The work was there. The system wasn’t.
When Karim Yahia joined as Adapt’s founding GTM engineer, the stack was conventional but disconnected: HubSpot as the CRM, Apollo as the primary enrichment source, and ZoomInfo seats that were rarely used because they sat outside the team’s day-to-day workflow. There was no data warehouse and almost no built enrichment workflow.
A narrow ICP, with no shared operating layer
Adapt sells to independent P&C insurance agencies. Account records were sparse and inconsistent, so reps filled gaps by cross-checking vendors, using the CRM directly, or patching issues with their own AI tools. There was no account prioritization or scoring, and very little top-of-funnel signal reached the team.
The real cost was invisible work
Individual workarounds had accumulated across personal automations and native workflow builders. They were useful to the person who built them, but difficult for the rest of the team to discover, safely change, or reuse. A coding agent could not see them either.
From scattered judgment to an operating system.
The result was not another dashboard. It was a durable way to find, decide, and act on GTM signals.
A dependable data foundation
Adapt started with TAM enrichment, using a waterfall across providers so one vendor’s gaps do not become blank CRM fields. That work exposed duplicate account fields, duplicate data, and inconsistent CRM modelling that had been hidden in daily operations. Deepline became the layer its coding agents use instead of writing directly to HubSpot and running into rate limits.
Signals built for a narrow ICP
The team combines de-anonymized website visitors, LinkedIn page visitors, and semantic and keyword searches across social platforms. Because Adapt’s ICP is so narrow, a relevant site visitor has unusually high signal. Each signal filters by ICP, enriches the contact, and pulls existing HubSpot context before it reaches a shared Slack channel.
One library instead of scattered workarounds
The hard part was surfacing a fragmented set of existing rep-built processes and migrating them without breaking downstream dependencies. The result is a central library of Deepline Plays that the whole team can see, review, and improve.
A question becomes a workflow. Then it keeps running.
- 01
Start with the TAM
Build a complete account foundation first, so segmentation, targeting, and later persona enrichment have dependable inputs.
- 02
Turn research into reusable signals
Package ICP filtering, enrichment, and CRM context into repeatable Plays instead of rebuilding the same one-off script.
- 03
Standardize without breaking the work
Bring scattered rep workflows into one visible system gradually, preserving what depends on them while replacing the patchwork.
One visible system, with room to keep building.
Nearly every GTM workflow at Adapt now runs as a Deepline Play, with the relevant customer data in one shared database rather than spread across tools and personal automations. Routine fixes typically take 10–15 minutes, and the team now sends roughly 20–30 signal alerts a day. Those alerts feed a meaningful share of newly created opportunities.
