GTM Engineer: Jai @ Deepline

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