HumanJudge offers a platform for real‑time, human‑in‑the‑loop evaluation of AI outputs. Verified domain experts use a Chrome extension or web playground to vote on responses, creating timestamped, attributed verdicts that are aggregated with a time‑decayed algorithm to produce dynamic trust scores, which developers can embed as audit‑ready trust badges via lightweight Node.js or Python SDKs.
Funding
Funding not disclosed
Founders
Product
Problem
AI systems are increasingly deployed in high‑stakes domains, but there is no public, transparent standard for determining whether their outputs are trustworthy. Existing benchmarks provide generic scores and do not capture context‑specific errors, bias, or harmful content, leaving users and regulators without reliable evidence of safety.
Solution
HumanJudge provides a public platform that records, attributes, and aggregates expert human judgments on AI outputs in real time. Domain specialists use a Chrome extension or an integrated playground to vote “Pass” or “Flag” on individual responses, adding reasoning that becomes part of a verifiable, timestamped record linked to their identity. The platform’s time‑decayed aggregation algorithm produces evolving consensus scores rather than static leaderboards, reflecting changing standards and emerging consensus. For AI developers, a lightweight SDK (Node.js or Python) streams production outputs to HumanJudge, where verified experts evaluate them continuously; results sync back to observability tools such as Langfuse and can be displayed as public trust badges. This creates transparent, auditable proof of human‑in‑the‑loop evaluation that can be shown to users, regulators, and partners.
Target Audience
Primary customers are AI product teams and developers who need verifiable trust signals for their models, and domain experts who wish to monetize their expertise by reviewing AI outputs.
Features
- Chrome extension and web playground that let reviewers evaluate AI responses anywhere on the web, capturing context and reasoning.
- Real‑time human‑in‑the‑loop (HITL) feedback loop via a one‑line SDK integration for Node.js and Python, compatible with existing Langfuse setups.
- Time‑decayed aggregation of votes to generate dynamic consensus scores that evolve as more judgments are collected.
- Public, timestamped, and attributed verdicts displayed on reviewer profiles and on model evaluation pages, ensuring auditability.
- Verified domain experts (medical, legal, code, etc.) provide credentialed evaluations, distinguishing them from anonymous crowd‑workers.
- Trust badge and public evaluation pages that developers can embed to demonstrate verified human oversight to end users.
- Open APIs and SDKs for ChatGPT, Claude, and Python, enabling programmatic access to evaluation data across tools.