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The problem
Leads were routed by segment or territory, to the enterprise, mid-market or SMB team. That logic ignored whether a specific rep was a good match for a specific buyer, even though Jacob kept seeing calls go better when the rep and buyer had something in common, like a favorite football team, a hometown or a university.
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What they built
A routing model built from Attention's own deal history. Jacob and his team combined CRM records with call transcripts and enriched company data to find where each account executive wins and where they struggle, by region, segment, industry and shared interests. One finding: the rep with a UK passport closes international deals at a higher rate than the rest of the team. When someone books time with Attention, the lead is now routed to the best-fit rep.
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What changed
+30% win rate since routing to the best-fit rep
Jacob reports a 30% lift in win rates and 25% faster time to close since routing opportunities to the best-fit rep. He also says reps enjoy selling more because they talk to buyers they have more in common with.
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So what
Leads go to the rep with the best record for that kind of buyer, based on the team's own wins and losses rather than gut feel or territory alone.
Under the hood
For GTM engineers
Salesforce deal data (firmographics, amount, stage, win/loss, closed-lost reasons) joined with the Attention API, which mines call transcripts for champions, economic buyers, competitors, personas and geography, and Deepline for account enrichment. The analysis and new account scoring models were built in Claude Code after a historical rerun of all Salesforce data, with Deepline re-enriching the CRM. Best-fit matching is embedded in the live booking workflow.
More examples
Other systems GTM engineers have built, or see all of them.
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OpenAI Cut post-call admin to a quick review: Salesforce tasks, follow-up emails and internal Slack updates are drafted from the call
Bryant McCombs · Account Director -
AssemblyAI An in-house AI agent now resolves 80% of inbound sales and support questions without a human, up from 10% with an off-the-shelf bot
Matt Lawler · Lead Forward Deployed Engineer, Onboarding -
LangChain Turned LangChain's customer calls into answers anyone can look up, like which customers run on which cloud or need self-hosting
Vishnu Suresh · Software Engineer
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