Scoped access in your environment.
We work in your repositories and cloud environment, using your identity provider where supported. Permissions are limited to the agreed scope and remain revocable by your team.
AI transformation for
product engineering teams.
We help you go from an idea to a feature in production ~2–3 times faster.
For organizations with 100+ engineers.
Engineering experience from Microsoft and Amazon.
PROBLEM
AI adoption hasn’t shortened your release cycle.
Your engineers use AI coding tools, but features still take too long to reach production.
Different teams get stuck in different places.
Slow reviews and handoffs.
Routine work waits on PR reviews, approvals or steps agents lack permission to complete.
Unclear specs and context.
Vague requirements, missing context and undocumented decisions leave agents guessing.
Broken feedback loops.
Agents lack the tools, environments or tests to build, verify and fix their own work.
Large, unfamiliar codebases.
Agents struggle to navigate sprawling repositories and legacy systems without clear instructions.
How we work.
We start with one engineering team. Together, we assess how you use AI, choose where to intervene and agree how to measure the results.
Assess your workflow.
Review specifications, codebase, agent configuration and CI/CD pipelines with your engineers. Establish a delivery baseline and prioritize changes based on their likely impact, dependencies and risk.
Implement and validate.
Improve specifications, agent context, orchestration and automated checks where the assessment identifies a need. Test the changes on real engineering tasks and integrate them into your existing workflow.
Train your engineers.
Work through task definition, agent execution and debugging with your team. Document configuration and operating procedures, and establish who maintains the tooling.
Measure the results.
Compare delivery time, engineering effort, defects and AI costs against the baseline. Use the findings to refine the implementation and decide whether to extend it to other teams.
Proven at scale.
At Microsoft we helped 50+ internal teams ship 2–2.5× as many PRs per engineer, on average, in three months by improving the agent tooling and workflow around real delivery bottlenecks.
What we measure.
We agree a baseline and scoring method before making changes, then compare similar work over the same period.
| Outcome | What we track | Why it matters |
|---|---|---|
| Output | Shipped PRs, adjusted for complexity. | Tracks estimated engineering work reaching production, using consistent scoring and accounting for team capacity. |
| Speed | Time from idea to production. | Shows whether customers receive features sooner, including time spent waiting on specifications, review and release. |
| Quality | Bugs and incidents introduced. | Shows whether faster delivery comes with more defects that escaped checks and review, or more disruption in production. |
| Cost | Total spend and cost per feature shipped. | Shows the full cost of delivery and whether each shipped feature is becoming more economical. |
Improve agent performance across the development lifecycle.
We design and implement the developer tooling, agent systems and delivery controls that remove bottlenecks in your engineering workflow. We connect them in a self-improving loop: capture what went wrong, improve how agents work, and verify the change before it reaches other teams.
Code review, release and production feedbackMake review and release keep pace with AI-assisted development.
We build code-review workflows that combine agent analysis with deterministic checks for API compatibility, schema changes and security. Review findings are calibrated against your engineers’ decisions to reduce noise. We connect CI gates to staged rollouts and service health signals, with explicit stop, rollback and escalation rules. Production defects feed back into regression tests and review checks.
Agent loops and orchestrationBuild workflows that recover from failure and know when to stop.
We build agent harnesses with explicit task contracts, durable state and deterministic build and test gates. Checkpoints let runs resume after interruption; idempotent tool actions prevent duplicate work during retries. Agents investigate failures and repair changes within a fixed retry and cost budget, then escalate with the evidence an engineer needs. Parallel work uses dependency-aware scheduling and integration checks, so locally passing changes also work together.
Self-improving agent systemsTurn past mistakes into better performance on the next task.
We build feedback loops that capture failed runs, review corrections and production defects, then turn recurring problems into updates to agent instructions, reusable skills, tools and automated checks. A missed edge case becomes a regression test; a repeated correction becomes guidance future tasks can reuse. Changes are versioned and tested against real tasks and a separate validation set, tracking successful completion, engineer rework, time and cost. Your engineers control what is adopted and can roll it back.
Codebase context and engineering knowledgeGive agents the context your experienced engineers use to make decisions.
We map how your services fit together and capture the product rules and architecture decisions that are missing from the code. We build workflows that find the relevant code, documentation and tests for each task, including downstream services a change could affect. Repository instructions and reusable skills live alongside the code, with checks that flag outdated guidance as the system changes.
Development environments and agent toolsLet agents run the application and verify their changes before review.
We build isolated development environments that start with the application, its dependent services and approved test data ready to use. We connect browsers, logs and internal tools through MCP or APIs, so agents can reproduce a bug, run an end-to-end test and investigate failures. Automated setup and reset keep each task reproducible; scoped permissions let agents do useful work within your access controls.
Security and Compliance.
Defined IP and licensing terms.
The engagement agreement specifies IP assignment, confidentiality obligations and third-party licensing before work begins.
Required checks and accountable engineers.
Changes pass your required engineering checks. Human approval follows the permissions and risk criteria your team defines. A named engineer remains responsible for delivery.
Approved model providers and data use.
Only approved providers process your code and data. Provider configuration and contract terms prohibit their use for model training.
Where is delivery
getting stuck?
Tell us how your team uses AI and where delivery falls short. We’ll tell you what’s worth investigating and whether an assessment makes sense.
Book a discovery call