AI apps that solve one problem properly.

AI app development is building custom software that uses artificial intelligence to solve a specific business problem, such as an internal tool that drafts quotes, an agent that researches leads, or a dashboard that turns scattered data into decisions. The difference from buying an AI product is that the application is shaped around how your business actually works rather than around what a vendor chose to build.

You get an AI app when you have one specific problem worth solving properly. The alternative is rarely one tidy subscription. It is usually five or six tools that each solve a slice of it, or three if you also need the data to stay inside your systems, and every join between them is a person copying something across. An app collapses that into one thing that works the way you actually work, and you own it. Where an off-the-shelf product genuinely covers the whole problem, we will point you at it instead, because a build you did not need is the fastest way to lose your trust.

A focused internal tool or agent typically lands between $8,000 and $30,000 depending on how many systems it has to touch and how much of your data needs preparing first. Ongoing running costs are usually tens to low hundreds of dollars a month in model usage. You get the number before work starts, and we scope it so the first useful version ships early rather than at the end.

A first working version is usually live in two to four weeks. We deliberately build the riskiest part first, in days, so that if something is not going to work you find out in week one rather than week ten.

You do. It is built in your repository and deployed to your accounts, documented, with your team trained to run it. We do not hold your systems hostage as a retainer mechanism.

Typically Claude and OpenAI models via API, Astro or Next.js for interfaces, Supabase or Airtable for data, Pinecone for retrieval, and Make.com or n8n where a visual pipeline is genuinely simpler to maintain. The stack follows the problem rather than the other way around.

No. We use business and API tiers where inputs and outputs are not used to train public models by default, and we never paste client data into consumer chat interfaces. If a dataset must never leave your infrastructure, say so at the start and we design around it.

If you want it. Some clients take the handover and run it themselves, which is a perfectly good outcome and the one the documentation is written for. Others keep us on to keep improving it, which is cheaper than it sounds because we already understand the system.