Build an AI MVP that puts your idea to the test.
In 6–8 weeks, we help you turn a defined opportunity into a first version people can use — built around the essential features and a solid foundation for what comes next.
When is the right time to build an MVP?
Good fit
The product decision is already clear
- A customer problem and target user are defined.
- A product owner can make scope decisions during delivery.
- You have access to the data, integrations and stakeholders the release depends on.
Bring to the brief
The constraints worth scoping early
- The one outcome the first release must prove.
- Known users, integrations, data sensitivity and launch timing.
- The commercial or operational decision that follows the MVP.
Receive
A bounded first release
- A build scope and architecture shaped around the first useful outcome.
- Visible increments, a documented release path and handover materials.
- A clear recommendation for what should happen after launch.
Deliverables
- UI/UX design & interactive Figma prototype
- Production-ready frontend (React / Next.js)
- Backend API (Python / Node.js / FastAPI)
- AI integration layer (LLM, agents, RAG if needed)
- Cloud infrastructure & CI/CD pipeline (AWS / GCP / Azure)
- Analytics & monitoring setup (Datadog / Grafana)
- Documentation & developer handover pack
- Post-launch partnership for iterative product improvement
The 6–8 week process
Discovery & Scoping
Stakeholder interviews, problem definition, technical architecture, sprint plan.
Design & Architecture
UI/UX wireframes, system design, data model, API contracts, AI architecture.
Build Sprints
Weekly demo-ready increments. Frontend + backend + AI in parallel. Async updates daily.
QA, Security & Deploy
End-to-end QA, security review, load testing, cloud deployment, monitoring setup.
Shipped in production
Blind CV Generator
A private CV-anonymisation platform designed to remove selected personal characteristics before later processing. The project combined product delivery with EU data-residency controls and a documented human-review workflow.
Read the case study →Technology we deliver on
We choose the right tool for your product — not the most fashionable one. Every stack decision is documented and justified.
Frontend
Backend
Platform
AI Layer
Cloud
Monitoring
Engagement model
Time & Materials with a ceiling agreed before sprint one. You always know the maximum — and unused time is never charged.
Typical engagement
If the problem is earlier or the product already exists
Uncertain opportunity
Start with an AI Discovery Sprint
Use a short decision phase when the data, user need or best technical path is still unclear.
Plan a discovery sprint →Existing product
Add AI to a working workflow
For a product that needs RAG, decision support or a reliable AI integration rather than a new build.
Explore AI workflow integration →Ready to scope your MVP?
Share the problem, known constraints and the first outcome worth proving. We will use that context to decide whether an MVP is the right delivery path.