Asriel is a research intelligence platform that helps companies find and hire scientists by ranking candidates based on the evidence in their published work rather than CVs or referrals. The platform indexes researchers from domains like protein engineering and machine learning, using ORCID profiles to generate industry translations and match candidates to specific roles. It also offers researchers a free, private profile that remains hidden until a relevant opportunity matches.
Funding
Funding not disclosed
Founders
Product
Problem
Companies struggle to identify top scientific talent because the best researchers are often passive and invisible outside their immediate networks. Traditional hiring methods rely on CVs, keyword filters, and referrals, which fail to capture the depth of a researcher's published work or their true problem-relevant expertise. This leaves both companies and researchers trapped in a closed loop where discovery depends on who you already know, not what you have accomplished.
Solution
Asriel provides an infrastructure layer that reads and evaluates researchers' published work to rank them by evidence, not credentials. The platform indexes thousands of papers across domains like NeurIPS, Nature, and Cell, scoring each publication against the specific requirements of a job description. For researchers, connecting an ORCID iD automatically generates an industry translation of their expertise, making them discoverable to companies without applying. For companies, Asriel delivers structured matches before the first interview, reaching candidates across institutions and research groups far outside their existing networks.
Target Audience
Primary customers are research-driven companies in fields like biotech, AI, and materials science that need to hire scientists with deep, specialized expertise. The platform also serves researchers who want to be discovered by industry without actively applying.
Features
- Indexes researchers by their published work, not CVs or LinkedIn profiles, covering domains like protein engineering and machine learning
- Scores papers against job-specific requirements using a capability framework that extracts actual research needs, not keywords
- Provides a free, passwordless ORCID-verified profile for researchers that stays private until a role matches
- Generates an industry translation for each researcher, mapping their research depth to specific roles and company needs
- Reaches passive candidates across institutions and domains that are invisible to traditional job boards and referral networks