Your AI-built prototype works. Now it has to survive users.
You built a working app with an AI builder or a coding assistant, and it is good enough that people want to use it. A senior pod takes it over, fixes what blocks a launch and ships it, in your repository, at $3,000–$7,500 per engineer per month, with a trial before you commit.
A demo has to work once. A product has to work every time.
AI tools are very good at getting to a first version. The first version proves the idea, wins the meeting and sometimes signs the first customer. What it was never asked to do is keep one customer’s data away from another, recover from a failed payment, tell you it is down, or stay affordable at a thousand users.
None of that means the prototype was a mistake. It means the next stretch is engineering work of a different kind, and it is cheaper to do before launch than after the first incident. We wrote up the same pattern for AI pilots in the five gates between a pilot and production.
Eight things we check before anything else.
In the order they tend to block a launch. You get the findings in writing, ranked, whether or not you carry on with us.
Who can see and change what
Sign-in, sessions, roles and row-level access. We test whether one customer can read another customer's data, because that is the failure that ends a launch.
Secrets and keys
API keys in client code, in the repository history or in a shared environment file. Anything exposed is rotated before anything else happens.
The data model
Whether the schema will survive real usage, whether changes go through migrations, and whether there is a backup anyone has restored from.
What happens when something fails
Error handling, retries, timeouts and what the user sees. Then logging and alerts, so a failure reaches an engineer before it reaches a customer.
Tests and a release path
Enough automated tests around the money and data paths to change the code safely, a staging environment, and a deploy that can be rolled back.
What it costs to run
Model and API spend per user, rate limits and caps. A prototype that is cheap for ten users can be expensive at a thousand.
Any AI features inside it
If the product itself calls a model, it needs an eval set, traces and guardrails before it runs unattended. Those are the five production gates.
Who owns it
The repository, the cloud accounts, the domain and the licences should all sit with your company. We move anything that does not.
The call we make in week one.
Every takeover lands in one of three groups. You hear which, and why, before you have committed to more than the trial.
Keep and harden
The data model is sound, the code is readable and the gaps are the usual ones: access rules, tests, error handling, deployment.
Most of the work is additive. This is the common case when the prototype was built carefully.
Repair
The product works but one layer does not hold: usually the schema, the auth model or a tangle of duplicated logic.
We replace that layer and keep the rest. Users see the same product.
Rebuild
Fixing it would cost more than writing it again: no consistent structure, no safe way to change one part without breaking another.
We say so in week one, with the reasoning written down. The prototype becomes the specification, which is still worth a great deal.
Fixes from the first week, a decision by the fourth.
Read it, rank it, start fixing
We get access, read the codebase and give you a written list of what blocks a launch, in priority order. First pull requests land by Day 5.
The trial
The pod works through the list in your repository. At the end of the trial you have seen the work and decide whether to continue. There is no lock-in.
Close the list, ship
Remaining blockers closed, staging and rollback in place, then a launch you can undo. How long this takes depends on what the first week found.
Keep, hand over or operate
Keep the pod for the roadmap, take a documented handover to your own engineers, or have us run the AI parts on a monthly retainer.
Priced as a team, by the month.
There is no fixed price for a codebase nobody has read yet. There is a published monthly rate, and a trial that shows you the work first.
By role and seniority. Two engineers come to $6,000–$15,000 a month. 2–4 week trial, no lock-in, 30-day notice. First pull requests by Day 5.
Only one workflow to fix?
A single automation, scoped, built and deployed for a fixed price. A team would be the wrong size.
AI features that need running after launch?
Monitoring, eval regressions and model-change response on a retainer. See what the retainer covers.
What we can show you, and what we cannot yet.
Joining a codebase that already exists
A Series B HR-tech company embedded a three-person Gigabit pod in a live product with a seven-engineer team. First pull requests in 5 days, and sprint velocity went from 34 to 78 points. Read the case study.
Shipping a first product to paying customers
A logistics SaaS went from pitch deck to 14 paying customers in 11 weeks for $48,000, with zero critical bugs at launch and 78% automated test coverage. Read the case study.
We do not yet have a published case study of taking over an app that was built with an AI builder. The two above show the parts it is made of: working inside someone else’s code, and getting a new product to a launch that holds. When the first takeover is public it will be linked here.
We are not a clean-up crew that bills by the hour to tidy code forever. The review ends in a ranked list, the trial ends in a decision, and the work ends in a launch or an honest recommendation to rebuild.
Questions founders ask first
Can an app built with AI tools go to production?
Often, yes. A prototype built with an AI app builder or a coding assistant usually proves the idea and the interface. What it tends to lack is what a demo never needs: access control that holds, tests, error handling, monitoring, a rollback path and a cost ceiling. Those can be added to a sound codebase. The first week tells you whether yours is one.
How much does it cost to make an AI-built prototype production-ready?
Gigabit prices this as an embedded team: $3,000–$7,500 per engineer per month, by role and seniority, with a 2–4 week trial and no lock-in. Two engineers come to $6,000–$15,000 a month. The total depends on how much the first-week review finds, which is why the trial comes before any longer commitment.
Will you rewrite my app or keep the code?
We keep what holds. In the first week we put the codebase into one of three groups: keep and harden, repair one layer, or rebuild. If the answer is rebuild we say so then, in writing, with the reasons. We do not start a rewrite quietly.
Which tools' output do you work with?
Any that produce a normal codebase you can export: apps started in Lovable, Bolt, Replit or v0, and code written with Cursor, Claude Code, Copilot or similar assistants. What matters is the stack underneath, typically React or Next.js with a hosted database, and whether you can give us the repository.
How long does it take?
First pull requests land by Day 5 and the trial runs two to four weeks. The time to launch depends on what the review finds, so we give the estimate after week one, against a written list, and not before we have read the code.
Who owns the code and the accounts?
You do. The work happens in your repository and your cloud accounts. Part of the review is checking that the repository, hosting, domain and third-party accounts sit with your company and not with a personal login, and moving them if they do not.
What if I only need one automation fixed, not a whole product?
Then a team is the wrong size. A single, well-defined workflow is an AI Quick Win Sprint: $8,000 fixed, 3 weeks, built and deployed.
Find out what stands between your prototype and a launch.
Tell us what you built and what it runs on. We'll read it, rank what blocks a launch, and start fixing in the first week.