Design. Build. Launch.

Most MVPs are live in 3 to 5 weeks. We run tight design sprints in the first few days to lock in architecture, then build at full speed. Timeline depends on scope — a focused product with clear requirements moves faster than one still being defined. We'll give you a concrete number in our first call.

Not at all. We work with founders from all backgrounds, including non-technical ones. You bring the domain knowledge and the problem to solve. We handle the technical decisions, the architecture, and the build. You stay informed every step of the way through weekly walkthroughs and a shared project board.

An MVP proves your idea works and gets you to paying customers. Production-ready means it can scale, handle real traffic, and operate reliably without you firefighting every week. We build MVPs with production architecture from day one — multi-tenancy, proper auth, real billing infrastructure — so you're never paying twice to retrofit things you should have had from the start.

We default to Next.js or Astro on the frontend, Node.js or Python on the backend, PostgreSQL for data, and Vercel or Railway for hosting. For AI products we use OpenAI, Anthropic, and LangGraph depending on the use case. We're stack-agnostic when a client has existing infrastructure — we adapt, we don't force a rewrite.

Yes. We're comfortable jumping into existing codebases. Before we commit to scope we do a short technical audit to understand what we're working with, identify any architectural risks, and give you an honest estimate. If something needs to be rewritten we'll tell you — and explain exactly why.

Most projects are scoped as fixed-price engagements with clearly defined deliverables. For ongoing product development we offer retainer arrangements. We don't do hourly billing — it creates the wrong incentives. You should know what you're paying before we start, not after.

Launch is the start, not the end. We offer a post-launch support window on every project where we're available to fix issues, respond to user feedback, and iterate quickly. After that, many clients move to a retainer for ongoing development. We can also hand off cleanly to an in-house team with documentation and onboarding support.

Both. We build AI features into existing products — scoring models, automation layers, smart suggestions — and we build AI-native products from scratch. Our AI work is product-focused, not research: we use the best available models and tools to solve a specific user problem, not to experiment for its own sake.