Explore the governed loop from evaluation to live inference. This demo uses a fraud workflow as the example, but the pattern applies anywhere AI output needs to be evaluated, controlled, evidenced, and reviewed.
A live demo built from the shipped Studio surface and representative data. Click around to inspect the workflow from model evidence to production trace.
Each card below maps to a part of the dashboard above. Together they show the loop: evaluate before launch, guard production behavior, preserve evidence, and improve from reviewed feedback.
Upload your labeled examples and Orlo tests multiple AI models against them. You see scores with confidence ranges, so you know exactly how reliable each model is before deploying.
A visual comparison of how models perform. Green means winner, yellow means too close to call, red means eliminated. One glance tells you which model to deploy.
Every AI response is checked before it reaches downstream systems. You define the business rules, and Orlo can warn, retry via fallback, or fail closed. When configured confidence is too low, it abstains instead of guessing.
See exactly what happened for every AI call: what went in, what documents were retrieved, which model answered, and what came out. Full transparency for every decision.
When your team corrects an AI mistake, that correction enters a governed review queue, gets approved, and becomes curated evaluation data for the next selection cycle. The system improves from your team's expertise without hiding the review step.
Not everyone needs the same view. A domain owner sees the decision summary. A platform engineer sees confidence intervals. A risk reviewer sees controls and evidence.
By the end of the workflow, Orlo has created a set of artifacts that explain why the AI decision was allowed, how it was checked, and how it can be reviewed later.
The selected model is backed by task-specific examples, score ranges, sample counts, cost, latency, and uncertainty-aware comparison.
The live workflow is tied to a specific task version, model, strategy, and control set instead of a loose prompt or ad hoc model call.
The production output is checked against the expected contract, with room for abstention, escalation, fallback, or review when policy requires it.
The request, retrieval, model, validation, and output are kept together so the decision path can be reconstructed.
Reviewer corrections can be staged, approved, and promoted into future evaluation data instead of disappearing into logs.
Domain, platform, and risk teams can inspect the same workflow at the level of detail they need.
Start with the demo, then move into the docs, API guides, or a design-partner conversation depending on how you want to adopt Orlo.
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