Pipelines
CI/CD that runs eval suites, regression tests, and quality gates before AI changes reach production.
DevOps and Automation
The delivery loop that ships AI safely.
Deploying AI is not like deploying code. Model updates, prompt changes, and eval regressions need their own release controls. We build the pipelines and automation that make AI changes traceable, reversible, and safe to ship.
Pipelines
Rollback
Observability
Focus areas
Delivery systems built for AI-specific change.
Production AI depends on a delivery loop that treats model updates, prompt changes, and eval thresholds as first-class release artifacts — not afterthoughts.
Release safety
Canary deploys, feature flags, and rollback paths designed for model swaps, prompt updates, and config changes.
Operational automation
Automate eval runs, drift detection, and alerting so production AI systems stay healthy without manual babysitting.
Common triggers
Usually this work starts when AI changes feel risky to ship.
No eval gates
Model or prompt changes go to production without automated quality checks — and regressions show up in user-facing output.
Risky releases
Shipping AI changes feels stressful because there is no rollback path and no way to compare before and after.
Manual operations
Eval runs, drift checks, and quality reviews happen ad hoc instead of as part of the delivery pipeline.