AI automation
Automation that removes the work, not just the clicks
Automation earns its keep when a person stops doing something entirely, not when the same task gets a slightly faster interface. So the first question is always which step you would be happy never to see again — and whether a machine can be trusted with it.
In practice that tends to mean one of three things: pulling information out of documents and messages that arrive in no fixed format, answering the same questions against your own content, or moving work between systems that were never designed to talk to each other.
Get in touchWho this is for
- Someone on your team spends hours a week copying information between systems.
- The same questions reach your inbox often enough that the answers are effectively a script.
- Documents, orders or forms arrive in formats varied enough that no rule ever quite covers them.
- You have tried an off-the-shelf tool and hit the wall where your process stops looking like everyone else’s.
What you get
A working automation, in production
Built on Azure AI and .NET, integrated with the systems you already run, deployed where your data is allowed to live.
A human in the loop where it matters
Anything with real consequences gets a review step and an audit trail. Full autonomy is a decision you should make deliberately, not one you inherit by default.
Something that fails visibly
Logging and alerting so that when the model gets it wrong — and eventually it will — you find out from a notification rather than from a customer.
The running cost, up front
Token and hosting costs modelled against your actual volume before anything is built, so the monthly bill is not a surprise.
Related work
Common questions
How do we know the AI is not making things up?
By constraining what it is allowed to answer from and checking it. Answers get grounded in your own content rather than the model’s memory, cited back to a source where possible, and routed past a person wherever a wrong answer would actually cost something. Any consultant who tells you it will simply always be right is not one to hire.
Where does our data end up?
Wherever you require it to. Running on Azure AI in an EU region keeps data inside the EU, and prompts sent to Azure OpenAI are not used to train the underlying models. If your case will not tolerate a hosted model at all, that constraint is worth raising in the first conversation, because it changes the design.
Can it work with our existing systems?
Usually. Anything with an API is straightforward, Microsoft systems especially so. Older systems without one are still workable but the integration is a real part of the estimate rather than a footnote.