Most AI work fails because nobody checked whether the data could answer the question. We start there. We take a decision your business already makes by hand, prove against your own data whether a model can make it better, and tell you if the answer is no before you have spent anything building it.
What follows is ordinary engineering. Machine learning for forecasting and classification, computer vision where the answer sits in an image, language models for the work that arrives as text, and agentic systems that act on what they find. It runs wherever it has to run, in the cloud or on your own hardware when the data cannot leave the building.
Which of these you need depends on where your data already sits. Most engagements start with the machine learning work, because proving the use case is what decides whether any of the rest is worth building.
Most engagements draw on more than one of these.
Bring us a plan or just an idea. We will arrange a discovery meeting with you to go through it.