Risk management for agentic systems
Your experts set the bar. Your AI has to clear it. Automate the work, not the judgment — and stay in control of what ships.
AI adoption is outpacing risk controls
Demo success ≠ production permission. Shipping AI comes with unique failure modes traditional software never had.
Risk owners are stuck with two bad options.
Stall the rollout
Hold the release by lack of confidence — and leave value on the table.
Ship and hope
Release it anyway and hope nothing breaks — with no evidence for what it actually does in production.
Align your AI with business outcomes.
Three core components that bring standards and rigour to shipping AI products.
Define what correct looks like
Human experts author input → output scenarios that encode business processes into pass/fail tests. Business IP becomes a compounding data asset and serves as context for any AI product.
- Domain expertise captured as data IP
- Compounding scenario test suite
- Reusable context for any AI product
Set release gates
Employ the scenarios to stress-test the AI. A positive outcome gives business the confidence to release, and engineers the freedom to iterate quickly.
- Clear release evidence for business
- Faster, safer release cycle for engineering
- Swap prompt, model, or provider with confidence
Benchmark your AI
The release gates serve as a common yardstick to measure AI performance, quantify improvements, and compare different models and AI providers on capability and cost-effectiveness.
- Model and provider independence
- Business-readable performance scores
- Iteration-over-iteration improvement tracking
A scenario, end-to-end
From the expert's pen to the deployment gate — every scenario passes through the same four stages.
Built for regulated environments
No data leaves your perimeter. No decision goes untracked.
- Audit trail
Continuous audit log
Who did what, when? Every significant change captured continuously and automatically. Out of mind, there when you need it.
- Capture
- Continuous, append-only
- Tracks
- Ground truth, models, prompts, experiments
- EU AI Act evidence
Structured for the Act
Avoid expensive hours tracking down data. Arbitr shapes your audit logs against EU AI Act articles.
- Mapped to
- EU AI Act articles
- Output
- Structured evidence bundles
- Data residency
Runs on your infrastructure
Runs in your cloud, on your terms. Scenarios, prompts and model outputs never leave your perimeter.
- Deploys
- Customer cloud or on-prem
- Data egress
- None — source data stays in your perimeter
Notes from the lab
New components, open benchmarks, and notes from our research.
OCR Cost Comparison
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Public leaderboards
Compare models and agent architectures on business-relevant performance and risk signals.
Open evaluation framework
Our evaluation approach is transparent, auditable, and community-driven.
Common questions
What kinds of AI can Arbitr test?
Any system you can call — LLMs, agents, RAG pipelines, vision and document-extraction models, classical ML classifiers. If it has an input and an output, Arbitr can score it.
How is this different from an LLM eval tool?
Eval tools score the model. Arbitr scores the workflow against the business outcome it has to deliver — durable across model swaps, meaningful to non-technical stakeholders.
Who is Arbitr built for?
The team that owns the AI outcome — product, risk and compliance, and the engineers shipping the system. Domain experts write scenarios in business language; engineering wires the agent and the CI gate.
We have custom or fine-tuned models — does Arbitr integrate?
Yes. Arbitr calls your model through whatever endpoint you expose — hosted API, self-hosted server, or a model in your VPC. Custom prompts, fine-tunes, RAG stacks and agent frameworks all plug in the same way.
Where is our data stored?
Inside your perimeter. Arbitr deploys into your cloud or on-prem. Your scenarios, prompts, model outputs and audit logs stay on your infrastructure — nothing leaves, including to us.
How do we get started?
Get in touch — we will walk you through a pilot scoped to one of your workflows.