ClearRole

The description states that ClearRole is a tool that analyzes job descriptions for trustworthiness. It currently supports pasting text of a job listing into a web interface and returns:

• A 0–100 listing score;
• Risk level (low, medium, high);
• Verdict;
• Evidence-aware confidence level;
• Reasons linked to specific categories;
• Positive, weak, and risk signals;
• Input and source limitations;
• Recommended next action;
• Verification checklist.

It is built with Next.js, React, TypeScript, and uses OpenAI's GPT-5.4–5.6 models via the Responses API. The demo does not store or log pasted content, and only performs one AI request per input.

The product is described as being in an early prototype stage, with a planned Chrome extension to improve user workflow.

Inference The product appears to be a proof-of-concept for AI-assisted job-listing vetting, not yet a full commercial offering.

The description states that ClearRole was created to address the trust gap in job listings — particularly where AI-generated or fake listings are common. It positions itself as a decision-support and provenance layer for the job market.

It claims to help both job seekers and platforms by evaluating:

• Listing quality;
• Source credibility;
• Employer identity;
• Freshness;
• Duplicate history;
• Fraud indicators;
• Hiring-intent signals;
• What remains unknown.

The author describes three layers in its long-term vision:

1. Ghost Job Detector (consumer-facing);
2. ClearRole Trust Engine (B2B use for platforms);
3. Source Intelligence Layer (technical foundation for provenance).

Inference The positioning suggests ClearRole aims to become a trust layer for the job market, but only the first layer (consumer-facing) has been demonstrated.

The description states that ClearRole targets two main audiences:

1. Job seekers who want to evaluate whether a listing is worth applying to.
2. B2B platforms (job boards, staffing companies, employers) that need tools to identify low-quality or fraudulent listings.

It also mentions that the long-term goal is to build a system that helps career professionals and job platforms understand whether a listing appears credible, current, transparent, and worth acting on.

Inference The ICP is likely early-stage job seekers and platform operators, but no customer data or segmentation details are provided.

Not evidenced. The description does not state how ClearRole intends to monetize its product or what pricing model it will use.

The description states that ClearRole is built with:

• Next.js
• React
• TypeScript
• Codex (as engineering partner)
• OpenAI GPT-5.4–5.6 models via the Responses API

It demonstrates a vertical slice where:

• A user pastes a job description.
• The browser sends it to a Next.js /api/check-text route.
• The server makes one GPT-5.6 request.
• The result is parsed and normalized into a canonical format.

The demo path:

• Performs at most one OpenAI request;
• Does not fetch public URLs;
• Stores no pasted text server-side;
• Does not log the input or raw model output;
• Is limited to localhost and non-production execution.

It also mentions foundational work for:

• Canonical result contracts;
• Evidence categories;
• Scoring policy;
• Confidence controls;
• Source-quality separation;
• Controlled public-source reading;
• Network and private-address protection;
• PostgreSQL-backed authorization and accounting;
• Listing history and observation concepts;
• Change detection;
• Source discovery.

Inference The technical architecture suggests a scalable, privacy-conscious approach to AI-based job listing analysis, but only basic functionality is demonstrated.

Not evidenced. The description states that this is a hackathon submission (OpenAI 2026 Build Week), and no traction or adoption data are provided.

Not evidenced. The description does not mention any competitors or market positioning relative to existing tools in the job-listing or AI trust space.

1. No traction or revenue: The product is a hackathon demo with no evidence of adoption or monetization.
2. Limited scope in demo: Only one vertical slice (pasted text) has been demonstrated; the full vision remains unproven.
3. Privacy and data handling: While the demo avoids logging, it's unclear how future versions will handle user data or source intelligence.
4. AI reliability and bias: The system relies on AI models to detect fraud or inconsistencies — this introduces risk of false positives/negatives.
5. Scalability concerns: The current architecture is limited to one request per input; no scaling or batch processing is described.

1. What are the key assumptions behind the scoring and risk signals?
2. How does ClearRole differentiate between a low-quality listing and a legitimate one that simply lacks detail?
3. Has there been any user testing or feedback on the accuracy of the AI-generated verdicts?
4. What is the plan for data privacy, especially around source intelligence and provenance tracking?
5. Are there plans to integrate with existing job platforms or APIs?
6. How will the B2B Trust Engine be monetized?
7. What are the technical limitations of the current model in detecting fraud or impersonation?
8. How does ClearRole handle edge cases like internal company job postings or niche roles?