Rigel provides a skills‑first hiring engine that lets recruiters define the exact skills, experience, and education criteria for each role and assign weights to them.
Funding
Funding not disclosed
Founders
Product
Problem
Recruiters rely on keyword filters that generate large volumes of irrelevant resumes, hide strong candidates, and provide no clear ranking logic. Candidates must repeatedly rewrite CVs to match vague keyword criteria, leading to wasted time and bias in hiring decisions.
Solution
Rigel offers a skills-first hiring engine that lets recruiters define the specific skills, experience, and education criteria for each role and assign weights to them. The platform automatically scores and ranks applicants against these transparent criteria, producing a reproducible ranking for every candidate. Each ranking includes a detailed breakdown showing how the candidate’s profile matches the defined requirements, allowing recruiters to understand the rationale behind every score. Candidates can see how their profiles align with job requirements, reducing the need for multiple resume rewrites. By replacing keyword filtering with objective, traceable scoring, Rigel streamlines the hiring workflow and reduces bias.
Target Audience
Primary users are corporate recruiting teams and talent acquisition professionals who need objective, data‑driven candidate rankings, as well as job seekers looking for transparent feedback on how they match specific roles.
Features
- Configurable role templates where recruiters set required skills, experience levels, and education weights
- Automated scoring algorithm that calculates a composite match percentage based on defined criteria
- Transparent ranking view that displays a step‑by‑step breakdown of how each candidate’s score was derived
- Real‑time reproducibility: identical inputs and weights always produce the same ranking results
- Dual‑side visibility so both recruiters and candidates can see the scoring logic and data sources
- No reliance on keyword matching; the system evaluates actual skill and experience data from candidate profiles