Hire developers who work while you sleep.
A supervised agent workforce runs your backlog 24/7, a human squad handles the judgement calls, and you decide where the line between them sits. We will also build the whole setup inside your own accounts.
Humans, humans with AI, or agents that never clock off.
Most partners sell you one delivery model. We let you pick per project — and change your mind at any sprint boundary.
The squad above, allowed to use model assistance for the parts of the job it is genuinely good at — tests, migrations, boilerplate, review passes. You set the policy, and we can prove what was used where.
- Coverage
- Your working hours, agents pick up the queue overnight
- Who does the work
- Human writes and reviews, AI drafts
- AI policy
- Scoped by you, per repository
- Working day
- 8–9 hrs/day + overnight queue
What you get
- Written AI usage policy, agreed before sprint one
- Per-repository allow and deny list
- Provenance log of model-assisted changes
- No client code or data used for training
Best for
Teams that want the throughput without giving up human judgement on architecture and review.
Talk through this modelNot sure which one? Start human, add agents to the parts of the backlog that suit them, and turn them off again if it does not earn its place. The contract does not lock you into a model.
What the agents did while your team was asleep.
An agent fleet runs against your repositories around the clock. Every change arrives as a reviewed pull request — never a direct push — and anything ambiguous stops and waits for a human.
Overnight queue
22:00 — 09:00- 02:00Dependency upgrade opened, tests greenmerged
- 03:40Flaky integration test isolated and fixedreview
- 05:15Nightly migration batch 7 of 12 completerunning
- 06:50Accessibility fix: 14 unlabelled icon buttonsreview
- 08:05Escalated to human squad — schema ambiguityescalated
We will build the agent setup for your own team
You do not have to rent the workforce from us. We stand the fleet up inside your accounts, write the policy your security team will sign, and train your engineers to run it — then step back.
Agent platform setup
We stand the fleet up inside your accounts — orchestration, sandboxes, secrets, permissions and spend limits — and hand you the keys.
Guardrails and policy
An AI usage policy your security team will sign: what agents may touch, what they may never touch, and how every change is traced.
Evaluation harness
Before an agent is trusted with a task type, it has to pass a suite built from your own codebase and your own past tickets.
Team enablement
Your engineers learn to run, review and extend the fleet, so the capability stays after we leave.
The questions your security team will ask, answered first.
Agentic delivery is only worth buying if it survives a review. These are the boundaries every Codestarn fleet runs inside.
You set the AI policy
Some clients want model assistance everywhere. Some forbid it entirely, and we will sign that. Most sit in between and scope it per repository. The policy is written down before sprint one and it is yours to change.
Nothing merges unreviewed
An agent never pushes to your default branch. Work arrives as a pull request, a Codestarn engineer reviews it first, and your team merges it. The review pass is priced into every agent seat.
Scoped permissions, hard stops
Agents get least-privilege credentials, a deny list of paths they may never touch, and spend limits. Infrastructure, secrets and payment code sit outside the boundary unless you move them in.
Your code is not training data
We run on zero-retention endpoints. Your repositories, tickets and data are never used to train a model, and we will put that in the contract.
It escalates instead of guessing
When a task is ambiguous the agent stops and raises it to the human squad rather than inventing an answer. Escalation rate is a number we report on, not one we hide.
Provenance you can audit
Every model-assisted change is logged — which agent, which task, which review. If a regulator or an acquirer asks how a line of code came to exist, there is an answer.
If your procurement process needs this as a document rather than a web page, email info@codestarn.com and we will send the policy pack.
Turning your questions into confidence.
The things clients ask before they sign. If yours is not here, write to us and you will get a straight answer.
Yes. Our agent workforce runs against your repositories around the clock — triage, fixes, tests, dependency upgrades and long migrations. Every change arrives as a pull request that a Codestarn engineer has already reviewed, and anything ambiguous escalates to a human squad instead of being guessed at.
Yes, and we will sign it. The AI policy is yours: off entirely, scoped per repository, or agent-first with human review. It is agreed in writing before sprint one, it can change at any sprint boundary, and every model-assisted change is logged so you can audit what was used where.
That is a service in its own right. We stand the fleet up inside your accounts — orchestration, sandboxes, secrets, permissions and spend limits — write the usage policy your security team will sign, build an evaluation harness from your own codebase, train your engineers to run it, and then step back.
No. We run on zero-retention endpoints, and your repositories, tickets and data are never used for training. That commitment goes in the contract alongside the NDA.
Every project runs through code review, automated tests and a security pass before release. We follow OWASP guidance, sign NDAs before the first call, keep source in your repositories and can work inside your VPN or cloud account when compliance requires it.
We start from the job the user is doing, not the screen. Flows are prototyped in Figma and tested with real users before a line of production code is written, and we hold every build to accessibility contrast, keyboard and reduced-motion standards.
Over a decade of delivery, 300+ engineers and a model built around your time zone. You get a dedicated team with named people, not a rotating pool, and a single point of contact who is accountable for the roadmap.
Web and mobile application development, custom enterprise software, AI and automation, data engineering and analytics, cloud migration and DevOps, plus staff augmentation and full offshore development centres.
Yes. Support plans cover monitoring, incident response with agreed SLAs, security patching, dependency upgrades and a monthly improvement budget so the product keeps moving after launch.
Two-week sprints with a demo at the end of each one. Day-to-day communication happens in your Slack or Teams, work is tracked in your Jira or Linear, and you get a written status note every Friday.
A focused MVP typically lands in 10–14 weeks; a full product with back office and integrations runs 4–7 months. We quote fixed scope after a paid discovery sprint, or a monthly team rate if the roadmap is still moving.
Let's talk
About your next project
Tell us where you are stuck. You will get a written proposal with scope, timeline and cost within three working days — no obligation attached.