Developer Experience Assessment

Read-only delivery metadata only - PR cycle time, review latency, pipeline duration, flaky-test rate, deployment frequency, change failure rate, MTTR. We never read source code, secrets, or repo contents beyond delivery analytics. All data is anonymised at ingestion (engineer names hashed, customer identifiers stripped, free-text fields removed) before analysis. We provide the exact OAuth scopes / PAT permissions in advance for your security team to review.

No. Engineer interviews are strictly confidential. Quotes are anonymised in the final report, never attributed to a specific engineer, and individual interview transcripts are never shared with management. We use a structured DevEx interview protocol covering feedback loops, cognitive load, and flow state - and explicitly do not collect signal on compensation, performance reviews, or attrition risk.

Surveys give you self-reported satisfaction; this assessment gives you objective delivery data plus self-reported satisfaction plus external benchmarking. Internal surveys rarely produce DORA cohort comparisons (Elite vs High vs Medium vs Low), CNCF Platform Engineering Maturity Model alignment, quantified inner-loop speed-ups against industry medians, or a board-ready business case framing. A senior platform engineer who has built developer platforms at scale combines all three lenses into a prioritised investment plan.

Yes. We assess your IDP maturity against the CNCF Platform Engineering Maturity Model and recommend a platform from Backstage, Spotify Portal for Backstage, Port, Cortex, OpsLevel, Roadie, Configure8, Atlassian Compass, Humanitec, or Mia-Platform based on your team size, existing stack, and golden-path requirements. The deliverables include a concrete IDP rollout plan with phased adoption, golden-path scaffolding strategy (Backstage Software Templates, cookiecutter), and self-service infrastructure recommendations (Crossplane, Terraform Cloud, Spacelift, Humanitec).

Yes. We measure current adoption and impact of AI-assisted coding tools (GitHub Copilot, Cursor, Windsurf, Cline, Claude Code, Codeium, Tabnine, JetBrains AI, Sourcegraph Cody) plus AI code-review (CodeRabbit, Greptile), benchmark against industry productivity uplifts, and produce a rollout plan covering tool selection, training, security guardrails (especially for regulated codebases), telemetry, and the change-management work that determines whether AI coding actually moves your DORA metrics.

Yes. We compute deployment frequency, lead time for changes, change failure rate, and MTTR / failed-deployment recovery time from your delivery data, then place each metric in the DORA cohort buckets and identify the specific changes (pipeline parallelisation, merge queues, trunk-based development, feature flags, progressive delivery with LaunchDarkly / Unleash / Statsig / Flagsmith) that move you between tiers.

That is one of the explicit deliverables. The report includes a board-ready business case with ROI framing for the top 5 DX investments - quantified hours-saved per engineer per week, DORA cohort movement, retention impact, and cost-avoidance from reduced onboarding time and reduced toil. Engineering leaders use it to unblock platform-team budget and tooling spend.

Typical turnaround is 5-7 business days from data access and engineer interviews, plus a 45-minute live findings walkthrough at a time that suits your engineering leadership. Larger organisations with multiple business units can take a little longer; we confirm the timeline as soon as we see the scope.