Artificial Intelligence that actually ships

It depends on the problem. For tabular classification (like churn or defect detection), a few thousand labelled rows is usually enough to get a useful prototype. For image or text tasks, you may need tens of thousands of examples. During the data audit we give you an honest estimate, and if you do not have enough, we help you design a collection plan rather than pretend the gap does not exist.

No. About half our clients have between 20 and 200 employees. The deciding factor is not company size but whether you have a specific, measurable question and the data to answer it. We have turned down FTSE 250 firms whose brief was too vague, and taken on a ten-person logistics company whose GPS data was rich enough to build a solid route optimiser.

Python is the backbone: scikit-learn, XGBoost, PyTorch, and occasionally TensorFlow depending on the task. For deployment we favour FastAPI behind a containerised setup on your own cloud account (AWS, Azure, or GCP). We avoid vendor lock-in. Every model we deliver can be retrained and redeployed without calling us.

We set accuracy thresholds at the start of every engagement. If the prototype cannot meet them after reasonable tuning, we stop the project and you pay only for the hours spent up to that point. We have done this three times in four years. In two of those cases, the client came back six months later with better data and we succeeded on the second attempt.

Yes. We regularly work inside client VPNs and air-gapped environments. Two of our engineers hold SC clearance. We can also train models on synthetic or anonymised data if the real data cannot be accessed directly, though this usually adds a week to the prototype phase.