Monitoring & anomaly detection

The problem you find in the monthly report

By the time it's on a dashboard, it has been true for three weeks.

Continuous monitoring of the numbers that matter — margin, volume, quality, usage, cost — so a break in the pattern raises itself immediately instead of waiting for a report.

How it runs today

You will recognise at least three of these.

  • Problems are detected by a person noticing something in a monthly or weekly report.
  • Thresholds, where they exist, are static and set once.
  • Nobody watches for the absence of something — an order that didn't arrive, a job not booked.
  • Alerts, where they exist, fire so often that people have learned to ignore them.
  • Root cause analysis starts weeks after the cause, when the trail is cold.
The tells

Signals it is costing more than anyone has measured.

  • You have discovered a pricing, billing or supply error long after it started.
  • A customer's volume dropped for months before anyone in your business noticed.
  • Fraud, leakage or waste is found during audits rather than as it happens.
  • Your team ignores existing alerts, which is a rational response to false positives.
  • The first person to notice a problem is usually the customer.
Rebuilt from the ground up

What it becomes.

Instead of reporting on the numbers, the system watches them. It learns the normal pattern for each series — including its seasonality and its day-of-week shape, which is what makes static thresholds so useless — and raises something only when the deviation is genuinely unusual for that series at that time. Critically, it watches for absence as well as excess: the customer who has stopped ordering, the site that has submitted no inspections, the recurring invoice that did not go out. Each alert arrives with its context — what changed, by how much, since when, and the most likely related factors — and goes to the person who can act. The design constraint that makes it work is a strict false-positive budget: an alerting system people ignore is worse than none, so precision is tuned deliberately at the expense of catching everything.

What the system does

  • Pattern-aware detection that understands seasonality and day-of-week shape
  • Absence detection — the thing that stopped happening, not just the spike
  • Alerts with context and likely contributing factors, not just a number
  • Routing to the person who can act, in the channel they use
  • A deliberate false-positive budget, with alert quality measured and tuned

What it connects to

  • Every operational and financial system holding a number worth watching
  • The reporting layer, so an alert can be drilled into immediately
  • Email, SMS and chat for delivery
  • Workflow, so an alert can raise an investigation rather than dying in an inbox

It augments the systems of record you already run. Nobody is asking you to replace your accounting package.

The first release

Where we would start, and how long it takes.

Monitoring on three to five series that matter most — margin, volume, a quality measure — with tuned alerting, in one to two weeks. We start deliberately conservative and loosen it, because a noisy first week permanently damages trust in the system.

Others in data & decisions

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