AI development services

  • Agents, chatbots, RAG and integration
  • We host and maintain what we build
  • Scoped from your systems, not from a template

From the first conversation to something running in production

Most AI projects stall in the same place: a demo that impressed everybody, and no path from there into the systems the business actually runs on. We build the whole way through - the model work, the retrieval, the integration, the hosting and the part where somebody has to maintain it after launch.

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How an AI project actually runs

Four stages. The third is where most of the budget goes, and most writing about AI skips it.

The stages, in order

Decide what it must do

One task, stated so narrowly that you can tell whether it worked. "Answer customer questions" is not that. "Answer the eight questions that make up most of our enquiries, and hand the rest to a person" is. This conversation saves more money than any technical decision later.

Get the knowledge in order

Whatever the system answers from - your site, your policies, your product data - has to be found, split and checked for contradictions. If two documents disagree, the system will confidently pick one. Resolving that is part of the work, not a precondition for it.

Integrate it

Usually the larger half of the bill. Reading from your billing system, writing to your CRM, authenticating, handling the case where the other system is down. The model is a few lines; this is the project.

Run it

It needs hosting with root access, monitoring, a place for logs, a way to update the knowledge when your prices change, and somebody to look at what it got wrong. A system nobody reviews drifts quietly.

Where AI is the wrong answer

Said plainly, because a project that should not have started is the most expensive kind.

We will tell you not to

When a form would do

If the task is collecting five fields and emailing them somewhere, that is a form. An assistant in front of it adds cost, latency and a new way to fail.

When the data does not exist

A model cannot tell a customer your delivery time to Oman if nobody has ever written it down. Several projects are really documentation projects wearing a different hat.

When the answer must be exactly right every time

Tax figures, dosages, legal deadlines. Retrieval reduces invention; it does not eliminate it. Those belong in a lookup with a deterministic answer, with AI at most helping you find the right row.

When nobody will own it

An AI system is not a thing you install and leave. If no one in the business will review what it said and update what it knows, it will be worse in six months than on launch day.

AI development: common questions

Four things: defining one task narrowly enough to test, preparing the knowledge the system answers from, integrating it with the systems you already run, and operating it afterwards. The third is usually the largest cost and the one most quotes underestimate.

Both, chosen by the constraint rather than by preference. A hosted model is faster to ship and cheaper to start; a self-hosted one matters when the data cannot leave your infrastructure or the per-call cost at volume stops making sense. We will tell you which applies to your case and why.

Not on the commercial API tiers we build on, which exclude business data from training by contract. If that guarantee is not enough for your industry, the answer is a self-hosted model on your own server, and we will say so rather than talk you past the question.

Yes, and often that is the right shape: they know the system, we do the AI and the integration. What does not work is two teams both believing they own the data model.

Agree the number before the build. For a chatbot it is the share of conversations resolved without a person; for an agent it is tasks completed without intervention; for retrieval it is answers a human marks correct. If nobody can name the number, the project has no definition of done.

The knowledge goes stale as your prices, policies and products change, so it needs updating, and the logs need reading to find what it got wrong. We offer that as ongoing work, or hand it to your team with the documentation to do it.

By type of work

The detail on each, including where each one goes wrong.

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