Problem

AI agents can produce thousands of outputs while leaving teams unable to tell which interactions were genuinely good. Manual QA and offline tests are slow and brittle, and complex multi-step agents generate noisy logs that make failures difficult to reproduce. The operational consequence is that every model, prompt, or agent release becomes risky to validate, especially when quality depends on long conversations, tool calls, or domain-specific judgment.

ZeroEval targets the highest-cost version of this problem: production AI systems where mistakes can cost lives or millions. Its killer use case is helping a team measure and improve the reliability of an agent already serving users, using real interaction traces and human judgments rather than relying only on synthetic tests or intuition.

Product / Service

ZeroEval is an AI-agent evaluation and improvement layer. Teams instrument agents with its SDK to capture traces, including inputs, outputs, model choices, and prompt versions. They can attach thumbs-up/down ratings, corrections, expected outputs, or other human feedback, while automated judges score interactions against criteria such as helpfulness, domain accuracy, format compliance, and business rules. The resulting feedback is available across traces, spans, completions, and sessions through the console and API.

The system is designed as a feedback loop rather than a one-time test suite. Judges can be corrected with labeled examples until their evaluations match a team’s quality bar; ZeroEval can then use the feedback to identify patterns, catch regressions, compare models, and optimize prompts. Its Autotune workflow evaluates model-and-prompt combinations on live or replayed traffic and helps teams promote better-performing configurations with evidence, reducing the engineering effort required to build a custom evaluation pipeline.

Market

ZeroEval competes in the emerging LLM and AI-agent evaluation, observability, and production quality-management market. Its closest alternatives include LangSmith, Langfuse, Weights & Biases’ prompt and evaluation tooling, Arize Phoenix, and Humanloop. ZeroEval’s differentiation is the combination of production tracing, human-feedback-driven calibrated judges, and automatic prompt/model optimization, rather than observability or experiment tracking alone.

The company is an early-stage entrant rather than an established platform. The YC company profile lists the business as active, founded in 2025, with a two-person team, and public company data indicates a $500,000 pre-seed round backed by Y Combinator. The team’s earlier LLM Stats product reached tens of thousands of monthly active users and hundreds of thousands of unique users, providing some distribution evidence, but the available public materials do not establish current revenue or customer counts, so it is more accurate to describe ZeroEval as early-stage than definitively pre-revenue.