AdeptID develops machine-learning-powered talent matching software that identifies and connects hidden talent with job opportunities, focusing on non-linear career paths often overlooked by traditional recruitment methods. By leveraging real employment data and an easy-to-use API, the platform enhances sourcing efficiency for staffing firms, resulting in faster placements and larger talent pipelines.
Funding
$6.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional recruitment methods often overlook candidates with non-linear career paths, leading to missed opportunities for both employers and job seekers. Existing talent platforms may not accurately identify or value skills and experiences gained outside of conventional employment histories. This results in inefficient sourcing processes and smaller talent pipelines for staffing firms.
Solution
AdeptID provides an AI-powered talent-matching platform that identifies and connects individuals with diverse career backgrounds to relevant job opportunities. The platform's machine-learning models analyze real employment data to recognize talent often missed by traditional methods, focusing on skills and experiences gained through non-linear career paths. AdeptID integrates directly into existing talent stacks via an API, providing skill-based match scores and personalized recommendations. By learning from real employment outcomes, the platform enhances sourcing efficiency, expands talent pipelines, and helps staffing firms make faster placements.
Target Audience
AdeptID primarily targets staffing firms, talent acquisition platforms, and organizations involved in training and workforce development.
Features
- AI-powered talent matching based on skills and experience, not just traditional resumes
- Machine-learning models trained on over 2 million job transitions per month
- API integration for seamless connection to existing talent management systems
- "Narrative Recommendations" using GenAI to explain why candidates are a good fit
- Focus on identifying and matching talent with "non-linear" career paths
- Skill-based match scores and personalized recommendations
- Feedback loops that allow the AI to learn from real employment outcomes