orlo See Orlo at work
Your standard for production AI

Hold every AI answer and agent action to a standard your team wrote.

Models you compare, answers your application returns, steps your agents take, and the AI tools your people use all follow the same examples, rules, and skills.

See Orlo at work Compare models
Support tickets. Documents. Agent actions.
01 / The constant From testing to production
Your application Your examples + rules Your chosen model
One standard, through every change.
Bring your models
OpenAI Anthropic Hugging Face Cloudflare vLLM llama.cpp
The Orlo workspace

Review close scores before switching models.

The inbox flags comparisons where the results are too close to separate the candidates. More examples can help you decide.

Inbox

3 items need you.

Ready to decide
gpt-4.1 and the live gpt-4o-mini are too close to call

Support ticket routing. 3.9 points on 180 examples, 95% CI 82.1 to 92.2 against 77.8 to 88.9 for live. More examples would separate them.

Open change ↗

Use the same rules in testing and production.

Keep your task’s examples and rules connected to the release your application uses.

Turn reviewed corrections into evaluation examples.

A live answer can reveal a case your examples missed. Review the correction before adding it to the task’s dataset.

  1. 01 / A correction

    Correct the ticket’s priority.

    A reviewer catches an outage that the model treated as a routine question.

  2. 02 / A reviewed example

    Add the correction to your examples.

    The example records the correct priority and whether a person should handle the ticket.

  3. 03 / The next comparison

    Include it in the next comparison.

    Run each candidate against the reviewed ticket to check whether it makes the same mistake.

See how Orlo checks a live answer.

Explore a support answer with a missing field and inspect the warning Orlo returns with it.

See Orlo at work

About Orlo

Orlo is evals, evidence, and release guardrails for production AI, made by fineSample. Teams use it to compare AI models on their own examples with confidence intervals, release the winner as a frozen version, check every live answer against their rules, decide what each agent step may do, and keep a record of what ran. It runs as a hosted service or on a team’s own infrastructure and calls the models a team chooses rather than hosting any. Orlo is not a model host, an agent framework, or a compliance system. Documentation is at docs.useorlo.com.