Reliability, observability, governance and capacity for ML models, LLM applications and AI agents.
Talk to a MLOps / LLMOps AIOps Specialist
Each service can be taken alone. They share telemetry, policy and runbooks when combined.
Operate model endpoints with SLOs, quality signals, version context and safe rollback.
Example scenario
Deploy a degraded model version, detect the regression and roll back.
Trace prompts, tokens, tool calls and agent steps while enforcing operational policy.
Example scenario
Have an agent attempt a disallowed action and show policy interception plus the trace.
Forecast inference demand and tune scaling, batching and placement while protecting latency.
Example scenario
Generate an inference spike and scale predictively while preserving latency.
Detect feature, prediction and performance drift and trigger validation and retraining pipelines.
Example scenario
Shift the synthetic input distribution, detect drift, retrain and promote conditionally.
Monitor model, prompt and tool policy compliance, data handling and AI-system risk.
Example scenario
Run a prompt-injection or policy-violation test and show blocking plus evidence.
Every service is built as a local-first lab, then extended to your private cloud and to AWS, Azure or GCP.
Ingestion health, processing latency, decisions, automation success and failure, overrides and false positives where measurable.
Validated, allowlisted actions with approval rules and least-privilege identities.
Healthy, false-positive, policy-blocked, automation-failure, rollback and telemetry-source-failure cases, all exercised.
Retained evidence for every decision, a short demo script, and setup, troubleshooting and cleanup documentation.
Rules every AIOps action follows, in every track.
AI output never goes straight to a shell, cloud API or cluster. It selects from validated runbooks.
Disruptive actions wait for a person. Low-risk, reversible steps can run on their own once you agree the rule.
Every action is followed by a health check, with rollback or compensation when the check fails.
Signals, scores, model or rule versions, approvals and results are retained and reviewable.
Adapted to the tools you already run.
Combine tracks to share telemetry, policy and runbooks.
Tell us what you run and where operations hurt most. We will suggest where to begin.
Talk to an AIOps Specialist