Capture engineering decisions and intent: link the 'why' to code

Executive Summary

Engineering teams building distributed systems and microservices increasingly lose the "why" behind code changes—decisions recorded in PRs, chats, and docs rarely connect to the code paths that later cause incidents. This is a widespread problem for companies with more than 10 developers—roughly 160,000 engineering orgs—where repeated debugging, onboarding delays and costly rollbacks are routine. You could build an LLM-assisted intent-capture platform that automatically extracts decision intent from PR descriptions, chat threads and design docs, generates structured Architectural Decision Records, and links those records to commits, deployments and observability signals. Integrations with Git, CI/CD, issue trackers, Slack/Teams and monitoring (APM/logs/tracing) would let teams query “why” a line of code exists, trace incidents back to the originating decision, and surface drift between intent and implementation. The timing is favorable: a $9.6B TAM (160,000 orgs × $60K ACV), low direct competition, and recent advances in LLM extraction plus the convergence of observability and developer experience make automated intent tracking technically and commercially feasible. To stand out you'll need enterprise-grade accuracy (human-in-the-loop validation), privacy controls (on‑prem or encrypted pipelines), and deeply opinionated workflows that map intent to measurable outcomes (e.g., reduced MTTI/MTTR, faster onboarding) rather than just a searchable doc store. The strengths are clear—high ACV, low competition, and a real operational pain—but challenges include proving extraction precision, integrating with heterogeneous toolchains, and the enterprise sales motion required to change engineering habits.