Human Provenance Layer
humnlayer.ai operates as a Human Provenance Layer that verifies and documents every participant in AI data workflows. The company combines human recruiters, identity and credential checks, and live oversight to produce a defensible trust trail for AI training data. It serves high-stakes verticals including RLHF, model evaluation, expert feedback, and healthcare AI.
- Software Only
- Artificial Intelligence
- AI Agents
- HR Technology
Funding
Founders
Founder details are not available yet.
Product
Problem
AI teams pay premium rates for human judgment, but anonymous profiles and weak verification can hide fake experts, bots, AI-assisted responses, and recycled identities that contaminate the training signal. Without documented provenance, trust in model outcomes breaks down, creating data-quality and 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 end-to-end workflow covers defining, sourcing, screening, verifying, managing, and documenting participants for preference ranking, model evaluation, expert feedback, and healthcare AI use cases.
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