Ship more, page less. We build the CI, review, and on-call automations that the platform team’s roadmap has been deferring for two quarters — so engineers spend more time on design and shipping, and less on yak-shaving the build, waiting for review, and getting paged for the same flaky alert.
Indicative timeline. Final scope and dates agreed after the intro call.
The build wires AI assistance into the right places — PR review, scaffolding, test generation, refactor planning — not just sprinkled on as IDE autocomplete. The lift compounds across the team, not per-dev.
The gap between elite and low performers is mostly tooling and workflow, not raw talent. The build closes the highest-leverage parts of it — CI, review, deploy — without a multi-quarter platform rebuild.
AI first-pass review surfaces the obvious things — style, missing tests, common bugs, security smells — before a human looks. Reviewers get the diff with the nits already flagged and spend their time on design.
Smart routing, alert deduplication, and runbook auto-execution cut night pages on the recurring stuff. Your best engineers stop writing their resignation in the small hours after the third 3 a.m. page.
The build sets up the harness, the shared skills, the MCP servers for your internal tools, the permissions baseline, and the contributor runbook — not just “here’s an API key, good luck.”
Pipeline parallelisation, smart test selection (only what’s actually affected), aggressive build caching, and secrets hygiene. Median cut on existing repos lands at 40%+ wall-clock without changing your test suite.
First-pass review on every PR — style, common bugs, missing tests, security smells, and subtle regressions caught with custom evals against your repo. Human reviewer sees the diff with the obvious things already flagged or fixed.
Alert deduplication, escalation graphs based on real on-call patterns, and runbook auto-execution for the top recurring alerts. Tied into PagerDuty, Opsgenie, or Incident.io. The 3 a.m. pages that should resolve themselves do.
Org-wide Claude Code setup: shared skills, MCP servers for your internal tools, permissions and settings.json baseline, hooks, and a contributor runbook. The next engineer joining isn’t starting from zero.
One working session with your platform / eng leads. We profile your CI, sample recent PRs and review wait times, and pull the on-call paging history. Highest-payoff first deliverable picked by end of day three.
Usually CI parallelisation or AI review on a scoped repo — the change with the most measurable impact and the lowest blast radius. Real numbers against the week-one baseline by end of week one.
Alert dedup and runbook automation tied into your paging system, and the Claude Code baseline rolled out to a pilot squad with shared skills and MCP servers wired to your internal tools.
Expand from pilot to the rest of engineering, ship the dashboard (CI time, review wait, pages per shift), and walk the platform team through the runbook. You leave week three with the build live and a baseline you can keep measuring.
Indicative timeline. Monorepos, regulated environments, or unusually large on-call surface can stretch this; we confirm dates after the kickoff session.
Defined scope, agreed in writing before kickoff. No metered hours, no surprise add-ons, no scope creep mid-build. The first week sets the bar — we ship to it, and you see the build running against your real PRs and pages by week three.
You own the repo, the prompts, the skills, and the model relationship. Production model usage is billed by the provider directly to your account — no markup, no reseller margin, no vendor lock-in to us.
Investment is sized to your repo count, on-call surface area, and Claude Code adoption scope after the intro call. We come back with one number, in writing.
Start a projectWe don’t rewrite from scratch — that’s how DevEx projects get killed. Week one is a profiling pass: which steps run on every PR vs only on main, what’s actually blocking on the network vs the tests, where caching is missing. We ship the highest-leverage cuts in the first week without touching the rest of the pipeline.
That’s the failure mode we’re explicitly building against. The reviewer is grounded in your repo’s conventions, your tests, and your past PR comments — not a generic style guide. We tune signal-to-noise on a sample of recent PRs before turning it on, and the dashboard tracks comment dismissal rate so you can see if it’s drifting.
Copilot helps individual devs in the editor. The build addresses the parts no IDE plugin can touch: PR-level review, on-call routing, multi-step refactors via Claude Code, and the org-wide tooling that turns one productive dev into a productive team.
In infrastructure you control. Code never leaves your VCS. Review and Claude Code orchestration deploy in your cloud account (AWS, GCP, Azure) by default. Your code does not train external models.
Three numbers, baselined in week one: median CI time per PR, median PR review wait time, and pages per on-call shift. The dashboard ships with the build and reports against the baseline so the lift is on the record, not in a slide.
Yes. Production model usage is billed by the provider directly to your account — no markup, no reseller margin, no lock-in to us. You can swap providers post-handover. Build-time spend is on us.
Three options: (1) take the repo and run it internally, (2) keep us on for monitoring, runbook expansion, and Claude Code skill development, (3) scope a follow-on build (test generation, infra automation, observability pipelines). No pressure to continue.
Tell us where the engineering bottleneck sits today — CI, review, on-call, or Claude Code adoption. We’ll come back within one business day with the next step.
Open the contact form