AI-OPS – monitoring and managing AI in production

AI systems behave differently from classic software. Providers update their models, data changes, users ask unexpected questions and costs grow unnoticed. AI-OPS is the discipline that keeps all of this under control.

What we monitor

• Availability – are the agents running, are the providers’ APIs responding, are there errors.
• Quality – accuracy of answers, the rate of successfully completed tasks, cases of “hallucination”, user feedback.
• Speed – response time and bottlenecks.
• Costs – usage by agent, department and task; anomalies; forecasts.
• Security – manipulation attempts (prompt injection), data leaks, unusual behaviour.

What we do

Evaluations (evals)

A set of test scenarios that run automatically on every change of model, instructions or data – to make sure an improvement in one place hasn’t made things worse elsewhere.

Optimisation

Choosing the right model for each task, caching, shortening instructions and batch processing – often with a significant cost reduction at the same quality.

Model updates

When a provider releases a new model or retires an old one, we test and migrate in a controlled way.

Knowledge maintenance

Updating the knowledge base the assistants rely on when prices, products and rules change.

Incident response

Alerts to an on-duty specialist, quick shutdown or rollback to the previous version, and root-cause analysis.

Reports

A monthly report with quality, availability and cost metrics, the improvements made and recommendations.