Normal branches and PRs
Work is packaged for your existing review and merge path.
Governed Delivery Sprint
For early-stage startups and small product teams with a GitHub repo and a few clear features or changes they already know they need.
A bounded paid sprint helps you use AI-assisted speed on real work while keeping review, merge, and product judgement with your team.
Selective, bounded, and review-led. Your team keeps normal review and merge control.
Market observation
AI-assisted engineering gives teams speed. The harder part is keeping the work reviewable: making sure the codebase stays understandable and decisions behind the work remain visible to the people who maintain it.
The sprint is for teams that want help shipping useful changes through a governed workflow, without needing to commit to a permanent platform or operating-model change upfront.
Unreviewable speed creates unclear intent, hidden decisions, weak handover, and maintenance work that lands later.
The sprint keeps evidence, acceptance criteria, verification, and reviewer context close to the work.
What this is
Useful software changes are planned, implemented, reviewed, verified, and handed over through a controlled AI-assisted delivery workflow.
The output is ordinary engineering work with better review evidence attached, not a platform purchase or a permanent adoption commitment.
Work is packaged for your existing review and merge path.
The sprint starts from changes specific enough to verify and hand over.
Reviewers see what was checked, what remains uncertain, and where judgement is still required.
Maintainers get context for the work, not just a diff.
Best fit
A working or mostly working app where normal PR review is possible.
A few features, fixes, workflow improvements, or refactors you already know you want.
Someone on your side can review, ask questions, and decide whether to merge.
The first engagement should avoid sensitive production, auth, payment, secret, and data-migration boundaries.
You want AI-assisted speed without losing evidence, handover context, or engineering judgement.
Not a fit
A clean first sprint protects both sides. If the request is too vague, too urgent, or too high-risk, the safer next step is a readiness conversation or a narrower assessment first.
Requests like "sort the app out" need diagnosis before delivery.
Incidents and emergency recovery need a different operating mode.
SDF does not replace customer approval, merge ownership, or product judgement.
Auth, payments, secrets, broad data operations, and compliance-heavy areas are not ideal first proof scope.
Large rewrites and undefined strategy work are too wide for a bounded proof sprint.
What you get
The main output is working software plus the evidence needed to review, maintain, and build on it. You also get a clear recommendation on whether SDF should become part of your ongoing workflow.
Bounded implementation work prepared for your review path.
What the change was expected to satisfy.
Checks run, results observed, and anything blocked or unavailable.
What changed, what did not, and where reviewer judgement still matters.
Maintainer context for review, merge, and follow-up.
Where the sprint reveals governance gaps or a better ongoing workflow path.
How it works
Tell us the product changes, repo shape, stack, review owner, timing, and any boundaries you already know.
We agree whether the sprint is bounded enough to deliver safely and how review/merge decisions stay with your team.
The work is handed over with acceptance criteria, verification, risk notes, and reviewer context.
Apply
Use this when you already have a few clear software changes in mind and want to test governed AI-assisted delivery on real work.
The first filter is fit: bounded scope, a review owner, low-to-medium risk, and a clean handover path.
This is a manual, bounded delivery offer. It does not connect to GitHub, scan private repositories, approve, merge, deploy, repair, or enable hosted enforcement. Your team keeps review and merge control.