The model that worked great on launch day quietly goes quiet six months later, and nobody notices The deployed AI solution performed well at launch, so the topic got closed. Meanwhile the data it runs on keeps shifting month to month — new customers, new products, different ways people phrase their questions — and the model's accuracy slowly declines until someone notices the problem after the fact.
Ready to publish without client data. --- # The model that worked great on launch day quietly goes quiet six months later, and nobody notices
The deployed AI solution performed well at launch, so the topic got closed. Meanwhile the data it runs on keeps shifting month to month — new customers, new products, different ways people phrase their questions — and the model's accuracy slowly declines until someone notices the problem after the fact.
Once an AI solution goes live, the team's attention moves to the next project, and nobody regularly checks whether the model still performs as well as it did on acceptance day. A model trained on data from six months ago doesn't know reality has moved on.
The decline in accuracy happens gradually, so it's easy to miss — until the number of errors or escalations grows large enough that someone finally asks what happened.
We monitor a deployed solution's accuracy on an ongoing basis and flag it when performance starts dropping below an agreed threshold — before it becomes visible in the team's daily work. When needed, we fine-tune the model on more recent data so it keeps pace with a shifting reality.
We build monitoring on the same metrics we agreed with you at rollout, so comparing today's accuracy to acceptance day is a fair comparison, not a guess. Regular review of input data shows when reality starts drifting away from what the model learned on.
What we don't do: we never fine-tune a model without your knowledge and approval — every change to a live solution is logged and requires confirmation that it's worth making.
This is a new service line — we don't yet have published deployments to show off, and we say so directly rather than inventing references. We run first monitoring agreements on preferential terms, in exchange for the right to describe the result, without client data, as a reference case.
The scope covers monitoring one deployed solution on a subscription basis.
Price depends on the monitoring frequency, the number of monitored solutions, and how often the input data actually changes. You know the amount after defining the metrics, not before it.
You set the success threshold. We agree together at the start of the engagement on the level that triggers a response.
No change without your knowledge. Fine-tuning a model requires your approval — we never change a live solution silently.
A decision log for auditing. Every measurement and every model change is recorded in a history you have ongoing access to.
The same team that monitors deployed solutions also builds new pilots and rollouts — so the number of new agreements we take on each month is limited.
Send us which AI solution you've already deployed and when it was last checked — we'll reply within a few business days with the scope of work and a firm price.
Every month without monitoring is a month where a deployed solution's accuracy can decline unnoticed, until it shows up in worse results or customer complaints.
Ongoing accuracy measurement of a deployed AI solution using the same method as at acceptance · a warning before your team feels the decline · model tuning only with your approval · response threshold agreed at the start · a full log of measurements and changes.
We compare current results against the same metrics we agreed at rollout — a deviation from the threshold is visible right away.
Usually not — we make changes in a way that doesn't interrupt the solution's ongoing operation.
We agree on the frequency together with you, depending on how quickly the underlying data changes.
The change requires your approval — without it, we stay on the current version and flag the risk of further decline.
Yes, as long as we have access to the input data and results — we first check how the solution performs today to establish a baseline.
We flag it immediately, regardless of the agreed review cycle — a serious drop in accuracy doesn't wait for the next scheduled report.