What We Do
We build.
We design.
We ship.
Three services. One standard. Every project is built by the people who scoped it — no account managers, no junior handoffs.
Start a projectWeb Development
Engineered for performance. Built to last.
We build fast, accessible, and scalable web applications using modern tooling. Our stack is opinionated — we use what we know works at production scale — and we own the full delivery from architecture to deployment.
Every project starts with a technical discovery: data models, API contracts, caching strategy, and CI/CD pipeline before a single UI component exists. This upfront investment is what makes the build phase clean.
What's included
- Full-stack web application
- Database design & migrations
- REST or GraphQL API
- Authentication & authorisation
- Deployment pipeline (Vercel / Railway / AWS)
- Performance audit & Core Web Vitals
- Technical documentation
Tech stack
UI/UX Design
Interfaces that feel inevitable.
Design is not decoration. A well-designed interface reduces cognitive load, increases conversion, and builds trust. We approach UI/UX as an engineering discipline — systematic, measurable, and iterable.
We design in Figma with production-ready specifications. Every component is designed for states (hover, focus, error, loading, empty), every spacing value is from a defined grid, and every typographic choice is intentional. Handoff is a Figma file your engineers can open and immediately understand.
What's included
- User research & journey mapping
- Information architecture
- Wireframes & interactive prototypes
- Design system (components + tokens)
- Figma file with dev specifications
- Accessibility audit (WCAG AA)
- Motion design guidelines
Tech stack
AI / ML Solutions
Intelligence integrated, not bolted on.
AI is most valuable when it's invisible — embedded cleanly in a product flow, not a chatbot widget stapled to the side. We build ML pipelines, language model integrations, and intelligent automation that solve specific, measurable business problems.
We start by defining the problem clearly: what decision is being automated? What does success look like? From there we prototype, measure, and iterate. We don't ship AI until it's reliable.
What's included
- Problem definition & scoping
- Model selection or fine-tuning
- Data pipeline design
- LLM integration (OpenAI / Anthropic / local)
- Vector database setup (Pinecone / pgvector)
- Evaluation framework
- Production MLOps pipeline
Tech stack
Ready to start?
Tell us what
you're building.
We'll scope the right engagement — sometimes one service, sometimes all three. No discovery calls with someone who won't build it.
Get in touch