We are hiring across engineering, design and delivery.
See open rolesMost businesses have the data to answer their important questions. It is in four systems, in different shapes, with the customer identified three different ways.
The work is less about clever analysis than about getting it into one place, reconciled, with agreed definitions. Once "active customer" means the same thing to sales and to finance, most of the questions answer themselves.
What data exists, where, how good it is, and what is missing entirely.
Automated extraction and loading, scheduled and monitored, rather than a monthly export.
A single modelled store — BigQuery, Snowflake or Postgres, sized to what you have.
De-duplication and entity resolution, so one customer is one customer.
Written down and agreed, so two reports cannot disagree about the same word.
The questions you started with, answered, with the caveats stated.
If one of these sounds like your situation, it is worth a conversation.
Two systems, two answers, and a monthly argument about which to believe.
Days of manual work to answer a question that should take a minute.
Models need clean, joined data. This is the step people skip and then regret.
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 data analytics is not the answer, we will say so.
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