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See open rolesMost dashboards are opened once, at the meeting where they were presented, and never again. They show what was easy to chart rather than what anybody has to decide.
We start from the decision: who makes it, how often, and what would change their mind. A dashboard that answers that gets opened on a Monday morning without being asked for — and it is usually far simpler than the one that gets abandoned.
The recurring decisions and who owns them. Everything else follows from this.
Agreed and documented, so the number means one thing across the company.
Power BI, Looker Studio or Metabase — chosen on your stack and licensing, not ours.
From the headline to the underlying rows, because the first question is always "why".
Delivered to inboxes or chat, since the best dashboard is one you do not have to remember.
So people can answer their own follow-up questions instead of raising a request.
If one of these sounds like your situation, it is worth a conversation.
Someone spends the first week of every month building the board pack by hand.
Built, launched, ignored. Almost always because they answer the wrong question.
The data exists but is too slow to reach, so people go with a feeling.
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 business intelligence is not the answer, we will say so.
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