AI product development
AI product development for funded startups and product teams.
The AI feature has been on the roadmap for three quarters. Every sprint, something more urgent wins. Meanwhile a competitor just shipped theirs.
We build products with intelligence at the core, not bolted on: copilots users actually adopt, voice-native assistants, vision systems, platforms that learn. Full stack means full stack: the model layer, the evals, the backend, the interface, and the deployment pipeline, one team, no vendor relay race. We have shipped AI products across ten industries, from a voice assistant for gamers to biometric onboarding for fintech.
What you get
- Product discovery and AI feasibility in days, not months
- LLM, voice, vision and multimodal engineering
- Evals and quality gates wired into CI
- Full product build: backend, APIs, web and mobile UI
- Cost and latency engineering for unit economics
- Launch, instrumentation and iteration loop
How it runs
Shape
The bet, the riskiest assumption, and the smallest system that tests it.
Prototype
A working product slice on real data. Not a deck, a URL.
Build
Production engineering: quality gates, cost ceilings, security review, real users.
Scale
Instrument adoption and quality, tune unit economics, hand over clean or keep shipping together.
From our production work
- Biometric onboarding that cut a 2-day process to about 8 minutes, working offline in the field.
- A voice assistant for gamers that handles in-game tasks hands-free.
- Skin analysis from a selfie in under a minute, with personalized guidance.
Do you build the whole product or just the AI part?
The whole product: model layer, backend, APIs, web and mobile interfaces, deployment. Coordinating a separate AI vendor and app team adds cost and risk; we remove the seam.
Which stack do you use?
TypeScript and Python end to end: React and Next.js, Node and Python backends, Postgres with pgvector, vector stores like Weaviate, and model providers chosen per use case including Claude, GPT, Gemini and open weights.
How do you handle AI quality in a product?
Eval suites run in CI like tests: golden sets, regression checks, cost and latency budgets. A model change that drops quality fails the build before it reaches users.
Can you work with our in-house team?
Yes. We slot in as the AI product team, pair with your engineers, and hand over with documentation, runbooks and training rather than lock-in.
Bring us the workflow. Leave with a plan.
One call. We will tell you honestly what AI can and cannot do about it, and what it costs to find out.