What “production-grade” means here
A prototype is software written to be thrown away; an MVP is software written to be extended.
The distinction is not cosmetic. A production-grade MVP is typed, tested where behaviour actually matters, and deployed through continuous integration. Its data model is chosen for the product you intend to have in three years, not the demo you need next month. That costs a little more in weeks one and two and saves a rebuild in month nine.
The most expensive outcome in early-stage software is not a slow build. It is a fast build that has to be written twice, because the first version could not absorb its own success.
A data model that cannot express a second customer type, or a deployment nobody can reproduce, is invisible at launch and terminal six months afterwards.
Fixed scope and milestone-based delivery
The scope and the milestones are agreed before the build starts, normally coming out of a Discovery Sprint.
Each milestone ends in a working demo you can use yourself, not a status report or a screenshot. Payment is released against delivered and accepted work. There is no open-ended hourly billing and no time-and-materials drift, so the number you budget at kickoff is the number you pay at launch — unless you change the scope yourself.
Where a build lands inside the 8-to-12-week range depends almost entirely on how settled the scope was on day one, which is why the sprint exists. When something needs to change mid-build — and it usually does — the new requirement is not absorbed silently and paid for in quality. It is priced, and you decide whether it is worth the weeks it costs.
How the eight to twelve weeks are spent
The spine of the product
The data model, the authentication and account structure, the deployment pipeline, and whichever integration or AI component carries the most technical risk.
The features the scope prioritised
Each one landing in an environment you can use rather than accumulating on a branch.
Hardening, not features
Error handling, observability, performance under realistic data volumes, and the operational details that separate software that demos well from software that runs.
Nothing is left as “we will sort that out after launch”, because after launch is when the founder is busiest.
Working software at every milestone is a control, not a reporting convention. A founder who can use the product at week four can correct a misunderstanding that would otherwise survive to week ten. Corrections at week four are cheap.
The stack, and why it is chosen per product
Bluesphere Digital is deliberately stack-agnostic.
The default starting point for a SaaS MVP is TypeScript with React and Next.js on the front end, Node.js or Python on the back end, PostgreSQL for data, and deployment to Vercel or AWS behind CI. That combination reaches market fastest and is the easiest stack to hire into later.
If a product already runs on something else, we work in it rather than proposing a rewrite — and where a founder has an existing team, the stack that team can maintain outranks the stack that would be marginally faster to build in.
Code and IP ownership
You own 100% of the code and intellectual property from day one — not at handover, and not after final payment.
Source code, designs, documentation and infrastructure configuration are yours, and you have full repository access throughout the build, so you can read every commit as it lands. There is no proprietary framework to license from us afterwards and nothing that makes moving to another engineering team expensive. If an engagement ends early, everything built to that point is still yours.
Equity participation is available where there is genuine alignment, but it is optional and always sits alongside fees rather than replacing them.
A studio that holds the codebase hostage has every incentive to be hard to leave. Removing that incentive means the work has to be good enough to keep you, rather than sticky enough to trap you.
What happens at launch
Launch is a milestone, not an ending.
Some founders take the product in-house from there, which the ownership terms are designed to make straightforward. Others continue on an ongoing retainer for continuous development, or keep fractional CTO support so the person making architecture decisions remains the person who wrote the architecture.
Products with AI features often continue into applied AI work, where the evaluation set built during the MVP becomes the thing that keeps accuracy honest as usage grows. In every case the handover is uneventful, because repositories, infrastructure and documentation were yours throughout.
That is the test we hold the arrangement to: if leaving is hard, something was set up wrong.