Humetric

The company appears to be a self-reported AI-native talent platform called Humetric, built as part of the OpenAI 2026 hackathon. The description states it enables recruiters to find people faster and cheaper via an API or MCP, while applicants can make their profiles AI-searchable and control publicity. It is described as having a working web app, database-backed architecture, and agent-facing interfaces.

What changed: The project was submitted to a hackathon, suggesting a prototype or proof-of-concept stage with no evidence of prior traction or commercial deployment.

The single most important open question: Is there any evidence of actual user adoption, revenue, or real-world testing by recruiters or applicants beyond the hackathon submission?

• The description states Humetric is a public, AI-native talent platform.
• It offers:
  • A database API/MCP for recruiters to search talent.
  • AI-searchable applicant profiles with field-level publicity controls.
  • Public profile pages that are machine-readable.
  • Resume/profile ingestion, consent-aware access logging, and structured data output.
• The platform is built using:
  • Next.js, TypeScript, Supabase, PostgreSQL, pgvector, React, Tailwind, Vercel.
  • It includes API routes, public directory pages, and MCP endpoints for AI agents.

Note: This is a self-reported technical stack. No evidence of actual product deployment or usage beyond the hackathon submission.

• The description states Humetric aims to "recruiters find people faster and cheaper via out database API/MCP".
• It positions itself as an alternative to closed, expensive recruiting databases.
• The platform is described as enabling:
  • AI-native discovery of talent.
  • Applicant control over profile publicity.
  • A consent-first model for data sharing.
• The project evolved from a personal experience in a large headhunting company, where manual searching was inefficient.

Inference: The positioning is framed around AI-native recruitment and user control. However, no evidence of prior market validation or customer feedback exists beyond the author’s own claims.

• Recruiters are described as primary users who search talent via API or MCP.
• Applicants are described as users who make their profiles AI-searchable and control publicity.
• The platform is positioned to serve:
  • AI agents, through MCP endpoints.
  • Job seekers who want to be discoverable without losing control.

Note: No evidence of actual customer segments, personas, or feedback from either group. The description does not name specific industries, roles, or use cases.

• The description states that recruiters can find people faster and cheaper via the API/MCP.
• It implies a data-as-a-service model, where access is monetized through API or MCP usage.
• Applicants are described as publishing their own profiles, suggesting no direct monetization for applicants.

Inference: The business model appears to be B2B SaaS with API access for recruiters. However, no pricing structure, monetization details, or revenue model are provided.

• Built with:
  • Next.js, TypeScript, Supabase, PostgreSQL, pgvector, React, Tailwind, Vercel.
  • Includes API routes, public profile pages, MCP endpoints.
• Features include:
  • Resume/profile ingestion.
  • Field-level publicity controls.
  • Machine-readable APIs.
  • Consent-aware access logging.

Note: The technical stack and features are described in detail. However, no evidence of production deployment or performance data is provided.

• The project was submitted to the OpenAI 2026 hackathon.
• It includes a working web app, database-backed architecture, and agent-facing interfaces.
• The authors state it is not just a landing page but has real functionality.

Absence of evidence: No data on user adoption, retention, revenue, or product usage beyond the hackathon submission.

• The description states that recruiting is still trapped in expensive, closed databases.
• It positions Humetric as an alternative to such platforms by enabling:
  • AI-native discovery.
  • Public, structured talent data.
  • Applicant control over profile publicity.

Inference: The platform competes with traditional talent platforms like LinkedIn, Indeed, or niche recruiting tools. However, no mention of existing competitors or market positioning is provided.

• The project is described as a hackathon submission, suggesting it’s in early-stage prototype form.
• No evidence of:
  • Revenue.
  • Customers.
  • Product-market fit.
  • Real-world testing.
• The platform relies on applicant participation to build its database, which may be a significant adoption barrier.
• The use of field-level publicity controls introduces complexity in implementation and user experience.

Inference: The lack of traction or commercial evidence raises concerns about viability beyond the hackathon stage.

1. What is the actual user base for this platform, if any?
2. How many recruiters or applicants are currently using the API or MCP?
3. Are there any real-world test cases or feedback from users?
4. What is the monetization model for recruiters and how is pricing structured?
5. How do you plan to scale applicant participation and profile creation?
6. What are the technical limitations of the current architecture that could hinder scaling?