A mining safety company needed to find prospects, and no pre-built tool existed for that niche. Jai Toor at Deepline pointed a coding agent at the problem. It found a Reddit ML model, combined it with hiring signals, and delivered qualified contacts in under 30 minutes.

Jai walks through Deepline's developer-first approach to GTM data infrastructure, from waterfall enrichment to deploying deterministic code-based workflows. He shares how close-lost analysis surfaces niche buying signals specific to each company.

Topics discussed:

  • Building code-native GTM infrastructure designed for coding agents
  • Using waterfall enrichment across multiple data providers programmatically
  • Discovering niche buying signals through close-lost regression analysis
  • Deploying deterministic plays from iterative agent testing
  • Adopting pay-as-you-go pricing inspired by OpenRouter's model
  • Centralizing data while decentralizing agent-based rep interfaces
  • Generating org charts from multiple data sources at $7 each
  • Predicting cold call answer rates with ML-driven scoring models

Rss
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