MLOps / LLMOps & AI Observability

Getting a model to work once is a different problem from keeping it working. We build the release path: prompts and models under version control, a review process matching the one your application code already goes through, and evaluation running in CI so a change that lowers answer quality fails the build instead of reaching customers.

In production, the things worth watching are cost, latency, drift and safety. We instrument all four and put them where your on-call engineer already looks, rather than in a separate tool nobody opens. Token spend in particular has a habit of growing quietly until somebody reads the invoice.

This suits teams who already have something live and are finding it hard to change safely. The first thing we do is reconstruct how a release actually happens today, which is rarely the process written down.