Data Analytics · Machine Learning · AI

AI that survives contact with your operations

We build machine learning into the systems a business already runs on, so a model changes a decision rather than sitting in a notebook. Prototype quickly, prove it works, then scale it into the enterprise.

What we do

Six areas where machine learning reliably pays for itself, and where we have done the work before.

Process automation

Machine learning applied to the repetitive decisions inside a workflow, so the routine cases clear themselves and people see only the ones that need judgement.

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Data analytics

Turning operational data into something a decision can rest on, including the unglamorous work of getting it clean and consistent first.

🤖

AutoML

Automated model selection and tuning, so a working baseline arrives in days and the argument moves on to whether it is useful.

🔮

Forecasting

Trend and demand models built on your own history, with the uncertainty stated rather than hidden behind a single number.

👀

Vision and language

Reading documents, images and free text that no one has time to process by hand, with review kept in the loop.

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Healthcare AI

Applications built for clinical and life sciences settings, where evidence and traceability matter as much as accuracy.

What AI means here

Applying advanced analysis and logic-based techniques, machine learning among them, to interpret events, support and automate decisions, and then act on them.

That definition rules a lot out. A model that produces an interesting number but changes nothing about how the work runs has not finished the job. We are interested in the part after the model.

How we work

Two ways to work with us

Same company, two front doors, depending on what you need.

Tell us what you are trying to automate

We will say plainly whether machine learning is the right tool for it.

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