Run AI in production with confidence
CTOs, platform and ML teams
Production AI has more moving parts than normal software: models, prompts, agents, tools, providers and human review. Each team watches a different slice.
A shared record for every team
Traces, evals, spend and product events live together, so platform, ML, product and support look at the same runs when something changes.
Give each team its own dashboards on the same data, alert on what matters, and investigate from the same traces.
Observe
Bring traces, logs, metrics, evals, cost, user behavior, and process events together.
Orient
Map issues to the affected model, agent, workflow, team, customer segment, or KPI.
Decide
Use root cause, eval evidence, policy state, and impact to prioritize response.
Act
Route incidents, trigger workflows, report status, and close the operational loop.
What you get
- Give executives and platform teams a shared operating model for AI.
- Reduce incident confusion across product, ML, engineering, support, and security.
- Turn observability data into ownership, decisions, and repeatable operating workflows.
Is this only AI observability?
Observability is a core input, but Anosys is broader: it combines observability, evals, cost intelligence, governance, root cause analysis, and workflows.
Who owns Anosys in an organization?
Most teams start with engineering or platform, then expand to ML, product, support, governance, and operations leaders.