Explore the governed loop from evaluation to live inference. This demo shows how Orlo compares models, validates responses, traces decisions, and turns feedback into improvement.
A live demo built from the shipped Studio surface and representative data. Click around and inspect the same workflow an engineer or domain lead would see.
Each card below maps to a part of the dashboard above. Together they show the full loop: prove it works, deploy it safely, trace it in production, and improve it over time.
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 team lead sees the summary. An engineer sees confidence intervals. An admin sees costs and compliance. Same data, right level of detail.
Start with the demo, then move into the docs, API guides, or the open-source packages depending on how you want to adopt Orlo.
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