AI‑Based Reverse Engineering

Mine your delivery exhaust — repos, pipelines, incidents and tickets — to surface systemic blockers and outcome drift without slowing teams down.

How it works

  1. Ingest — read‑only connectors for Git, CI/CD, observability, ticketing (no PII).
  2. Model — code graph + LLMs map ownership, coupling, and change risk to outcomes.
  3. Recommend — prioritised, evidence‑based actions with expected ROI.
Read‑onlyLeast‑privilegeData minimisation

Sample findings

  • High PR cycle time and review lag in Gateway service (queueing bottleneck).
  • Test flake concentrated in Mobile module; target contracts first.
  • Incident pattern links to shared auth dependency; decouple seam.
Repo A42Service B68Gateway57Mobile81Batch63
Illustrative risk/flow index by component (higher = more attention).

Signals we extract

  • Code churn, ownership, coupling, dependency centrality.
  • PR cycle times, review quality, flaky test hotspots.
  • Incident patterns, MTTR/MTBF, toil signatures.
  • Backlog volatility, delivery cadence, rework.

Why teams trust it

  • Transparent reasoning: link every recommendation to evidence.
  • Safe by default: no writes, no secrets harvested, narrow scopes.
  • Actionable: prioritised, small steps that teams can land this quarter.

What you get

  • Hotspot map by service/team/interface.
  • Refactor vs. replace decision support using flow & risk.
  • Top‑3 plays with owners, effort and expected impact.