Most quality and risk problems get caught after the cost is already locked in. The model's job is to move that moment earlier, so the team acts on a prediction instead of a postmortem.

REACTIVE

Act after the outcome

The cost is already incurred by the time anyone notices.

Drift begins. The process or portfolio quietly trends toward the risk zone, unseen.

Outcome recorded. A lab test, a monthly report or a default confirms it after the fact.

Late reaction. The team responds, but the cost already landed.

WITH PRED AI

Prevent before it happens

The model predicts the trend and flags it while there's still time to act.

Trend predicted. The model flags the drift before it reaches the risk threshold.

Action routed. The flag lands directly in the workflow the team already uses.

Cost avoided. The team intervenes while it's still cheap to fix.

HOW WE WORK

Four steps, every time

Define the problem and success measure

What decision this needs to improve, and how we will know it worked.

Check the data

Whether there is enough signal, and whether it is the signal we think it is.

Build and validate

Build the model, test it against held-out data, check that it holds up.

Deploy and monitor

Ship it into the real workflow, then watch how it performs.

SELECTED WORK

What we build

View all case studies →

CreditSense

Credit risk scoring for thin-file borrowers, covering both new-applicant underwriting and existing-borrower monitoring, explainable at the score level. In active development.

Churn & retention

Churn and renewal propensity modeling with risk bands, so a retention team knows exactly who to call first.

Sales prioritisation

A ranking engine that scores accounts by propensity and feeds the result directly into the CRM a sales team already uses.

Have a problem worth solving? Let's talk.

Discuss a project

Machine learning thatruns in production.

Two practices, one method

Industrial AI

Business ML Solutions

Reactive, or ahead of it