AI observability

At Cashcrown, we build this layer as part of every deployment: Prometheus metrics, Grafana dashboards, threshold alerts, and regression tests on a golden set. The model router logs every call: cost, latency, model, and outcome. Sensitive fields are masked before logging in accordance with PII rules.

When quality metrics drop below the threshold or the query distribution shifts relative to the baseline, the system notifies the team. Diagnosis and the decision to fix remain human responsibilities, and human-oversight is built-in, not optional.

How much does this service cost, and when does it pay off?#

We work in tiers based on scope: we start with a limited-cost pilot so you can see the value before a larger investment. Observability often reveals inefficient LLM calls, whose elimination covers the deployment cost within a few weeks. You can calculate the exact ROI using the ROI calculator; we don’t provide upfront pricing because it depends on the actual scope.

Does it integrate with our systems and comply with GDPR?#

Yes. We plug into your existing stack via SDK or proxy, without rewriting integrations. PII is masked before logging, and the audit trail is required by the AI Act for high-risk systems. GDPR compliance is designed in from the start: log retention, access control, and the right to erasure are built into the architecture.

Where do we start?#

With an audit of your existing AI system: we check what’s already measured and what only becomes visible after something goes wrong. Then we deploy a minimal observability layer on one process, verify alerts, and expand the scope. More on the approach: how to choose your first process.