Deployment pipelines
Models and application code move as versioned artifacts through tested promotion gates.
CAPABILITIES / GOVERNANCE AND OPERATIONS
MLOps and run
Keeping it working after launch, which is where most AI programs quietly fail.
What this covers
Monitoring and alerting
Service health, input drift, output quality, and spend trigger alerts with named owners.
Retraining and tuning cycles
New reviewed examples enter scheduled evaluation before any candidate replaces production.
Incident response
Runbooks define containment, rollback, evidence capture, and communication for each failure class.
Ongoing operation under retainer
A standing team owns the queue of model changes and production issues with the client.
How it shows up in the work
ENTERPRISESOne platform for a growing agent workforce.The first agent is a project. The tenth is a platform problem. Shared retrieval, model routing, monitoring, and tuning are what make the second agent take weeks instead of months.ENTERPRISESCatch defects before they move downstream.Inspection that happens on a schedule finds problems late. Cameras and agents at the line can watch continuously and route what they find to the people who fix it.BUSINESSESRun leaner with agents on the busywork.Intake, quoting, scheduling, chasing. The middle of the day disappears into work that has a pattern. Agents can carry the pattern and leave your team the judgment.
WHERE IT FITS
Operate is the natural fit, often following the team that delivered the proof and production release.
