For teams deploying AI into regulated, sensitive, or business-critical operations, the hard problem is not only generating an answer. It is proving the workflow was evaluated, approved, controlled, evidenced, and reviewed.
The Problem
Teams can prototype quickly with model APIs and prompt tools. Production is different. Domain owners need reliable outputs. Platform teams need operating controls. Risk and compliance teams need evidence. Audit needs to reconstruct what happened later.
Policy documents describe intent, but they do not prove which model was approved, which controls ran, or why a decision was allowed.
A gateway can show that a model answered. It usually cannot prove the task was evaluated, the output was valid, or a sensitive action was reviewed.
An offline test does not keep a workflow safe after launch. Production needs validation, monitoring, feedback, and an evidence loop.
The Orlo Model
Orlo is the AI control plane for high-stakes workflows. It helps organizations turn expert judgment into governed AI systems that improve with every decision.
Capture the task, schema, prompt, retrieval context, owner, validation rules, and approval expectations as a versioned workflow.
Compare models and strategies against domain examples, with confidence intervals and uncertainty-aware recommendations.
Freeze task version, model, strategy, and controls into a reproducible deployment snapshot.
Validate outputs, ground answers, route uncertainty, govern tool use, and require approval for sensitive steps.
Connect task, evaluation, deployment, runtime trace, validation, attribution, approval, and feedback artifacts for review.
Promote corrections and trace samples into future evaluation datasets so production experience improves the workflow.
Who Uses It
Standardize evaluation, deployment, validation, credentials, monitoring, and agent governance across workflows.
Prove AI works on the workflow they own, inspect production behavior, and turn expert feedback into better evaluations.
See which controls exist, when review happens, and what evidence supports a high-stakes AI workflow.
Reconstruct the decision path from task approval through production output, validation, attribution, and feedback.
Control credentials, deployment boundaries, provider access, and runtime behavior without scattering governance across apps.
Connect policy expectations to operational controls and reviewable artifacts without turning Orlo into a legal sign-off system.
What Orlo Is Not
Those systems can still exist in the stack. Orlo governs the workflow around them: evaluation, deployment, validation, attribution, approvals, feedback, monitoring, SDK components, and evidence.
Explore the demo, then use the docs to inspect the workflows, APIs, and components behind it.