Orlo evaluates models on your data with confidence intervals, validates every production response, and governs retrieval, feedback, and agent steps in one platform. Use external providers or run the stack in your own environment.
Independent evaluation and safety tooling is being pulled into model-provider stacks. Promptfoo is joining OpenAI. Humanloop joined Anthropic. Teams that need a provider-agnostic governance layer still need a neutral option. Orlo fills that gap.
How It Works
From task definition to live deployment, Orlo keeps evaluation, validation, feedback, and governance in one loop.
Describe what the model should do. Set input and output schemas. Provide a prompt template. Orlo versions everything immutably — no silent changes to production behavior.
Use examples from your actual operations. These become the evidence base for model selection: your edge cases, your formats, your languages, your quality bar.
Run 2–4 candidate models against the same dataset in one evaluation. Orlo reports accuracy with confidence intervals and budget limits. If two models are too close to call, it says so instead of inventing a false winner.
One click. The winning model goes live with a frozen deployment snapshot. Every response passes through deterministic validation — schema checks, business rules, and custom rules. Depending on policy, Orlo can reject, retry, abstain, or require review.
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
Evaluate OpenAI, Anthropic, and self-hosted models under the same scoring and budget. Orlo tells you when a winner is real and when the result is still too close to call.
Schema enforcement, business rule checks, and custom logic execute inline on every response. This is a live gate on production behavior, not an after-the-fact dashboard.
Hybrid retrieval combines keyword and semantic search, then traces the answer back to the source material. You can see which documents influenced the response and why.
Every inference keeps request, response, deployment snapshot, validation result, retrieval attribution, token counts, and trace IDs together for debugging, review, and audit.
Each org uses its own credentials, encrypted per org and resolved at runtime. Orlo keeps data and access scoped at the organization level across the platform.
Run Orlo in your own environment when you need tighter control over residency, infrastructure, or model hosting. Or connect external providers when that is the right fit.
Sovereign AI
Compute and open models are only part of the stack. Teams still need evaluation, validation, retrieval, auditability, and controlled deployment before AI can be trusted in production.
Use Cases
Classify transaction alerts by risk level and recommended action. Compare models on real alerts, deploy the winner, and route uncertain cases to review.
Classify incoming tickets by category, urgency, and team. Prove the model works on your support history before it touches real operations.
Answer policy and compliance questions using your own documents. Every answer is grounded in retrieved sources with traceable attribution.
Extract structured terms from contracts and similar documents. Keep outputs well-formed with validation, then improve the workflow with reviewer feedback.
The interactive demo runs on mock data. Explore a fraud workflow, inspect evaluation results, and trace an inference end to end before you connect your own systems.