UniversAIMS: what we watch after a system goes live

A model that performs well on launch day is not a finished product. Data changes, vendors change their APIs, staff find new ways to use the system and new ways around it. Without someone watching, an AI system degrades quietly until the people who depend on it stop trusting it.

UniversAIMS, the UniversAI Management System, is the platform we use in the Partnership phase to keep every system we deploy accurate, affordable and in use. It is how we keep the commitments we make after launch.

Four things we measure continuously

1. Availability and speedUptime and response times for each production system, with alerts when either moves outside the agreed range.
2. Accuracy and driftHow far the system's outputs move from their validated baseline over time, by system and by task.
3. Running costToken spend and API usage, tracked against budget, with renewals handled before quotas run out.
4. IncidentsUpstream changes, schema breaks and vendor outages, routed to our engineers before they reach your staff.

Why we built it ourselves

General monitoring tools tell you whether a server is up. They do not tell you whether an invoice matcher has started matching the wrong invoices, or whether a document reader has become less reliable on a new waybill format. The questions that matter for an AI system are specific to the work it does, so the monitoring has to be specific too.

The useful question is not whether the system is running. It is whether it is still right.

From signals to decisions

Monitoring only matters if someone acts on it. UniversAIMS feeds three regular decisions: when to retrain or recalibrate a model, when to change how a system routes work to keep costs down, and when to bring a finding to leadership. The last of these happens in a monthly review that reconciles time saved and cost reduced against the baseline set in Discovery.

• Retraining and recalibration as products, customers and processes change
• Routing and caching changes that reduce running cost without reducing quality
• Monthly ROI statements reviewed with leadership
• A record of every incident, its cause and its fix