Technical deep-dives, case studies, and insights on GTM Engineering, AI Agents, and Revenue Operations. We share what we learn building autonomous revenue engines.
Your CRM, billing system, and ERP show three different ARR numbers. Here's how to unify them with a warehouse-native architecture.
REST vs GraphQL, rate limiting strategies, webhook signatures, and the patterns that separate production-grade integrations from demo-day hacks.
79% of sales orgs miss forecasts by 10%+. The problem isn't your reps—it's data entropy. Here's how to detect and fix it.
Enterprise attribution tools cost $30-60K/year. Here's how to build warehouse-native MTA for under $500/month using dbt, Snowflake, and first-party tracking.
95% of AI agent pilots fail to reach production. Here's a framework for knowing when agents help—and when they're an expensive liability.
Commodity enrichment providers cover 60-70% of your data. Here's how to build a waterfall architecture that fills the gaps.
How we reduced lead response times from 2 days to 5 minutes using n8n workflows. A technical breakdown of our lead routing architecture.
Most companies are paying for 3x the tools they need while still having data gaps. Here's how to audit your stack and cut the waste.
Most AI agent projects fail in production. Here's why, and the engineering patterns that make the difference.