Your APM tells you what broke. DORA tells you how fast you shipped.
Neither tells you whether your engineering practices are getting better or worse.
The SDLC isn't dead — it's just unobserved.
AI accelerated the development lifecycle. But speed without observability creates blind spots. Review quality collapses. Governance gaps widen. Practice drift compounds silently until it surfaces as an incident, a failed audit, or a security breach.
50 Protocols Across 6 SDLC Phases
Every protocol is scored from real toolchain data — GitHub, GitLab, Bitbucket, Jira, Linear. No surveys. No self-assessments. Just evidence.
Issue hygiene, backlog health, sprint cadence, architecture decision records
Velocity metrics mask the fact that 40% of stories ship without acceptance criteria.
Branch strategy, commit practices, code ownership, dependency management
AI-generated PRs bypass ownership rules and inflate commit counts without review.
Review depth, time-to-review, approval patterns, rubber-stamp detection
Auto-approvals rise 3x when teams adopt AI code gen — review quality collapses silently.
CI reliability, test coverage trends, build times, flaky test detection
Teams disable failing checks to unblock deploys. Green pipelines hide red practices.
Deployment frequency, rollback capability, environment parity, release governance
Faster deploys without governance produce more incidents, not fewer.
Incident response, SLA tracking, on-call practices, post-mortem culture
Post-mortems happen for SEV-1s but practice drift causing them goes untracked.
The Missing Layer in Your Engineering Stack
| Category | Tools | What It Observes | What It Misses |
|---|---|---|---|
| Application Observability | Datadog, New Relic, Dynatrace | Runtime — requests, errors, latency | How the code got there. Review quality, governance gaps, practice drift. |
| DORA / DevOps Metrics | LinearB, Sleuth, Faros | Throughput — deploy frequency, lead time | Whether speed is degrading quality. Fast and broken looks identical to fast and safe. |
| SDLC Intelligence | Concordance | Engineering practices — the human and process behaviours across all 6 SDLC phases | Nothing. This is the missing layer. |
If you can't answer yes to these, you have a visibility gap. Check the ones you can do today.
Most engineering teams have this blind spot. Find out where you stand.
Run a free assessment on any repo. 60 seconds. No sign-up. See what you've been missing.
Get Your Practice Score Free →Read-only access. Your code is never stored.
From blind spot to Practice Score in 60 seconds.
OAuth to GitHub, GitLab, Bitbucket, Jira, or Linear. Read-only. 60 seconds.
50 engineering protocols scored from real toolchain data across all 6 SDLC phases.
Continuous practice monitoring. See drift before it becomes an incident.
Velocity Governance lenses for compliance (CRA, NIS2), AI risk, and team health.
Built for teams that ship fast and need to prove it's safe.
Single pane of glass for engineering practice health. Answer "how are we doing?" with data, not intuition.
Continuous CRA and NIS2 evidence from real engineering data. Not annual audits. Not checkbox surveys.
Team-level practice scoring. Spot drift early. Coach with evidence.
The missing observability layer. You instrumented your apps — now instrument your practices.
Stop flying blind. Start observing your SDLC.
50 protocols. 6 phases. One Practice Score. Scored from real toolchain data — not surveys, not self-assessments, not gut feel.