If you work on the technical side of a B2B SaaS company, you already treat your product codebase with rigor. Version control. CI/CD. Automated testing. Code review. Monitoring. Alerting.
Now look at how your company treats its revenue stack. Manual CSV imports. CRM fields nobody maintains. Lead routing logic from 2022. Outbound sequences built once and never iterated. No monitoring. No alerting. No version control on anything.
The traditional approach to fixing this is a GTM audit. A consulting firm spends six weeks pulling data, interviewing people, and producing a 90-page slide deck with recommendations. Cost: $30K. Implementation rate: near zero.
GTM Engineering takes a different approach. Treat the revenue stack like software.
Diagnose with automated agents. Connect an AI to the CRM and enrichment layer. Let it analyze conversion rates across funnel stages, identify routing gaps, measure response times, and map enrichment coverage. This takes minutes, not weeks.
Ship fixes in sprints. Take the three highest-impact leaks and build implementations. Speed-to-lead routing automation. Enrichment waterfall configuration. Sequence optimization. Deploy them the way you would ship product code. Incrementally. With testing. With measurement.
Measure velocity. Track pipeline velocity metrics the way you track deployment frequency. How fast are leads being contacted? What is the conversion rate at each handoff? Where are records decaying?
The five most common leaks from an engineering perspective: routing latency (42-hour average lead response time), sequence termination (campaigns ending at 3 touches when data shows responses peak at touches 7 to 12), dead record accumulation (MQL graveyard), schema misalignment between marketing and sales qualification criteria, and single-provider enrichment creating systematic data gaps.
All of these are infrastructure problems, not strategy problems. They get fixed by engineering, not consulting.
Full breakdown: artemisgtm.ai/blog/gtm-audits-dead-gtm-engineering
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