Process & engagement
How we work
The same process whether we are building something new, improving something that exists, or keeping a live product healthy.
From idea to production
How a product actually gets made
Five stages. You always know which one you are in, and what comes out of it.
- 01
Understand
The business, the users and the problem — before anything gets designed. We would rather challenge the brief early than build the wrong thing well.
OutputA shared understanding of the problem
- 02
Define
The problem becomes a product and technical plan: scope, architecture, milestones, and an explicit list of what we are not building yet.
OutputScope, architecture and a plan
- 03
Build
Design and engineering in an AI-native workflow, shipping in reviewable increments you can open and use — not a demo at the end.
OutputWorking software, continuously
- 04
Launch
Deploy properly: monitoring, error tracking, releases and rollbacks. Launch is a process we run, not a date we hope for.
OutputA production release you can trust
- 05
Improve
Once real users arrive, the roadmap changes. We keep building against what actually happens, not what the original document assumed.
OutputA product that keeps getting better
AI-native engineering
AI across the lifecycle. Judgement stays with us.
The honest test of “AI-native” is whether taking the AI away would slow the team down. For us it would — it is in discovery, implementation, tests, review and docs every day. What it does not do is decide anything.
- 01
Discovery
Faster research, competitor teardowns and requirement drafts to argue with.
- 02
Architecture
Options explored quickly, then chosen deliberately by an engineer who will maintain it.
- 03
Development
Implementation against a plan a human already reviewed, in the codebase’s own conventions — not whatever the model prefers.
- 04
Testing
Wider coverage and edge cases surfaced earlier, plus evaluation sets for anything whose output is a judgement call.
- 05
Review
Every change read by a person who understands the system before it merges. AI helps write it; it never approves it.
- 06
Deployment
Repeatable releases, migrations and rollbacks — scripted rather than remembered.
- 07
Maintenance
Documentation that stays current, and faster diagnosis when something breaks in production.
What stays human
AI shortens the distance between a decision and a working implementation. It does not make the decision — and unreviewed generated code is how teams quietly accumulate the debt that cancels out the speed. That is why review is a stage here, not a courtesy.
- Product decisions and scope
- Architecture and technical trade-offs
- Code review and quality
- Security and reliability
- What we tell you, and when
- Final delivery and accountability
Where you can check it
Kevta, our own product, ships a Model Context Protocol server so an AI client can act on real boards through the same authorised API as the interface — with tokens, scoped permissions and user-facing docs, because it runs in production rather than in a demo.
See how Kevta does itYou will not find a “40% faster” claim on this page. We have not measured ours in a way we would be willing to defend, so we are not publishing one.
Ways to work with us
Full product build
You have an idea or an early version. We own discovery, design, engineering and launch, and stay on for what comes after.
Best when there is no in-house engineering team yet.
Defined scope
A specific piece of work with a clear outcome: a rebuild, a migration, a new module, an AI feature, a performance pass.
Best when you know exactly what you need built.
Ongoing engineering
Continuous development and maintenance on a live product: features, fixes, monitoring and the unglamorous upkeep.
Best when a product is in production and needs to keep moving.
Working alongside your team
Our engineers embedded in your process, your repositories and your standards, adding capacity without adding management overhead.
Best when you have a team but not enough of it.
How we actually run it
You see working software, not status reports
Progress is demonstrated in something you can open and use. If a week produced nothing you can try, we will say that too.
Scope is explicit, including what is out
Every plan lists what we are not building yet. Most project failures we have seen were disagreements about that list.
Human review on every change
AI assists implementation, tests and documentation. A person who understands the system reads every change before it merges.
Your accounts, your code
Repositories, cloud accounts and infrastructure are yours from day one, with documented handover. No lock-in through obscurity.
One team, one thread
You talk to the people building the product. Nothing is relayed through an account manager.
Operations are part of the build
Monitoring, error tracking, migrations and rollbacks are designed in, not added after the first incident.
Have a product in mind?
Tell us what you are trying to build. If we are the wrong fit, we will say so — and usually tell you what we would do instead.