“It worked in testing” is not a monitoring strategy.
AI systems degrade silently. The data shifts, the business changes, the model keeps answering with the same confidence, and nobody notices until the damage has a number attached.
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We instrument the system so degradation announces itself: accuracy measured against live outcomes, drift watched at the source, and business impact tracked against the metric the system was funded to move. Evidence, continuously, instead of faith.
FOR LEADERS WHO
have to answer “is it still working?” with data.
FOR SYSTEMS THAT
make decisions someone must be able to defend.
FOR TEAMS WITH
dashboards full of latency and nothing about truth.
01 What you get
01
The evaluation suite, live.
The definition of “correct” for your domain, running continuously against production traffic rather than a frozen test set.
02
The drift watch.
Input data, model behavior, and outcomes monitored for shift, with alerts tuned to what matters and silent on what doesn't.
03
The business dashboard.
Impact in the terms the system was funded for: hours saved, losses caught, revenue moved. Accuracy is a means, not the metric.
04
The incident loop.
When quality drops, a defined response: who's told, what's checked, when retraining triggers, and what the record shows afterward.
02 How it runs
Four phases, from hoping to knowing.
The dashboard is the easy part. Deciding what deserves an alert is the work.
PHASE 01
Define “working.”
With your operators and risk owners: what correct means, what failure costs, and which signals prove which.
PHASE 02
Instrument.
Evaluation, drift detection, and business tracking wired into the live system, with the paper trail regulators expect.
PHASE 03
Baseline and alert.
Normal established, thresholds set, alerts tuned until they're rare enough to be believed.
PHASE 04
Review and tighten.
A standing review rhythm with named owners: what moved, why, and what gets adjusted, model, data, or threshold.
03 Questions we answer
Is it still as accurate as the day it shipped?
What's drifting: the data, the model, or the business?
What did the AI decide last quarter, and can we prove it?
Which failures matter, and which are noise?
Is it moving the metric we funded it for?
When do we retrain, and how do we know?
04 Continue the arc