EvalsHub

About EvalsHub

EvalsHub is an AI quality-assurance platform built around LLM-as-a-judge scoring, aimed at teams still catching regressions through manual spot-checks. You define rubrics as natural-language criteria with weights and thresholds — accuracy matched against ground truth, hallucination held above a confidence bar — and evaluations run continuously against your data, comparing models and flagging regressions before a release rather than after a user finds them. Results are deterministic scores rather than impressions, which is the stated point: the site frames it as bringing traditional engineering rigour to generative output, so you can compare GPT-, Claude- and Llama-family responses on the same rubric and see which passed and which hallucinated. Alongside evaluation there is an adversarial testing surface that red-teams the model automatically: heuristic and LLM-based detection of prompt injection hidden in user input, stress testing against evolving persona-based jailbreaks and DAN-style bypasses, and verification of content filtering, PII leakage and internal policy compliance. Tracing, datasets and experiments are the underlying units — spans, AI-generated dataset rows, experiments and projects — and CI/CD integration puts the whole thing in the release path. Pricing is published in full: a genuinely usable free tier, a $39/mo Pro tier that unlocks red-teaming, A/B prompt tests, online auto-evals and custom LLM judges, and a scoped enterprise tier.

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