How We Build
Engineering products like we mean it.
Every product we ship follows the same uncompromising standards, from first wireframe to production CI/CD pipeline. Here's exactly how we work.
Philosophy
AI-First. Not AI-bolted-on.
Most teams add AI as a feature after the product is built. We design AI into the architecture from day one, choosing the right model for the job, engineering the prompt layer as a first-class system, and making sure the product gets smarter as it grows. Claude, GPT-4, and Gemini aren't integrations we bolt on. They're core infrastructure.
Model selection
We choose between Claude, GPT-4o, Gemini, and on-device Core ML based on latency, cost, privacy, and capability. Not hype.
Prompt engineering
Prompts are code. They're versioned, tested, and optimised the same way as any other system component.
On-device vs cloud
Where privacy or latency demands it, we run models on-device via Core ML or ONNX. No data ever leaves the user's device.
AI UX design
AI features are designed into the UX from the wireframe stage, not retrofitted. Loading states, fallbacks, and trust signals are first-class design decisions.
Design Process
From pixel to production.
We don't hand off a spec and disappear. Design and engineering work in parallel. Every component built in Figma is reflected 1:1 in code via design tokens and a shared component system.
Figma-first
Every screen, state, and edge case is designed before a line of code is written. Clickable prototypes validate flows before engineering begins.
Design system
We build a shared component library in Figma and code simultaneously. Tokens for colour, spacing, and typography ensure pixel-perfect parity.
Responsive & accessible
Mobile-first layouts, WCAG AA contrast ratios, and semantic HTML are non-negotiable defaults on every project.
Motion design
Micro-interactions and transitions are designed with intention, not sprinkled in. Every animation has a purpose and respects prefers-reduced-motion.
Software Lifecycle
Every project. Same rigour.
Discovery
Business goals, competitive landscape, user personas, and technical constraints. We define what success looks like before writing a single line of code.
Architecture
System design, data models, API contracts, and AI integration points. We map the full technical blueprint. Scalability and cost are considered upfront.
Design
Wireframes → high-fidelity UI → interactive prototypes → design system. Stakeholder sign-off before engineering starts.
Build
Two-week sprints, daily standups, PR reviews on every commit. No solo cowboy coding. Everything is reviewed before it merges.
QA & Testing
Unit tests, integration tests, and E2E flows before every release. Real-device testing on iOS across multiple OS versions.
Launch & Iterate
Zero-downtime deployments, feature flags for gradual rollouts, crash monitoring from minute one. We stay on to iterate based on real data.
Engineering Standards
The rules we never break.
TypeScript everywhere
No plain JavaScript. Every codebase is fully typed. Runtime surprises are not acceptable.
PR review on every commit
No code reaches main without a review. Every pull request is reviewed for logic, security, and style.
Automated linting
ESLint and Prettier run on every commit via pre-commit hooks. Consistent code style is not a debate.
CI checks before merge
Type-checking, linting, and tests must all pass in CI before any PR can be merged. No exceptions.
Zero-downtime deploys
Vercel preview deployments for every PR. Production is never broken by a deploy.
GDPR by default
No third-party tracking without consent. Privacy-first architecture with data minimisation from day one.
CI/CD Pipeline
Automation from commit to production.
Every repository is wired to a fully automated pipeline. No manual deploys, no broken builds reaching users.
Tech Stack
The right tool for the job.
Design
Mobile
Web
AI Layer
Backend
Infrastructure
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