MLOps that keeps production AI reliable — and affordable.
Deployment pipelines, monitoring, cost ceilings, and fallbacks for teams running real models in production.
Shipping a model is the easy part. Keeping it accurate, fast, and inside budget for months is the hard part — and it's where most AI projects quietly fall over. We operate the unglamorous, critical layer that stops your AI from degrading or blowing past its bill.
The layer that keeps AI alive in production.
CI/CD for models and prompts
Every change runs through evals before it reaches production.
Monitoring and drift detection
Catch quality drops and data drift before your users do.
Cost ceilings
Token budgets, per-tenant dashboards, and model fallbacks that keep spend predictable.
Incident response
Alerting, on-call, and clean rollback when something breaks.
We treat inference spend as a first-class engineering problem.
Per-tenant cost dashboards, token budgets, and model fallbacks are standard on the systems we run — the same tactics we documented in Putting a cost ceiling on your AI before the bill puts one on you. The result is AI that scales with usage without surprising you on the invoice.
Two ways to engage.
Hand-off
We operate what we (or you) built, with clear runbooks and dashboards.
Retainer
A dedicated reliability engineer, new features on cadence, and drift and cost watch.
We fit your tooling.
Datadog, Sentry, OpenTelemetry, Grafana, and PostHog for observability. AWS and GCP, with Docker, Kubernetes, and Terraform underneath. We fit your existing tooling rather than forcing a migration.