Anomaly detection that doesn't cry wolf.

Most alerting tools generate noise. Here's the statistical and product thinking behind a low-noise alert stack.

Five years ago, the answer to "we need better visibility" was a dashboard. Three years ago, it was a self-serve BI tool. Today, it is something else entirely. In this post, we share what we have learned across 200+ enterprise rollouts of DatiqAI.

The shift from dashboards to decisions

Most organisations have far more dashboards than they have decisions. The reason is simple: dashboards are easy to make and hard to evaluate. Decisions are hard to make and easy to evaluate. DatiqAI inverts the work — we treat the dashboard as a side effect of a decision being made, not the artefact teams optimise toward.

Grounding is non-negotiable

Trust in an AI answer collapses the moment you cannot trace it back to a row. We invested heavily in citation infrastructure before we shipped a single Copilot feature. The result: every answer is a path back to the source data, with the metric, the time range, and the SQL exposed.

The decision loop

Insights are commodified. The differentiator is the loop: detect an anomaly, brief the right person, propose an action, log the decision, and measure the outcome. DatiqAI compresses that loop from weeks to hours.

"We stopped writing executive summaries. The Copilot writes them and we edit. Most weeks, we do not edit."

Where to next

The decision layer for the modern enterprise is not a feature on top of analytics. It is a new category — and it is where DatiqAI AI is investing every available engineering hour.