Our Process
A spec-driven loop, accelerated by AI.
Move the product in small, testable steps. On embedded work, we also introduce the specifications, review rules, tests, and release gates your team can keep using.
How a build moves
Six phases run as a loop, not a one-way line. Each release feeds the next:
1Understand
Goals, users, constraints and success metrics. We align on the problem before reaching for a solution.
2Shape
Architecture, specs and contracts, and AI use-case mapping. The important decisions are made before code is written.
3Prototype
A testable slice, fast, to validate UX, feasibility and the AI use case before committing real budget.
4Build
AI-accelerated implementation behind quality gates: lint, type-check, tests and human review on every change.
5Launch
App-store readiness, release, analytics and monitoring, prepared for release with the right quality gates in place.
6Improve
Evals, observability and iteration. Learnings feed back into specs and prompts; new languages at much lower marginal cost.
↻ Evals & observability feed learnings back into specs and prompts.
Architecture is decided in Shape, before framework momentum makes the decision for you. We compare Flutter, React Native, and native against the product's hardest constraints.
AI speeds up the work. Engineering discipline keeps it safe.
We use AI throughout the build process, from prototyping and implementation to refactoring and documentation. But every production decision stays human-owned, reviewed, tested and maintainable. This is the opposite of vibe coding.
Architecture is decided by people before code is written; every pull request is reviewed to the same standard as any shipped app. More on AI-assisted engineering.
What you get
- Working increments, something real at the end of every phase, not a big-bang reveal.
- Specifications before generation, acceptance criteria and architecture decisions that tell AI what good looks like.
- Review at the seams, types, native boundaries, APIs, and runtime behaviour checked before release.
- On embedded builds, a delivery system you own, code, repository rules, tests, release gates, and a defined handover. No private-tooling dependency.
- Multilingual by default, additional languages at much lower marginal cost, QA-validated.
- Privacy-respecting, EU-hostable, with zero-retention model options when customer data is involved.
- Senior accountability, experienced engineers own the architecture and review every release.
Ways to work together
Most engagements start small and grow with confidence. Each one produces something concrete you can act on.
Discovery sprint
→ Evidence for the next decision
Scope assumptions, architecture options, and an AI feasibility review. If building is next, we add the cost and timeline estimate.
Discovery sprint →Prototype sprint
→ A tested product assumption
A clickable or working slice that tests the core product value and the AI use case with real users.
Prototype sprint →MVP support
→ A first shippable product
A focused first version, built AI-first. We help your team get the core product ready to ship.
MVP support →Embedded product and AI transition
→ A shipped product change, client-owned delivery practices your team can keep using, and a defined handover.
We deliver a named product change alongside your team while introducing the specifications, review rules, tests, and release gates behind it.
Embedded product and AI transition →