Arbitr
Arbitr provides a risk management platform that lets human experts author input-output scenarios to test agentic AI systems before release. The platform automates testing while keeping human judgment central, enabling teams to swap models or providers with confidence. It also publishes a public OCR mini-bench comparing 31 models across 42 business documents with 13,020 runs.
- Artificial Intelligence
- Developer Tools
- Regulatory & Compliance Technology
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Risk owners deploying agentic AI systems face a dilemma: either stall rollouts due to lack of confidence and leave value on the table, or release without evidence of how the AI will actually behave in production. Traditional evaluation approaches fail to capture the nuanced, domain-specific judgment needed to safely ship these systems.
Solution
Arbitr provides a risk management platform that lets human experts author input-output scenarios encoding business processes into pass/fail tests. The AI under evaluation consumes each scenario's input and returns its actual output, which gets stamped onto a scenario card for transparent assessment. A positive outcome gives business teams the confidence to release and engineers the freedom to iterate quickly. The platform supports swapping prompts, models, or providers with confidence, while domain expertise becomes a compounding data asset and reusable context for any AI product.
Target Audience
Primary users are risk owners, business experts, and engineering teams at enterprises deploying agentic AI systems who need evidence-based release gates and continuous evaluation capabilities.
Features
- Expert-authored input-output scenarios that encode business processes into pass/fail tests
- Automated testing that runs the AI on every input and stamps actual outputs onto scenario cards
- Compounding scenario test suite that turns business IP into a data asset
- Model and provider swap support, enabling side-by-side comparisons without rework
- Public OCR mini-bench comparing 31 models across 42 business documents with 13,020 runs, measuring critical-field success, pass^n reliability, latency, stability, and cost per successful outcome