gtm-engineering

Start by gathering your lead volume, CRM, and current stack, then build a five-stage pipeline: ingest, enrich, score, route, act. Use an enrichment waterfall with a 0.85 confidence threshold and route hot leads to AEs with a response SLA under one hour.

Choose n8n for complex AI workflows, high volume, or self-hosting needs; Make for visual workflow design at moderate complexity and budget; Zapier for simple trigger-action integrations by non-technical teams. Enterprise compliance needs point to Tray.io or Workato.

An enrichment waterfall calls data providers sequentially, such as Clay, then Apollo, then ZoomInfo, stopping when confidence reaches 0.85. Results between 0.50 and 0.85 are accepted with a flag, and anything below 0.50 is rejected to control cost and quality.

Common causes are API rate limits, bad input data, and timeouts. Add retries with backoff, validate inputs, version workflows as JSON in Git, cache enrichment results for 30-90 days, and alert when workflow success rates drop below 95%.

Use smaller models like Haiku for classification and larger models like Sonnet for writing, cutting LLM costs 60-80%. Cache enrichment results for 30-90 days, apply tiered enrichment only to high-scoring leads, and batch API calls.

Do not use it for general software implementation, code review, or application architecture tasks. It covers GTM workflow design, automation platform selection, and agent orchestration for revenue systems, not product engineering work.