Process shaped by the problem,
not by a template
Domain first. Then architecture. Then code the operators can run. Send the brief — estimate within 24 hours, no developer-pool framing.
Six steps, in this order
The sequence matters. Every step exists to make the next one cheaper, and the first one exists so the rest are solving the right problem.
Domain immersion
We study your operations, users, data flows and technical constraints before writing requirements. For an energy operator that means learning the protocols and what a wrong reading costs; for a broadcaster, the delivery specification a package has to pass. Industry context shapes every architectural decision that follows.
Requirements & risk analysis
We identify assumptions, dependencies and risks before implementation begins. Known risks are addressed in the architecture. Unknown risks are named early and tracked in the open, rather than absorbed silently into a delivery buffer.
Architecture & UX
We design the system structure, data model, integration contracts and interface logic together, because in data-heavy products they constrain each other. Prototypes are validated against real operator workflows before full implementation starts.
Incremental implementation
We deliver working, reviewable functionality in iterations. Each increment is tested, peer-reviewed and documented as it is built, so progress is something you can click through rather than a status report.
Quality assurance
Unit tests, integration tests, end-to-end scenarios and performance profiling run alongside implementation — not as a final phase. Integration tests cover database and protocol logic in containers, so they exercise the same paths production does.
Delivery & operations
CI/CD pipelines, staging environments, monitoring, structured logging and observability. We hand over systems that are maintainable from day one, with the documentation and runbooks a team needs to continue without us.
Engineering practices in every project
A partner, not a staffing line item
We take responsibility for the technical outcome, which means we will argue with a specification when we think it creates problems twelve months from now. Progress is shared early and often, including rough work, because feedback is cheapest before investment solidifies.
We are based in Poland, but our heart is in Kharkiv, Ukraine. We work Monday to Friday, 09:00–18:00 EET. Communication runs over email, Slack, Telegram, Google Meet or Zoom — whichever your team already lives in. Written decisions and assumptions stay in one place so nothing depends on someone remembering a call.
- One team accountable for architecture, implementation and delivery
- Trade-offs presented with their alternatives, not as conclusions
- Scope assumptions surfaced the moment they turn out to be wrong
- Working software reviewable in every iteration
- Documentation, diagrams and runbooks handed over as part of delivery
- Support continues after launch — the team that built it evolves it
The default stack
What we reach for unless the domain says otherwise. Testing and delivery are part of the stack, not an add-on to it.
- React 19 & TypeScript
- SSR with React Router
- Responsive layouts
- SCSS modules
- Sentry error reporting
- Node.js with Express / Fastify
- PostgreSQL, MongoDB, ClickHouse
- JWT & Passport authentication
- REST and WebSocket APIs
- Structured logging
- Jest unit tests
- Integration tests in Docker
- End-to-end scenarios
- Performance profiling
- Review on every change
- CI/CD quality gates
- Docker & Kubernetes
- Staging environments
- Monitoring & alerting
- Documentation & runbooks
Production AI as part of the engineering process
Assistants are useful when the repository already carries context, tests and review gates. We treat AI as a delivery amplifier — not a substitute for domain immersion or accountable architecture.
Production AI process
Rules, evals and review sit next to CI. Generated code is treated like any other contribution: typed, tested, and owned by the same engineers who sign the release.
Agent and context workflow
Clients get a repeatable context pack — repo conventions, domain constraints, and operator workflows — so assistants stay inside the system you actually run, instead of inventing a parallel stack.
Legacy modernization with AI
Brownfield work starts with characterisation tests and seam identification. Assistants accelerate mechanical migration; humans keep the cut-over and data integrity decisions.
See the process applied
The expertise and industry pages describe what this process produces — protocol integrations, operator dashboards, transcoding pipelines — at the level of detail an engineer can evaluate.
Working with the team
Send the brief. Estimate in 24 hours.
Describe the plant, the protocol stack and the deadline. We reply within one business day with a 24h estimate.