AI & Partners

Product feature

Model Drift

Spot when data or results start to shift.

Catch your models drifting before your decisions do

Models don't fail all at once. They drift slowly and quietly, as the world their training data described stops matching the world they run in. AI & Partners measures that drift feature by feature, alerts the right people when it crosses a threshold, and shows exactly where it started.

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Model drift overview listing models with drift health, open events and riskiest features

Feature drift (JSD)

score feature · Critical
0.323
0NormalWarning 0.15Critical 0.300.35

Demo environment · illustrative figures

Is the model you deployed still the model you have?

A model that was accurate at launch can be quietly wrong six months later without a single error in the logs. Input data shifts, customer behaviour changes, a new region comes online, and the model keeps answering with the same confidence it always had. By the time the outcome data catches up, the damage is already in the decisions.

  • No early warning: drift only shows up when outcomes go wrong

  • No way to tell which feature is driving the change

  • Retraining decisions made on instinct, not evidence

Features

Drift detection that tells you what, where and how much

Every monitored model, listed with its drift health (Normal, Warning or Critical), open drift events, last alert and riskiest features. The team sees at once which models are stable and which are moving.

Drift Overview in the platform

For your team

What this means for you

  • Get an early warning weeks before drift shows up in outcomes
  • Know exactly which feature moved, by how much and since when
  • Retrain when the data says so, and show why
  • Set thresholds that match the risk of each model
  • Keep a record of drift events that proves ongoing oversight

How it works

Four steps, running continuously

  1. 1

    Step 1

    Set a baseline

  2. 2

    Step 2

    Measure each feature (JSD)

  3. 3

    Step 3

    Flag Warning or Critical

  4. 4

    Step 4

    Drill down and decide whether to retrain

Try it

Set your own drift thresholds

Drag the Warning and Critical thresholds and watch the sample drift line recolour.

0.15
0.30
Drift score (0 = no change)

Date (daily, last 20 days)

12 events would be flagged

Drift: how far today's data has moved from the data the model was trained on. 0 means no change; higher means the model is seeing unfamiliar data.

3 critical

Illustrative data

Let's Get Started

See drift detection running on one of your own models.

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Model Drift in Action

Where a slow shift in the data becomes a fast problem.

Insurance Claims and Underwriting Models

Catch shifts in claim patterns before pricing goes wrong.

Fraud Detection Systems

Know when fraud tactics change before the model falls behind.

Demand Forecasting and Pricing

Spot changing buying patterns before forecasts miss.

Prior-Authorisation and Triage Assistants

Flag changes in requests before decisions drift from policy.

Frequently asked questions

Drift is the gap that opens between the data a model was trained on and the data it sees in production. Data drift is a change in the inputs. Concept drift is a change in the relationship between the inputs and the right answer. Both make a model less reliable without any visible error.

Let's Get Started

See drift detection running on one of your own models.

Book a Demo