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AI that answers to a number

We start from a task that costs you money and a figure to beat. Then we build the smallest thing that beats it, and show you the measurement.

What we build

AI inside a product or inside an operation — not a strategy deck about AI.

  • Assistants and chatbots that answer from your own documents, policies and data, and say they do not know rather than inventing an answer.
  • Document processing — invoices, contracts, delivery notes, applications. Reading them, extracting what matters, and putting it into the system that needs it.
  • AI agents that carry out a multi-step task end to end: triage a request, gather what it needs, draft the response, route it to a person to approve.
  • AI features inside your own product — search that understands intent, drafting, summarisation, recommendations.
  • Automation where AI is only part of the answer and plain software does the rest, which is more often than the market admits.

How we keep it honest

Most AI projects fail the same way: nobody agreed in advance what success looked like, so nobody can tell whether it worked.

So the first thing we do is find the number. How many hours a week does this task take now. What is the current error rate. How long does a customer wait. We measure it before we build anything, and we measure it again at the end with the same method. If the number does not move, the honest answer is that it did not work, and you will hear that from us rather than discovering it yourself six months later.

We will also tell you when AI is the wrong tool. A deterministic rule, a better form, or fixing the process upstream frequently beats a model — cheaper to run and far easier to trust.

Your data stays yours

The first question any serious UAE business asks is where the data goes, and it is the right question.

We design around it from the start: what leaves your environment and what does not, which model provider is used and under what terms, whether anything is retained or used for training, and whether an open model running in your own cloud is the better answer for the sensitivity involved. For regulated work — financial services, healthcare — that question usually decides the architecture, so we settle it in the first week rather than the last.

Start small on purpose

One cycle, one task, one number. That is deliberately a small commitment, because the honest position on most AI work is that nobody knows how well it will perform until it is built against real data.

A first cycle gives you a working thing and a measured result for a known price. If it beats the number you expand it in the next cycle. If it does not, you have spent one cycle finding that out instead of a year.

Questions

Asked before,
answered straight

What does AI development actually cost?

The build is priced per cycle, like everything else we do, and you get the number before work starts. The part people forget is the running cost: model usage is charged per use, so an assistant handling a thousand conversations a month costs more to run than one handling fifty. We estimate that running cost up front, in dirhams per month at your expected volume, because discovering it after launch is how AI projects get quietly switched off.

Will our data be used to train someone else's model?

Not if it is set up properly. The major providers offer business terms where submitted data is not retained or used for training, and we use those. Where the data is sensitive enough that it should not leave your environment at all, an open model running in your own cloud is the alternative, and we will tell you when that is the right call rather than the expensive one.

How do we know the AI is not making things up?

You constrain it and you test it. An assistant answering from your documents is built to answer only from those documents, to cite which one it used, and to say it does not know when the answer is not there. Then it is tested against a set of real questions with known answers, and that test set stays in place so you can re-run it whenever the model or the content changes.

Do we need a lot of data to start?

Usually far less than people assume. Modern language models do not need you to train anything — what they need is access to the right documents and a clearly defined task. If what you want genuinely requires a custom trained model, that is a different and much larger project, and we would tell you so before you spent anything.

Can you add AI to a system we already have?

That is most of the work. We build against your existing systems through their APIs or databases rather than replacing them, so the AI becomes a capability inside what you already run instead of another platform your team has to learn.

Day 01 starts here

Bring us a task that costs you money.

Not an AI strategy — a specific task, and roughly what it costs you now. We reply within 24 hours and will tell you if AI is the wrong tool for it.

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