Getting AI past the pilot stage and into the work, with humans in control of the decisions that matter.
Most AI pilots never reach production. Not because the models underperform, but because a demo that works for the person who built it is a long way from something a team depends on.
We have been on both sides of that gap, including on our own platform. The work that matters is rarely the model. It is the data, the integration, and deciding what should never be left to a model in the first place.
The most expensive mistake we see is applying AI uniformly. Some processes need to be exactly right every time, and a model is the wrong tool for them. Others benefit enormously from something that can interpret messy human input.
Order entry should be deterministic. It needs consistent business rules, every time, and no token cost. Turning a rambling client email into a structured request is the opposite: that is exactly what a model is good at, with a person reviewing the result.
Getting that split right is most of the job. We wrote about it in what's actually working with AI inside businesses.
Our own development platform takes a client request written in plain English, shapes it into structured development tasks, and hands those to coding agents with a developer supervising the output.
It also spent months stuck, and not for the reason anyone expects. The models were fine. A message bus between two services kept dropping, and clients sat in a chat window with no reply. We fixed it by deleting infrastructure, not adding it.
That post-mortem is public: why our AI pilot stalled before production. We would rather show you the failure than a slide deck.
Tell us what is not working. We will tell you honestly whether we are the right people to fix it.
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