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Managed Services · Sub-service
AI Ops & Model Monitoring
Monitor latency, cost, quality drift, and safety signals for production AI.
Summary
Monitor latency, cost, quality drift, and safety signals for production AI.
Problem
What teams are stuck with
Model quality decays as data and prompts change; nobody notices until users complain.
Token spend surprises finance at month end.
Offerings
What we deliver
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01
AI observability stacks
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02
Drift and eval monitors
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03
Cost anomaly alerts
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04
Provider failover playbooks
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05
Weekly quality reports
Approach
How we work it
Dashboards for quality, cost, and latency with alert thresholds.
Periodic eval reruns against gold sets.
Runbooks for provider outages and quality drops.
Outcomes
What good looks like
- Early detection of quality regressions
- Predictable AI spend
- Faster incident response for AI paths
Related
Keep exploring
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