
Smartflow Maestro
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AI sprawl grows fast. Coordination usually doesn't
Teams experiment with different models. Costs balloon. Quality varies. Guardrails drift. Most orgs end up flying blind, adjusting knobs by instinct instead of data.
Maestro gives you a single pane of control for the entire AI estate, aligned to business goals instead of ad-hoc decisions.
Define policy-as-code for data handling, egress, model access, jurisdictions
Apply controls globally or scoped per team, app, or workflow
Set business objectives like cost ceilings, latency targets, or quality thresholds
Continuously tune guardrails and routing as usage patterns shift
Evaluation harnesses plug into your workflows
Traffic mirroring compares models side-by-side
A/B and canary routing measure quality before full rollout
Outcome analytics show what’s working, what’s wasting, and what’s drifting
Cost per prompt, per token, per team
Quality scores and pass/fail criteria
Compliance posture and policy adherence
Drift, anomaly, and spend alerts
Historical trends for CFO, CISO, and CIO reporting
SSO for access controls
SIEM for security events
ITSM platforms for approval workflows and incident tickets
FinOps systems for automated showback/chargeback
Smartflow for policy enforcement and execution
Maestro + Smartflow: Control, Optimize, Enforce
1
Console and dashboards
2
Data rules, egress, model accessJurisdiction constraintsScoping: global, team, workflowVersioning, rollback, audit logs
3
Evals and test harnessesTraffic mirroring and shadow testsA/B and canary routingOutcome and quality analytics
4
SSO / IAMSIEM / ITSM / FinOpsWebhooks, approvals, tickets
5
Inline inspection and policy checksCost/latency routingCaching, deduplication, safety filtersLogging and telemetry
6
OpenAI, Gemini, Anthropic, Llama, on-prem, custom
Without a console like Maestro, orgs rely on scattered docs, inconsistent routing, ad-hoc spending controls, and slow, manual updates. Maestro brings order and evidence to everything that happens above the GPU.
See Maestro in action.
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