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Silent drift
Accuracy decays as data and behaviour change, with no one watching.
Drift detection, performance tracking and scheduled retraining so your models stay accurate long after go-live.
A model is only as good as its last week in production. The world shifts, data changes, and accuracy quietly erodes. We put the monitoring, alerting and retraining in place that keep models dependable — so the value you built doesn’t decay.
Most models are launched and forgotten — until a bad decision reveals the drift.
Accuracy decays as data and behaviour change, with no one watching.
There’s no repeatable, safe way to refresh the model when it slips.
No one owns model health, so problems surface as business mistakes.
Four capabilities that keep production models healthy.
Continuous tracking of accuracy and business KPIs in production.
Detection of data and concept drift before it hurts decisions.
Safe, repeatable retraining pipelines triggered by drift or schedule.
A clear, shared view of every model’s status and history.
Production models that stay trustworthy.
Live tracking of accuracy, drift and KPIs with alerts when they slip.
Automated, safe retraining triggered by drift or schedule.
One view of every model’s status, version and performance.
Clear ownership and steps for keeping models healthy.
Bring a real decision or dataset — we’ll show you how KEPLER would approach it, with no obligation.
Book a 60-minute sessionWe start with a short diagnostic — the decision to improve, the data behind it, and a first slice that proves value fast. See how we engage →
From a quick diagnostic to a fully managed service — start small and scale as value is proven. How we engage →
The industries this work serves.
R&D, safety and commercial analytics.
Operational and patient-flow analytics.
Project controls, cost and schedule analytics.
Demand, pricing and customer analytics.
Quality, throughput and maintenance analytics.
The discipline that survives go-live.
We wire monitoring into the live model and its data.
Accuracy and drift are tracked continuously.
Slippage triggers an alert to the owning team.
Safe pipelines refresh the model when needed.
A runbook keeps ownership and standards clear.
Models keep performing as the world moves.
The category, capabilities and expertise this connects to.
Common problems in this area, how KEPLER solves them, and the likely outcome.
Drift caught early
View details →A deployed model decays as the world shifts under it. Without monitoring, the first sign is a bad business outcome, not an alert.
Model decay caught by a metric, not by a costly wrong decision downstream.
Auditable model decisions
View details →A model in production has no record of versions, inputs or rationale, which is a problem the moment a regulator or customer asks how a decision was made.
Every model decision traceable and explainable, ready for audit or challenge.
Automated retraining loop
View details →When a model finally degrades, retraining is a hand-run project each time, so it happens late and inconsistently.
Models that refresh themselves on a safe, automated loop instead of a periodic fire drill.
No use cases match this filter yet — but the problem is almost certainly one we can help with.
Ask us about your problem →Tell us which models matter and we’ll put the monitoring and retraining around them.
Talk to our AnalyticsOps team
