AI adoption that actually ships.
We make engineering organizations AI-native across the full SDLC — drive real adoption, remove the bottlenecks, and prove the ROI. Measured, not hyped.
20 years leading engineering orgs · up to 65 people · 4 continents
Two ways we make AI pay off.
The core work: getting AI adopted across your whole organization, and rebuilding your delivery pipeline around it.
AI Adoption for Organizations
Enterprise-wide AI adoption across the full SDLC — not a tool pilot that fizzles. We roll out AI coding, review, and testing, drive it to real adoption across every team, define the measurement, and prove the ROI to your C-suite with an honest before/after.
AI-native SDLC
We rewire your delivery pipeline so AI carries every stage — requirements to release — removing the bottlenecks, with a human gate at every consequential step. Faster flow, higher quality, control intact.
See how the pipeline works →Delivery Health Assessment
Instrument DORA and flow metrics, find the real bottlenecks, and leave with a prioritized 90-day roadmap.
Fractional Engineering Leadership
Part-time Director or VP of Engineering for teams without senior leadership.
We rewire delivery around AI — without losing control.
Most teams bolt AI onto a step or two and hope. We rebuild the whole software lifecycle so an agent carries the load at every stage, coordinated by one orchestration layer, with a named human at every consequential gate. The queues between stages disappear, and quality goes up, not down.
Discovery & Requirements
Analyst agentReads your codebase, docs and tickets, then drafts user stories with acceptance criteria.
Architecture & Design
Architect agentProposes a design that fits the patterns already in your repo, and writes down the tradeoffs it made.
Planning & Decomposition
Planner agentBreaks epics into dependency-ordered tasks, each small enough to review in one sitting.
Implementation
Developer agentWrites the failing test first, then the code that passes it, one task at a time.
Code Review
Reviewer agentReviews every diff against your standards before a human ever opens it.
QA & Test
QA agentGenerates the suite from the acceptance criteria and runs it on every change.
Release & Deploy
Release agentAssembles the release, writes the notes, and watches the rollout.
Operate & Learn
SRE agentTriages incidents, tracks down the cause, and files the fix back into the backlog.
⟲ Telemetry from 08 Operate returns to 01 Discovery through the orchestration layer. The loop closes.
AI proposes at every step; your team decides at every gate. That's how adoption sticks — and how you remove the bottlenecks without handing over the keys.
Measured change, not opinions.
A sample of before-and-after outcomes from leading engineering organizations through delivery and AI transformation.
Metrics are real; company names withheld by design.
Measured, not hyped.
Measure first
We instrument before we recommend. No opinions where numbers will do — and a baseline so every gain is provable.
AI where it earns it
Adoption tied to outcomes and ROI, not tool count. Real before/after, honest reporting, human-in-the-loop gates.
Code review to boardroom
Technical enough to be credible in a review, clear enough to make the business case to a CEO. Both, from one person.
We build our own software, too.
Compliance and developer-productivity products of our own are in the works — the same measured, AI-native approach, turned into a platform. More soon.
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