Getting AI into production

Most organisations do not have an AI problem. They have a workflow problem they hope a model will fix. When the model arrives it still needs data, permissions, an interface, a fallback and an owner for the failure modes.

We treat AI as production software. Bounded use cases. Measurable behaviour. Integration into tools people already use. Monitoring so it does not become an unowned experiment.

If you cannot evaluate it, you cannot trust it. If you cannot operate it, it will not last.

A monitored job that clears an inbox queue is often worth more than an agent with no owner.

The engineering questions are ordinary: where does the data come from, who is allowed to see it, what happens when the model is wrong, and what does it cost to run every day.