Service · AI Observability

Observability, for production AI.

Models in production behave differently from models in a notebook. Laystone Technologies instruments your AI systems so that quality, drift, and risk are continuously measured, not assumed, and so that regressions are caught before they reach a user or a regulator.

What we instrument

Continuous evaluation

Automated scoring of live and sampled traffic against quality, accuracy, and safety criteria, with calibrated evaluators and golden datasets that evolve with the system.

Drift detection

Statistical monitoring of input distributions, embeddings, and prediction patterns to surface data drift and concept drift before they erode output quality.

Output traceability

Full lineage from every output back to model version, prompt, retrieval context, and configuration, so any result can be reproduced and explained.

Regression gates

Pre-release and shadow evaluation that blocks a degraded model, prompt, or provider update from reaching users, integrated into your MLOps pipeline.

Risk-committee dashboards

Governance views that translate technical telemetry into the metrics oversight functions need for NIS2, DORA, and EU AI Act reporting.

Adversarial monitoring

Detection of prompt injection, jailbreak attempts, and anomalous usage mapped to MITRE ATLAS and OWASP, feeding both security and quality signals.

Get in touch

Let's talk about your project

Tell us where your AI systems run today and what visibility you are missing. We will scope an observability layer that fits your stack, your risk appetite, and your reporting obligations.