A system we built and run
Catch the moment a number breaks — before it shows up in a month-end report — without anyone watching dashboards.
Book an AI auditThe point of it
We built Anomaly Detector because no one on our team could watch every client account every day, and the problems that hurt most were the quiet ones that surfaced weeks later in a review. So we built something that watches for us. It checks every account daily, learns each one's normal, and pings the right person the moment a number breaks from its own pattern — in plain language, with a first thing to check.
A metric that drops off does not wait for the next review to be noticed. The system checks every account every day and surfaces the break the day it occurs, so a small problem is caught before it becomes an expensive one.
A change has to be both a real-size move and a real-percentage move before it counts, and findings are tiered by severity and batched into digests. The trivial stays quiet and the urgent stands out, so the feed earns attention instead of spending it.
Each alert says what moved, by how much, against what baseline, and what to check first. The serious ones can be promoted into a tracked task with a checklist, so a signal becomes work that gets closed rather than a ping that scrolls away.
Built for us first
This is what we built to run our own companies. Because we built it, we can rebuild it around yours.
Underneath the alerts is one reusable pattern: learn the normal shape of a high-volume data stream, catch the moment it breaks, and tell the right person without burying them in noise. Pointed at a different set of numbers it becomes something else entirely, and we'd build that version around the data your business already watches by hand.
Watch margin and unit-cost data across products and lines, and flag the moment a cost input drifts or scrap pushes a margin out of its normal band — as a same-day alert instead of a line item in a month-end report.
Monitor inventory and order data for stockout drift and demand swings across thousands of SKUs, so the items breaking from their own pattern surface on their own and buyers act on the exceptions instead of scanning the whole catalog.
Track defect and scrap rates by line, shift, or supplier and alert when one spikes past its baseline, while ordinary variation on high-volume runs stays quiet and a low-volume line's single failure still gets heard.
Watch conversion rates, revenue, and SLA timers for sudden drops or breaches, so a stalled funnel or a service window about to be missed raises an alert the day it happens, not at the next review.
Questions
It builds a baseline for each metric from sixteen weeks of same-day-of-week history and projects the trend, so it has a grounded expectation for what today should look like. An anomaly is a move far enough from that expectation, in both size and percentage, to be unlikely for that specific account.
That's the exact failure mode it's built to avoid. Every alert has to clear both an absolute and a percentage floor, severity is tiered so the trivial doesn't read like the critical, and routine findings are batched into digests. A standalone interruption is reserved for a genuinely urgent break.
Yes. The engine doesn't care whether the numbers are ad spend, unit costs, inventory, defect rates, conversions, or SLA timers. If it's a high-volume stream with a normal shape that someone should watch but can't, the same baselines, floors, tiering, and digests apply, and we'd shape it to your data.
Start here
Every engagement starts with an audit — a fixed fee, in writing. If Anomaly Detector, or a version of it shaped around your business, is the right move, we build it. If the answer isn't AI, we'll say so.
Fixed feefixed durationa verdict you keep either way
