Two different jobs sit under this heading. Taking repetitive work off your team, and building AI into your systems and your product. They need different conversations and different skills, so they have their own sections below. Since 2012, for clients in the US, UK, Europe, Singapore and the Gulf.
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The two halves
The distinction matters more than it looks. The left is about work your people do that a system could do instead. The right is about building the AI itself, into software you own or sell. Many companies eventually need both, but almost nobody should start with both.
Taking repetitive work off your team. Reading the paperwork, applying your rules, asking a person when it is unsure, and writing the result into your systems. Usually bought by whoever owns the process.
Building the AI itself. Answering from your own content, running models on your own hardware, adapting them to your industry, and putting features into software you sell. Usually bought by whoever owns the technology.
Thirty minutes is usually enough for us to say which half of this page you are actually in.
How we work
Every piece of work starts with a set of your real cases and agreed correct answers. Sometimes that measurement says the project is not worth doing, or that a much cheaper fix would close most of the gap. We report that, even when it means less work for us.
Anything the system is unsure about goes to a person rather than being guessed at. Where the decision carries weight, the approval and the reasoning are recorded against a name. This is how the work stays defensible when someone asks six months later.
Twelve years of connecting to ERPs, finance systems and legacy databases is most of what makes an AI project succeed or stall. Software that clicks through screens is a last resort we price honestly, not a default we hide.
A scored test set that runs on every change, in your own pipeline. Our QA practice does this work for ordinary software already, which is why we can do it for AI rather than shipping on a demonstration.
Code in your repository from the first week. Trained models, test sets and documentation are yours. Applications talk to one internal endpoint rather than to a specific provider, so you can change model, or supplier, without a rebuild.
Where the data may go, what your auditor will ask, which risk tier applies under the EU AI Act, and what has to be agreed in writing. Much cheaper to answer now than to retrofit into something already running.
FAQ
With a call, then usually with AI Strategy if the question is about your own operations, or with a single small feature if the question is about your product. What we would avoid is a large programme before anything has been measured.
No. A good part of this work is picking up something built by someone else: testing it, fixing it, or keeping it running after the person who built it moved on.
Yes. Open models on your own hardware, in your own cloud region, or fully offline. That is its own page, and it is a common requirement for our clients in the Gulf and Europe.
A software company that builds AI. We were integrating business systems for eleven years before this became a category, and on most of these projects the integration and the testing are the hard part, not the model.
Next step
What your team does over and over, or what your product cannot do yet. We will tell you on the call which of these pages you actually need, and whether it is worth doing at all.
Ahmedabad, India. We work with teams in the US, UK, Europe, Singapore and the Gulf, and we are used to the time difference.