aicodecomb is an AI product studio. We design, engineer, and launch AI-native web and mobile applications — the kind of products only AI makes possible — from first sketch to live release.
We're a small, opinionated studio that builds AI-native applications end-to-end. Founders bring us an idea or a rough prototype; we ship a real product.
Every application we build treats AI as a first-class material — not as a chatbot bolted onto a CRUD app. LLMs, agents, retrieval, vision, voice, automation: we use whichever model and pattern makes the product genuinely useful, and we wire it into a clean, production-grade codebase that you can keep building on after launch.
Web, iOS, Android, internal tools, agentic backends — we work across the full stack. One team, one timeline, one bill. From idea to shipped.
Models, agents, and retrieval are designed in from day one — not retrofitted onto a legacy app. The product gets to do things a non-AI version simply couldn't.
No demoware. We ship apps that handle real users, real auth, real billing, real edge cases — with the observability and CI to keep them running long after we hand off.
Weeks, not quarters. We use AI-augmented tooling on our own side too, so you get a polished v1 in the time most agencies spend on a Figma file.
Four practices that we treat as one team. Strategy, design, engineering, and AI work happen in the same room — so the seams between them never reach the user.
We start with the question most teams skip: what should this product actually do, now that AI is on the table? Concept, scope, model selection, build-vs-buy — mapped against your users and your runway before a line of code is written.
Distinctive, usable interfaces that don't look like every other AI app. We design the experience around the model's strengths and around its failure modes — so users feel competent, not confused, when the AI is in the loop.
Web, iOS, Android, internal tools. TypeScript / Next.js, React Native / Swift / Kotlin on the front; Node, Python, Go on the back; Postgres, vector stores, queues, and the rest of the boring-but-essential infrastructure underneath.
LLM orchestration, RAG over your data, agentic workflows, evals, and guardrails. We pick the right model for the job (frontier or local), and we wire it up with the prompt engineering, caching, and observability needed to run reliably in production.
Every project moves through the same four phases. They're not boxes we tick — they overlap, repeat, and feed back into each other until the product is in users' hands.
Concept · Users · AI Feasibility
We pressure-test the idea. Who's it for, what does AI unlock that nothing else does, and which parts of the experience are real product versus demo magic. The output is a sharp scope and a build plan we both believe in.
Flows · Interface · AI Patterns
Wireframes, flows, and high-fidelity UI — designed alongside prompt and agent prototypes. We figure out the model's behavior on real inputs before we commit to a screen, so the design and the AI fit each other instead of fighting.
Engineering · Iteration · Evals
Weekly playable builds. Real auth, real data, real model calls from week one. We run evals on every meaningful change to the AI layer, and we keep the surface area small until the core loop genuinely works.
Launch · Telemetry · Handoff
Production deploy, app-store submission, observability, runbooks. We hand over a codebase your team can keep building on — not a black box. If you'd rather we stay on for a while, we can do that too.
Founders, operators, anyone with an AI product worth building — tell us about it. A short note about the idea and where you're stuck is enough to start.
We typically respond within 24 hours