Apps with AI at the core, not bolted on.

What you get

• Conversational interfaces — natural-language UX that lets users query, act, and get work done without forms or menus.
• Copilots — embedded assistants that draft, suggest, and automate inside your product or internal tools.
• RAG-backed assistants — answers grounded in your own documents and data, with citations and freshness, not hallucinated guesses.
• Intelligent workflows — AI that classifies, routes, and orchestrates multi-step tasks with humans in the loop where it matters.
• Guardrails & evaluation — prompt safety, validation, and an evaluation harness so quality is measured, not assumed.
• Your repository — the full-stack codebase in your GitHub organization, with handover docs and no lock-in.

How we build it

One focused workshop locks the scope and a fixed-price proposal. Senior engineers then design the architecture, data model, and AI grounding strategy — the blueprint the AI builds against. AI agents generate the full stack (APIs, frontend, retrieval, prompts, tests, CI/CD) in parallel, and every pull request is reviewed line-by-line by a senior before it ships. You see weekly demos against a live staging environment, and every build includes a 30-day post-launch window for fixes and tuning.

Default technology stack

Next.js / React / TypeScript on the front end, Node.js or Python (FastAPI) on the back end, PostgreSQL plus Redis, hosted on AWS or Vercel. The AI layer uses OpenAI, Anthropic Claude, and open-weights models orchestrated with LangGraph, with a vector database such as Pinecone for retrieval, plus Stripe and Supabase where they fit. If you have a hard requirement — Java, .NET, or on-prem — we adapt.