MVP Development
First version shipped fast — with the structure to survive what comes next.
Speed matters at the MVP stage, but not at the expense of basic structure. We have seen too many 'fast' MVPs that took a year to refactor because authentication was an afterthought, the data model was flat, and nobody wrote a test. Our MVPs are lean in scope, not in engineering quality.
We work with you to decide what is actually core — the one flow a user must complete to validate the thesis — and we cut everything else. Then we build that clearly, deploy it on infrastructure you own, and hand it back in a state your team or any competent engineer can extend. No orphaned repositories, no undocumented side projects.
What this covers
Discovery and scope reduction: identifying the smallest build that proves the core assumption
UX flow and high-fidelity prototype validated before engineering begins
Full-stack build covering authentication, core data model and the key user journey
Sensible defaults for structure: folder layout, naming conventions, environment management
Deployment to a live, shareable URL within agreed timeline
Basic analytics and error tracking in place at launch
A written architecture note so the next engineer understands every decision
Deliverables
- A live, user-testable product accessible via a real domain
- Source code in your repository with a setup and deployment guide
- An architecture decision record covering the key trade-offs made
- A prioritised 'v2' notes document identifying what was deliberately deferred
What we build with
Minimum Viable Product
An MVP is not a stripped-down version of the product you eventually want to build. It is the smallest thing that lets you test a specific assumption with real users — and the assumption is what matters, not the feature count. A login screen and a waitlist form is an MVP if the assumption being tested is demand. A single, polished user journey with no admin panel is an MVP if the assumption being tested is whether users can complete a meaningful task without support.
What we mean by 'viable' is: it works reliably enough that a user's failure to engage tells you something about the product, not about the bugs. Viable does not mean production-hardened. It means the core path is functional, the error states are handled, and the data is captured. Everything else — scalability, admin tooling, advanced features — is explicitly deferred and documented, not forgotten.
How we build an MVP
The MVP lifecycle is deliberately compressed. Each stage is designed to validate before building, so the engineering effort is concentrated on the smallest surface that answers the central question.
- Hypothesis articulation: what specific user behaviour will confirm or deny the assumption?
- Feature audit: every proposed feature evaluated against 'does this test the hypothesis?'
- Explicit deferral list: what we are not building and why, agreed in writing
- Success criteria: measurable threshold that determines what the MVP result means
- User flow diagram covering the critical path from entry point to value moment
- High-fidelity prototype of the core journey in Figma
- Usability test with three to five target users — enough to surface the obvious failures
- Iteration on the flow based on test findings before engineering begins
- Database schema for the core domain objects, designed for extension not just the MVP
- Authentication and session management wired before any feature work
- Core user journey built as a vertical slice: UI through to database
- Basic error handling on every API route — no unhandled promise rejections or 500 responses
- Environment variables and secrets management from the first commit, not the last
- Error tracking via Sentry: every unhandled exception surfaces in a dashboard, not a support email
- Product analytics via PostHog: funnel events on the critical path so drop-off is measurable
- Deployment to a live URL on infrastructure the client owns — not a localhost tunnel
- Smoke test of the complete critical path in the production environment before sharing
Assumption mapping and scope reduction
Before touching Figma or a code editor, we identify the one hypothesis the MVP must test and eliminate every feature that does not serve that test directly. The hardest work in an MVP engagement is saying no — including to ideas that are genuinely good but belong in version two.
UX flow and prototype validation
A clickable prototype in Figma tests the user journey before a single line of code is written. User feedback at this stage is cheap; the same feedback after engineering is expensive. We prototype the critical path only — not edge cases, not the full UI system.
Vertical slice build
We build one end-to-end flow at a time — schema, API, UI — not all the data models first, then all the API routes, then all the UI. This keeps a working, deployable product available throughout the build and surfaces integration issues early.
Instrumentation and launch
An MVP that cannot tell you what users did is not a learning tool — it is a guess delivered as software. We wire analytics and error tracking before the first external user touches the product.
How we'd choose
There's rarely one right answer — these are the trade-offs we weigh before recommending an approach.
