular classification per signal type with validated structured outputs.
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  Interrupt Criteria Design: Notification rules came from workflow research: what characteristics justify pulling an analyst out of their current task. Relevance over volume.

Batch enrichment produces a CRM that is accurate on a schedule. Event-driven enrichment produces a CRM that is accurate when someone needs to act. For a team running on signal speed, stale contact data is not a minor inconvenience. It is a competitive disadvantage. The engineering tradeoff is worth making.

Signal monitoring, contact enrichment, fit classification, and activity logging are separate capabilities that earn their keep when they connect. A signal surfaces a company. Enrichment fills in the contact. Classification answers whether they fit. Activity logging proves the chain happened. The product value is the workflow, not any single layer in isolation.

I traced the full analyst workflow before expanding scope. Surfacing a signal is step one. The product question is always who do we contact and do they fit our criteria? The platform architecture follows that sequence.

Production deployment with multi-source signal ingestion, automated enrichment, and live CRM activity monitoring. ~$215K annual GTM stack cost reduction (Kinage · regulated financial services client).