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AI App Development
We build AI products
that are real software,
not a wrapper.
Most AI apps are a prompt box with no moat. We build the other kind: production software grounded in your data, with an LLM layer that actually does something, owned by you and shipped in eight weeks. We know it works, because we built and run one ourselves.
What we build
AI that earns its place
in the product.
An AI app is a real application, auth, database, UI, deployment, with an LLM layer on top that is wired in properly: grounded in your data, given tools to act, and engineered for cost and quality. We build the whole thing, on Claude, as production software you own outright.
Proof
We built and run a real AI platform: sellyourboat.io
The strongest thing we can tell you about building AI products is that we did not just bill someone for one, we built one of our own and we run it. sellyourboat.io is a Wall & Fifth venture: a yacht marketplace and a white-label software platform for brokerages, carrying over 12,000 listings from more than 100 vetted brokers across 18 countries.
Underneath it sits a Claude-powered AI layer that works both sides of the market. For buyers, a RAG assistant helps people find the right boat by answering from the real listing data rather than guessing. For brokers, an agentic assistant helps manage listings and the admin around them. It is grounded, useful AI inside a real product, not a chatbot bolted to a landing page.
That matters when you are choosing who builds your AI app, because it is the difference between a studio that has read about RAG and one that has shipped it, scaled it, and pays the inference bill every month. We have made the production mistakes already, on our own product, so you do not pay to have us learn them on yours.
The wrapper trap
Why most AI apps have no moat, and how we avoid it
The honest problem with the current wave of AI apps is that most are thin wrappers: a prompt sent to a model, a response shown to the user, and nothing underneath that a competitor could not rebuild in a weekend. If your whole product is one API call, you have no defensibility and no reason for a user to stay.
A real AI product is defensible because the model is the smallest part of it. The moat is in everything around the model: your proprietary data and the retrieval that grounds the model in it, the tools and workflows that let it take real actions, the accounts and history and product surface that make it sticky. The LLM is a component, like a database, not the thing itself.
So the first question we ask on any AI build is not which model, it is what does this product own that the model does not. The data, the workflow, the integration into a real business process. Get that right and the AI is a powerful feature of a defensible product. Get it wrong and you have built a demo that the next model release makes redundant.
What's different
How an AI build differs from a normal app build
An AI app is a normal production app plus a set of problems that only AI introduces, and the second part is where the real engineering lives. Retrieval and grounding come first: getting the model to answer from your actual data, accurately, rather than confidently making things up. This is most of what separates a useful AI product from an embarrassing one.
Then there is the operational reality the brochures skip. Latency and streaming, because models are slow and users are not patient. Evaluation, because unlike normal code, an AI feature can be subtly wrong in ways that only show up against real inputs, so you need a way to measure quality, not just hope. And cost, because every call costs money and a naive design can run up a bill that quietly kills the unit economics.
Managing inference cost is a design discipline in itself: caching what can be cached, using retrieval so the model reads less, and right-sizing the model to the task rather than sending everything to the most expensive one. We build for this from the start, because we run a product where the inference bill is real, and we pass that cost through to you transparently, at cost, never marked up.
The build
Production stack, Claude, owned outright
Under the AI layer is the same production stack we build every product on: Next.js, React, and TypeScript, with PostgreSQL behind it, deployed properly. The AI does not change the need for real software engineering, it adds to it. A flaky AI app on shaky foundations is just two problems instead of one.
We build primarily on Anthropic's Claude, chosen per task rather than by reflex, and we architect so the model can be swapped as the field moves. Tying a product permanently to one provider is a risk, not a convenience, so the model sits behind an interface you can change. The landscape will look different in a year, and your product should be able to.
And as with everything we build, you own all of it: the full codebase, the prompts, the retrieval setup, the infrastructure. Handed over on delivery, no licensing, no lock-in. The AI app is yours to run, change, and scale, with us or with any team you choose.
FAQ
Questions, answered straight.
How is an AI app different from a normal app to build?
Will my AI app just be a thin wrapper around an LLM?
What does an AI app cost to build?
Who pays for the AI API usage?
Which AI models do you build on?
Have you actually built a real AI product?
How long does an AI app take?
Do I own the AI app and its code?
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Ready
Build an AI product
with a moat.
Tell us what you are building and the one thing your AI app needs to do. We will scope it, price it, and ship it in eight weeks, as real software you own.