Keep the momentum of the prototype. Add the engineering required for production.
The fact that your first version was built quickly is not a problem; it is an advantage if it helped you prove the product. Our job is to preserve that learning, understand the code and business logic already there, and add the production foundation real users will depend on. We do not begin with a ceremonial rewrite. We begin with evidence: what works, what is fragile, what is missing, and what the next version actually needs.
Take my prototype to production ↗Launchable MVP
Take a validated AI-built prototype and engineer the smallest reliable version that real users can begin using.
The published starting price is for a tightly scoped MVP. Final scope depends on workflows, roles, integrations, data, compliance, performance and infrastructure. The first step is always to understand what you already have before estimating what production requires.
Discuss this engagement →The prototype keeps its product learning. The application gains an engineering backbone.
A Launchable MVP is deliberately smaller than a fully mature enterprise platform, but it should be something you can operate responsibly for real users within its agreed scope.
A clear view of which parts of the existing prototype are sound, which need work and where the real technical risk lives.
The agreed core workflows engineered into a maintainable structure suitable for continued development.
Authentication, role boundaries, data integrity and server-side business logic appropriate to the product.
Critical-path tests and a repeatable deployment path so future changes can be made with evidence.
Production configuration, logging, basic monitoring and the operational basics needed to support real use.
A prioritized roadmap for deeper features, scale, integrations, enterprise controls or continuous engineering.
The question is not whether AI wrote the code. The question is whether the system is ready for real use.
A prototype is optimized to answer a product question: can this experience work, will users understand it, does the workflow create value? Production software has additional obligations. It has to protect data, enforce permissions consistently, handle failures, survive bad inputs, support releases, expose useful logs, recover from incidents and remain understandable when the original builder is no longer in the room.
We start by separating product value from implementation risk. A clean interface, useful workflow or well-structured module should not be discarded simply because it came from an AI-assisted build. At the same time, duplicated logic, weak authorization, secrets in the wrong place, fragile database assumptions or untested critical paths should not survive simply because the demo works.
The production plan therefore follows a keep / refactor / replace decision for each important part of the system. That gives the team a controlled backlog and lets us strengthen the application in the order that matters most to the launch.
Capabilities around the problem, not a fixed stack.
We select the technology and team shape around the business outcome, existing environment and production requirements.
From promising prototype to owned production system.
Assess the product and codebase
We run the key user journeys, map the repository, dependencies, data model, APIs, integrations, environments and deployment path, and identify the business-critical flows that must not break.
Decide what to keep, refactor and replace
We preserve product learning and sound implementation. Risky shortcuts are isolated and prioritized. The output is a production backlog based on impact rather than a blanket rewrite.
Build the production foundation
We move critical logic to the right layer, strengthen the data model, authentication, authorization, APIs, secrets management, error handling and environments, and introduce the tests needed for safe change.
Make the application operable
CI/CD, logging, monitoring, backups, alerting and recovery practices are added so the product can be released and supported without relying on heroics.
Launch with ownership
We deploy the MVP, verify the critical paths, document the operating model, and leave you with a clear backlog and a team that understands how to continue the product after launch.
A good fit when a successful demo is becoming a real responsibility.
Questions buyers usually ask.
Will you rewrite the whole application?+
Not unless the evidence says that is the safest and most economical path. We prefer selective engineering: keep good product and code, refactor structural weaknesses, and replace only the parts that create unacceptable risk or constrain the future product.
Can you take over something built by a founder or nontraditional developer?+
Yes. The first phase is designed for exactly that situation. We reconstruct how the product works, capture the business logic, and create technical ownership before making high-risk changes.
What usually changes before launch?+
Common areas include authentication and authorization, server-side business logic, data modeling, validation, secrets, error handling, APIs, tests, logging, deployment, backups, performance and security boundaries.
Does “from £6,000” mean every MVP fits that budget?+
No. It is a transparent entry price for a tightly scoped launchable MVP. Multi-role products, complex integrations, regulated data, advanced agents, high-scale infrastructure or broader workflows move into a larger production scope.
Can you stay with the product after launch?+
Yes. Continuous Product Engineering is the natural next stage when you want the same team context to carry into features, support, reliability, integrations and ongoing AI work.
You already did the hardest early work: you proved the idea.
Let us keep that momentum, protect what is valuable in the prototype and engineer the product that real users can rely on.