Orlo helps teams evaluate models on real operational data, deploy approved AI workflows, validate production outputs, govern agent actions, and capture the evidence needed for risk, compliance, and audit review.
Orlo helps organizations turn expert judgment into governed AI systems that improve with every decision. Govern before production. Apply guardrails in production. Keep every AI decision reviewable.
Decision Evidence
A model response is not enough for high-stakes work. Orlo keeps the task, evaluation, deployment snapshot, validation result, retrieval attribution, approvals, feedback, and trace evidence connected.
Evaluate candidate models, prompts, and retrieval strategies against operational examples. Use confidence intervals and thresholds to decide what is ready and what needs review.
Validate responses, enforce schemas and business rules, route uncertainty, govern tool requests, and require approval when an AI step should not run unattended.
Preserve the artifacts reviewers need to reconstruct what happened: task version, model choice, source evidence, validation result, agent steps, and human feedback.
How It Works
Orlo turns AI delivery into an operating loop: approve the workflow before launch, control behavior in production, and keep evidence after each decision.
Describe what AI should do, who owns it, what data it can use, what output shape is allowed, and which controls must apply before the workflow reaches production.
Test models, prompts, and retrieval strategies on examples from your actual operations. Orlo reports scores with uncertainty so teams know when a winner is real and when the result is too close to call.
Freeze the task version, model, prompt, retrieval behavior, and validation policy into a deployment snapshot. Production behavior is tied to a known approval state.
Apply runtime guardrails: validation, grounding, abstention, escalation, tool policy, and approval gates. Guardrails are the production controls inside the broader governance loop.
Keep request, response, model, retrieval, validation, deployment, token, latency, feedback, and agent-step evidence together so reviewers can reconstruct what happened.
When domain experts correct a response, the correction enters a governed pipeline: staged, reviewed, promoted into the dataset. The next evaluation incorporates it. The system gets better from the expertise of the people who use it.
Capabilities
Orlo sits above models, retrieval, applications, and agent runtimes so teams can manage the decision path instead of only watching traffic.
Compare models, prompts, and retrieval strategies under the same task, dataset, scoring method, and budget. Confidence intervals show whether the observed difference is meaningful.
Bind the selected model, task version, strategy, and controls into a reproducible deployment snapshot so teams know exactly what configuration is live.
Check every response against schemas, rules, confidence thresholds, and task-specific constraints. Orlo can reject, retry, abstain, escalate, or require review.
Connect responses to approved source material and retrieval evidence so reviewers can see which context influenced the answer and whether it was grounded.
Govern tool requests, approvals, timeouts, trace samples, and session events in external agent runtimes, including steps an LLM gateway may never see.
Preserve task versions, datasets, evaluation results, deployment snapshots, validation outcomes, retrieval attribution, approvals, feedback, and traces for review.
Product and SDK Surface
Orlo gives platform, domain, risk, and operations teams a shared product surface, plus developer primitives partners can embed into high-stakes AI workflows.
See active workflows, evaluations, deployments, validation health, and review pressure in one place.
Compare candidates on task data, inspect confidence intervals, and choose what is ready to deploy.
Track live inferences, validation outcomes, latency, costs, drift signals, and runtime behavior.
Review sensitive actions, handle escalation, and keep human oversight tied to the decision record.
Inspect tool decisions, step traces, timeouts, policy checks, and agent trajectories outside the model call.
Use web components for evaluation results, feedback review, inference traces, policy editing, approval queues, and session views.
Bring Orlo controls into external agent runtimes without forcing teams to replace their existing orchestration layer.
Manage provider access, workflow integration, and deployment boundaries without hard-coding model infrastructure into every app.
Adjacent To Gateways
LLM gateways are useful for access, routing, rate limits, and cost controls. Orlo works at the workflow layer: task evidence, model selection, validation, approvals, agent steps, feedback, and reviewable decision artifacts.
Which application called which model, how long it took, how much it cost, and whether the request succeeded.
Whether the model was evaluated for the task, whether the output matched the approved contract, whether it was grounded, and who reviewed sensitive steps.
Keep gateways for traffic infrastructure. Use Orlo to prove high-stakes AI workflows are correct, justified, and reviewable.
Use Cases
Orlo is defined by the risk profile of the workflow, not a single industry. It fits regulated, sensitive, or business-critical operations where AI output needs to be controlled and reviewed.
Classify requests, extract structured context, route uncertainty, and preserve review evidence for workflows where the next step matters.
Evaluate document-heavy decision workflows, validate structured outputs, and keep source attribution and reviewer feedback attached to each decision.
Classify alerts by risk level and recommended action. Compare models on real examples, deploy the winner, and route uncertain cases to review.
Answer policy questions using approved documents, ground answers in retrieved sources, and retain evidence for risk, compliance, and audit teams.
Support caseworkers and internal teams with governed AI workflows that can be reviewed, explained, and improved over time.
Extract structured terms from contracts and operational documents. Validate outputs, record trace evidence, and promote reviewer corrections into future evaluations.
Deployment Choice
Use external providers when they fit the workflow, or run the stack in your own environment when data residency, infrastructure control, or sovereign AI requirements matter.
The interactive demo runs on mock data. Explore a high-stakes workflow, inspect evaluation results, validate a production output, and trace the decision evidence end to end.