Ship more—without hiring more engineers.
Capacity Engineering for software and delivery teams.
A delivery operating system that aligns strategy, workflow, tooling, and AI—so lead time drops, deploys get frequent and boring, and the roadmap stops slipping. More throughput from the team you have, not a bigger burn.
- DORA-measured
- Guardrailed AI
- Phased delivery
- Ticket scoped & sized
- Branch & tests scaffolded AI
- Engineer reviews PRYou
- Merged to main
- Deployed & watched
What we believe in
Velocity isn't more hours—it's a delivery system. When lead time, deploy frequency, change-failure rate, and MTTR all move the right way, you ship the roadmap without growing the headcount. AI included.
What we are best at
The bottleneck isn't your engineers—it's the system around them.
The constraint is rarely raw talent; it's WIP, review queues, and brittle releases between commit and production.
Today's friction
- Roadmap slips in handoffs, review queues, and approvals
- QA is the bottleneck—or quality leaks into prod
- Releases are manual, risky, and rolled back
- On-call is firefighting; MTTR creeps up
- AI pilots that demo well and never reach main
With a delivery system
- Lower WIP and a clear definition of done—work flows
- Shift-left checks and coverage built into the PR
- Frequent, boring, one-click deploys you can trust
- Lower change-failure rate and faster MTTR
- AI embedded in the SDLC with guardrails and metrics
// the delivery pipeline
From idea to production, end to end
AI scaffolds the build and the tests; engineers own the PR, the merge, and the deploy; DORA tracks the whole loop.
Scope
Prioritized & right-sized
Build
AI scaffolds, you steer
AIReview & test
Coverage on the PR
AIShip
Engineer merges & deploys
HumanOperate
On-call, SLOs, DORA
Your Delivery Operating System
What We Build
A practical system that aligns strategy, workflow, tools, and execution—so delivery becomes repeatable.
- What to build
- What to stop
- How to prioritize
// AI in the pipeline, safely
AI you can actually merge to main
Embedded where it moves the DORA numbers, with the branch protections and guardrails engineering leaders expect.
Grounded in your docs
AI works from your runbooks, tickets, and code standards—not guesses.
Humans own the merge
AI drafts and accelerates; engineers review and ship.
Evaluation + monitoring
Evals catch regressions; we watch change-failure rate and MTTR for drift.
Guardrails on actions
Scoped permissions and checkpoints keep agents inside your branch protections.
How We Work
What We Assess
- Value-stream map: where work stalls between commit and prod
- Toolchain reality (CI/CD, VCS, tracker—used vs. ignored)
- Test strategy, coverage, and flaky-test load
- DORA baseline and the top throughput bottlenecks
- AI readiness: code context, evals, and governance
What You Get
- A prioritized phased roadmap (Phase 1, 2, 3…)
- Clear outcomes + KPIs for each phase
- Tooling recommendations (keep / improve / replace)
- Sequencing that doesn't overwhelm the team
Each phase typically includes
- Workflow redesign + standard work
- Tooling selection / configuration / deployment
- Automation + AI assist (where safe and useful)
- Training + coaching for adoption
- Dashboards + weekly operating cadence
Start with the constraint, prove value, then scale.
Typical Phases
- Map the value stream from commit to production
- Reduce WIP and tighten the “definition of done”
- Right-size intake and prioritization
- Baseline the four DORA metrics on a live dashboard
- Shift-left checks and lift meaningful test coverage
- Tame flaky tests and shrink the review queue
- Standardize one-click, low-risk release patterns
- Automate manual release and handoff steps
- Embed copilots and agents across build, test, and support
- Add branch-protection guardrails, evals, and monitoring
- Track lift on lead time and change-failure rate, then expand
Frequently asked questions
Will this slow the team down during rollout?
No—we deliver in phases and automate the busywork first (flaky tests, manual release steps, status-chasing), so the team feels relief in the first sprint instead of disruption.
How do you fit our existing toolchain?
We integrate around what you already run—GitHub or GitLab, your CI/CD, Jira or Linear, your observability and on-call stack—and improve it rather than mandating new tools.
Is the AI safe to put in our delivery pipeline?
Yes—agents run inside your branch protections and required checks, with evals and monitoring, and a human owns every merge to main.
We already do Agile—what's different?
Ceremonies aren't the constraint. We re-engineer the delivery system itself—lead time, deploy frequency, change-failure rate, and MTTR—and add AI leverage that moves those numbers.
How do you measure success?
The four DORA metrics plus throughput and engineering cost per shipped feature—baselined up front and tracked on dashboards so the ROI is visible.
Find your constraint. Get capacity back.
Book a 30-minute assessment—we'll map the bottleneck and the fastest path past it. No slide decks, no obligation.
Book your assessment