Custom build vs no-code / low-code vs off-the-shelf
At the MVP stage, the question is not always 'how do we build this?' — sometimes it is 'should we build this at all?' No-code tools and off-the-shelf products can validate assumptions faster than custom code. The risk is that the tool ceiling arrives before you have learned enough to justify a rebuild.
| Criterion | Custom build (code) | No-code / low-code (Bubble, Webflow + Airtable, Retool) | Off-the-shelf SaaS |
|---|---|---|---|
| Time to first user | Four to eight weeks for a focused, single-flow MVP with a senior team | One to three weeks for standard CRUD and form-based workflows with no custom logic | Same day to one week — configure, not build; onboarding is the time cost |
| Customisation ceiling | None — any capability expressible in code is achievable | Medium — complex business logic, custom integrations, and performance-critical paths hit platform limits quickly | Low — you are constrained to what the product allows; workarounds accumulate into technical debt |
| Data ownership | Full — schema, storage, and backups are yours | Partial — data lives in the no-code platform or a connected external store; migration is non-trivial | Vendor-controlled — export may be limited; migration cost is high if you leave |
| Migration cost when outgrown | Low — the codebase is the foundation for the next version; no migration required | High — business logic encoded in visual tools must be reverse-engineered and rewritten | Medium to high — data migration plus rebuilding workflows the SaaS handled |
| Best fit | Any assumption that requires unique UX, custom data models, or integrations the no-code tier cannot reach | Internal tools, marketplace mockups, form-heavy workflows, or where the founder needs to iterate without engineering support | Validating demand for a category before building into it — use Typeform, Calendly, or Stripe before building booking or billing |
MVP anti-patterns we actively prevent
Scope creep disguised as MVP features
The admin panel, the team management system, the API for a third-party integration that no user has requested yet, and the multi-currency support for a market the product has not reached — each is defensible individually, and together they turn an eight-week MVP into a six-month build. The assumption the MVP was supposed to test is now buried under features designed to impress an investor, not validate a user.
Lock scope in writing before engineering begins. Every new feature request during the build is evaluated against the stated hypothesis and defaulted to the deferral list unless there is a clear argument that the MVP cannot test the assumption without it. The deferral list is a deliverable — it is the v2 backlog.
Flat data model that cannot survive version two
An MVP built with everything in one table, IDs stored as comma-separated strings, or relationships handled in application code produces data that is impossible to query correctly, impossible to migrate without downtime, and impossible to hand to a second engineer without a long explanation. The 'fast' data model is not faster if it requires a full rewrite to add the second feature.
Spend two hours designing the core schema correctly before the first migration. Normalise relationships into foreign keys, use UUIDs for primary keys from the start, and add created_at and updated_at timestamps to every table. These are not premature optimisations — they are the minimum structure that allows the product to be extended without a rewrite. Write the schema as a migration, not as CREATE TABLE statements in a README.
No error tracking, therefore no signal
An MVP launched without error tracking is flying blind. When users drop off, there is no way to distinguish between 'they do not want this' and 'they hit a bug that broke the flow'. Both look identical in the analytics: the user left. Decisions made on this ambiguous signal are not validated learnings — they are guesses.
Wire Sentry before the first external user. Configure it to capture unhandled errors, failed API calls, and — critically — error metadata that identifies which user and which flow the error occurred in. A three-day sprint of bug fixing based on Sentry data after the first user cohort is normal and expected; it is not a failure of the MVP process.
Common questions
How fast is 'fast' — what is a realistic MVP timeline?
A well-scoped MVP with a single core flow typically takes four to eight weeks from kickoff to a live, user-testable product. Timeline depends heavily on scope discipline — the more we cut in the planning phase, the faster we ship. We set a fixed scope before the first line of code.
What happens after the MVP — can you keep building?
Yes. Many clients move straight into a phase-two engagement. Because we own both the code and the architecture decisions, there is no ramp-up cost. If you take it to a different team, the documentation we produce is written specifically to make that hand-off clean.
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