AI App Development for Non-Technical Founders
You prototyped an AI app with Cursor, Bolt, or Lovable. It worked well enough to get excited, then hit a wall. We build the production version. Mobile, web, and the AI pipelines behind them.
Start a ProjectThe App Builder Wall
AI builders are great at getting a working prototype in front of real users in days. That speed is valuable. It proves the idea, pulls in early feedback, and sometimes even closes a pre-seed round.
Then the wall shows up. Performance gets shaky past a few dozen users. The architecture does not support the feature you actually need. A data privacy question comes up and no one can answer it. An investor asks about scaling costs and the generated code does not have a story. The prototype was never meant to hold this weight.
That is where we come in. We do not compete with AI builders. We are the step that comes after. They create speed. We turn speed into substance.
Beyond the AI Prototype
Moving from a generated prototype to a production AI app is a different kind of work. A few of the things we tackle when founders hand us an AI-built codebase:
- Rebuilding the backend on a stack that scales past the demo
- Replacing glued-together API calls with real data pipelines
- Adding auth, billing, and admin tools the prototype skipped
- Wrapping AI calls with cost controls, retries, and fallbacks
- Evaluating models and prompts with real accuracy targets
- Shipping to the App Store and Google Play the right way
- Giving investors a technical story that holds up in diligence
AI Features We Build
Chat Interfaces
Conversational AI with memory, tools, and guardrails. Not just a thin wrapper around a public API.
Computer Vision
Camera-based identification, object recognition, and image analysis on mobile. We shipped this in Perfume Lens.
Personalization
Recommendation engines and user modeling that improve as people use your product.
Agent Workflows
AI agents that run multi-step tasks, call tools, and make decisions. The pattern behind our own NanoClaw ops agent.
RAG Pipelines
Retrieval-augmented generation against your own data. Vector search, chunking, evaluation, the whole stack.
Voice & Guidance
Conversational guidance flows for consumer products. We built this into CoGo for relationship coaching.
AI Apps We've Shipped
We do not demo hypotheticals. These are AI products in founders' hands right now.
- Perfume Lens — point your phone at a fragrance, get an identification. Camera-based AI on mobile, built in Flutter with a custom vision pipeline.
- CoGo — AI-powered relationship guidance. Conversational product design with real prompt architecture behind it, not a chatbot wrapper.
- NanoClaw — our own internal AI operations agent. Runs Slack, Jira, and portal workflows for the studio. Built on the same agent patterns we ship for clients.
Our Approach
AI app development is still app development. The AI is one layer. Everything around it, the mobile shell, the backend, the data model, the auth, the billing, still has to be built well or the AI does not matter.
Our stack is Flutter for mobile, Rails for the backend, Supabase for data and auth, Vercel for web. For the AI layer we pick the right model and pipeline for the job. That might be a commercial LLM with custom prompting and evaluation, a fine-tuned open model, a custom vision pipeline, or a mix. We are not an OpenAI wrapper shop.
We work on flexible monthly retainers, not fixed-bid quotes. You own your code. No vendor lock-in. Two founders on every project, not a pyramid of account managers.
Who This Is For
- Non-technical founders building an AI-first product
- Founders with an AI prototype that needs a production build
- Existing apps adding AI features without breaking what works
- Early-stage startups preparing for seed or Series A diligence
- Product teams that want AI done right, not just shipped
Frequently Asked Questions
How do I build an AI app as a non-technical founder?
Start by prototyping the core idea with an AI builder like Cursor, Bolt, or Lovable to validate your concept quickly. When that prototype hits real-world limits around performance, data, architecture, or investor due diligence, bring in a development partner who can move it to production. That is the gap we fill. We take the AI-assisted prototype and rebuild what needs rebuilding on a stack that will hold up under real users.
What does it cost to add AI features to an existing app?
It depends on what the AI is doing. A chat interface wrapping an existing LLM is a few weeks of work. A custom vision model, agent workflow, or RAG pipeline with your own data can run multiple months. We work on flexible monthly retainers, not fixed-bid quotes, so the scope can shift as you learn what actually matters.
Do you compete with AI app builders like Cursor, Bolt, and Lovable?
No. Those tools are great for getting a working prototype in front of users fast. We are the step that comes after. When the prototype needs to scale, integrate with real backends, pass a security review, or stand up to investor scrutiny, that is when founders bring us in.
What kinds of AI features can you build into a mobile app?
Chat interfaces, computer vision and camera-based identification, personalization engines, recommendation systems, agent workflows, and retrieval-augmented generation (RAG) against your own data. We have shipped camera-based AI identification in Perfume Lens and conversational AI guidance in CoGo. The right feature depends on what actually helps your users, not what is trendy.
Why not just wrap the OpenAI API and call it done?
Wrapping a third-party LLM is fine for a demo. Production AI apps need prompt architecture, data pipelines, cost controls, fallbacks when the model fails, evaluation loops, and often a custom model or fine-tune for the parts that matter. We build those layers so the product works reliably and you are not locked into one vendor.
What tech stack do you use for AI app development?
Flutter for mobile, Rails for the backend, Supabase for data and auth, Vercel for web hosting. AI integrations are custom. We pick the right model and pipeline for the job rather than forcing every problem through the same API.
Can you rescue an AI-built prototype that hit a wall?
Yes. This is one of the most common reasons founders call us. We review the prototype, figure out what is salvageable, identify the architectural decisions that have to change, and map a path to a production build. Sometimes we keep the frontend and rewrite the backend. Sometimes the opposite. Sometimes the prototype proved the idea and the production build starts fresh.
Have an AI App in Mind?
Tell us what you are building. If there is a fit, we will map out how to get from prototype to production. If there isn't, we will point you somewhere better.
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