Layer
humnlayer.ai provides a human provenance layer that verifies real humans for AI training, RLHF, model evaluation, and expert feedback workflows. The platform combines active sourcing, human screening, identity checks, and behavioral verification to deliver defensible data quality. It mitigates risks from fake experts, bots, and AI-assisted responses, ensuring data can be traced to verified individuals.
- Artificial Intelligence
- AI Agents
- HR Technology
- Software Only
Funding
Founders
Product
Problem
AI teams pay premium rates for human judgment, but anonymous profiles can hide fake experts, bots, AI-assisted responses, and recycled identities that contaminate the training signal. Weak verification and vendor black boxes make it difficult to prove that the people behind the data are real, which breaks trust in model outcomes and creates compliance risks.
Solution
humnlayer.ai operates as a Human Provenance Layer that verifies and documents every participant in AI data workflows. The company uses active outreach beyond traditional panels, human recruiters to screen for fit and qualifications, and identity, credential, and behavior checks before participation. It manages scheduling, incentives, and live oversight during studies, then produces a defensible trust trail showing who participated, why they were qualified, what they completed, and what risk was reduced. This approach supports preference ranking, model evaluation, expert feedback, and healthcare AI use cases with auditable reasoning and documented provenance.
Target Audience
Primary customers are AI teams that need verified human input for model training, RLHF, evaluation, and expert feedback, particularly those in healthcare, legal, finance, and other high-stakes domains that require defensible data provenance.
Features
- Identity, credential, and behavior verification checks conducted before any participant is allowed into a project
- Human recruiters manually screen candidates for qualifications, consistency, and risk signals
- Active sourcing through referrals, communities, social channels, and past-study intelligence rather than relying on anonymous panels
- Live participation oversight including scheduling, reminders, replacements, and incentive management
- Documented trust trail that records who, why qualified, what completed, and what risk reduced
- End-to-end workflow covering define, source, screen, verify, manage, and document for 12 high-stakes verticals including RLHF, model evaluation, expert feedback, and healthcare AI