We are hiring across engineering, design and delivery.
See open rolesA forecast without an error range is a guess with a decimal point. The useful question is not "what will demand be" but "how wrong might this be, and what does that cost me".
We build models that answer both, evaluated against how well your current method does — which for demand planning is often a seasonal average that is harder to beat than people expect. When we beat it we can say by how much; when we do not, you have saved the build cost.
Whether you have enough history, and clean enough, before anything is promised.
The simple approach first. Anything more complex has to earn its place against it.
The signals that actually carry information, built with your domain knowledge.
Time-split where time matters, so the score is not flattered by leakage.
Ranges rather than single numbers, because that is what a decision needs.
Automated, triggered by measured drift rather than by the calendar.
If one of these sounds like your situation, it is worth a conversation.
Capital tied up in the wrong lines while the right ones run out.
You know who left. Knowing who is about to is worth considerably more.
Cost-plus or matching a competitor, with no view of what the market would bear.
The difference between a model that helps and one that quietly misleads.
We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.
Held-out, time-split where time matters. An accuracy figure from the training set is not a result.
Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.
Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.
Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.
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Deliberately front-loaded with the question of whether to proceed at all.
We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".
A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.
Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.
Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.
Tell us what you are trying to do. If machine learning is not the answer, we will say so.
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