We embed alongside your engineers to build an Agentic SDLC around your real codebase. Your team leaves with the harness, specifications, test-first workflow and review gates needed to use AI safely at scale.
Trusted on systems that cannot fail
Most engineering teams already use AI. Few have the shared rules, specifications and delivery gates needed to turn individual gains into predictable team performance.
Pull requests opened this sprint, grouped by primary AI toolWhere the same 36 pull requests are nowReview comments per pull request, grouped by primary AI tool
Two of six engineers opened 20 of the 36 pull requests. The performance gap is visible, but the reason behind it is not.22 are waiting on the same two senior reviewers.From 1 to 17 comments on one codebase in one sprint. 8 went back for rework.
One accountable team covers the complete uplift, from measuring how your engineers work today to helping them run the new practice without us.
Establish an honest baseline across specifications, tests, reviews, harness controls and delivery metrics. The assessment shows where AI is helping, where it is creating new bottlenecks and which improvements are worth funding.
Build the guides, sensors, hooks and permissions that keep AI agents working safely inside your technology stack.
Replace ad hoc prompts with structured, behavior-first specifications. Engineers and AI agents work from the same agreed definition of success.
Introduce test-first habits and tooling that make AI-generated changes easier to verify. A failing test defines the expected behavior before implementation begins.
Automate routine checks and reserve human approval for decisions that require judgment.
Work side by side on your real backlog. Eastgate leads the early work, then gradually transfers ownership until your engineers run the practice independently.
The finished state is simple: nothing depends on Eastgate. Your engineers own the harness, specifications, tests, review gates and improvement cadence.
Nothing here needs us
Handed over
Every number comes from your own repositories and pipeline, before and after the uplift.
If the baseline shows the constraint is somewhere other than engineering practice (an unstable codebase, an unclear product decision), we say so and point you at the service that actually fixes it.
Select a card to compare it with the typical baseline.
Most organisations we assess sit at level 0 or 1. The assessment confirms where you are before a rollout is planned.
AI use is individual and largely invisible. There is no shared policy, governance or measurement.
Approved tools and a usage policy are in place, but results still depend on who uses the tools and how.
One team uses a shared harness, structured specifications and review gates, measured against a baseline.
Multiple teams build on one platform and follow one governance model, reviewed on a regular cadence.
AI-native delivery is the organisational default. Standards and performance improve release by release.
One team, one real deliverable
Apply the model to one team and one production deliverable. Measure before and after; if the numbers do not improve, revise the roadmap before expanding.
One shared platform, more teams
Turn what worked into a shared harness and governance model, so the next teams build on it instead of starting again.
Make it the organisation-wide default
Bring the remaining teams in by planned waves. Champions from earlier teams lead adoption and refine the practice.
Chosen for what your team can operate and hire for, not for what is new.
Claude Code 
GitHub Copilot 
Cursor 
Windsurf 
Gemini Code Assist 
MCP 
pre-commit 
ESLint 
SonarQube 
Snyk 
Cucumber 
Playwright 
Jest 
Vitest 
pytest 
Cypress 
GitHub Actions 
GitLab CI 
Docker 
Kubernetes 
Terraform 
Argo CD The operating model only holds if the people using it believe in it. Our engineers pair with your teams, run the reviews with them and hand the practice over once it runs without us.
Our engineers work your tickets next to yours, not a training project.
Templates and review checklists are drafted with the people who will own them.
Runbooks, playbooks and a trained internal champion stay when we step back.
Three shapes. After the assessment we tell you which one fits, including when the answer is none of them. Every engagement starts with an assessment. It is fixed price, it ends in a written plan, and the plan is yours whether or not you continue with us.
You need a baseline before you commit.
Prove the practice on one team first.
Roll it out across your teams.
Certified, accredited and independently reviewed
Something not covered here? Ask an engineer directly, no SDRs, no funnel.
Usually not. The harness sits around the tools your team already uses. We standardize how they are used, with specs, tests and review gates, not which tool is on the screen.
Yes. Enablement happens on your real backlog, not on a training project, so delivery continues while the practice is built.
The assessment records your baseline first: throughput, rework, escaped defects, lead time and traceability. The same measures are tracked through the engagement, so the result is a number, not an impression.
Then the baseline will show it, and we will say so. An unstable codebase or an unclear product decision needs a different fix, and we point you at the service that addresses it.
The harness configuration, spec templates, test tooling in CI, review checklists, playbooks, trained internal champions and a metrics dashboard. Your team runs it without a standing retainer.
German engineering discipline meets agentic delivery. Send a short note and we'll reply within one business day, straight to an engineer, no SDRs, no funnel.
