ZeroEval offers an instrumentation layer that records every AI agent interaction and automatically scores outputs using built‑in or custom judges for criteria such as hallucinations, safety, and user frustration. The platform provides real‑time feedback, automated optimization workflows, and local PII redaction, integrating via SDKs, REST, OpenTelemetry, or CLI to help product and engineering teams continuously monitor and improve LLM‑powered agents.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI agents often perform well in demos but degrade in production as untested edge cases cause failures, and there is no systematic way to capture, evaluate, and incorporate real‑world interactions back into the model.
Solution
ZeroEval provides an instrumentation layer that records every agent interaction and automatically scores the output using built‑in or custom judges aligned with a user’s quality criteria. Developers can define binary, rubric‑based, or sample‑rate evaluations for aspects such as hallucinations, safety, and user frustration. When a judge misclassifies, feedback is used to retrain the evaluation layer, improving its alignment with the organization’s standards. The platform then surfaces failure patterns, enabling prompt adjustments to prompts, models, or agent code, and supports automated deployment of these optimizations without redeploying the entire application. An SDK with local PII redaction ensures sensitive data never leaves the host environment, and the system integrates via Python, TypeScript, REST, OpenTelemetry, or a CLI.
Target Audience
ZeroEval is aimed at product and engineering teams that deploy LLM‑powered agents, virtual assistants, or code‑generation tools and need a continuous quality‑monitoring and improvement pipeline.
Features
- Two‑line SDK (Python/TypeScript) or ZeroEval Skills for quick instrumentation of 30+ agents including Cursor, Claude Code, and Codex
- Built‑in judges for hallucinations, safety, and user frustration plus support for custom rubrics and domain‑specific quality metrics
- Real‑time feedback loop: judges can be corrected (thumbs up/down with reasoning) and the evaluation model learns the organization’s standards over time
- Automated optimization workflow that generates prompt, model, or code candidates from production failures and validates them against baseline versions
- Local PII redaction of emails, phone numbers, SSNs, and credit cards before any trace leaves the infrastructure
- Multi‑mode access via SDK, REST API, OpenTelemetry, and a CLI for querying evaluations and triggering optimizations
- SOC 2 Type II compliance and continuous third‑party security audits