Loading organizations...
§ Private Profile · 184 High St, Suite 602 Boston, MA 02110
AI talent matching software for HR tech and workforce development, connecting non-college graduates to jobs and improving hiring outcomes.
AdeptID has raised $4.0M across 1 funding round.
Key people at AdeptID.
AdeptID has raised $4.0M in total across 1 funding round.
Boston-based AdeptID develops AI-powered talent matching software and APIs that identify transferable skills to connect individuals without four-year college degrees to in-demand jobs. Operating as a Public Benefit Corporation, the company provides its transparent machine learning models to staffing firms, vocational training providers, and corporate employers to power candidate sourcing and career path recommendations. The early-stage startup operates with fewer than 25 employees and facilitates millions of talent matches monthly across its various workforce applications. To support this growth, AdeptID has raised $3.5 million in total seed funding from lead investors including Zeal Capital Partners, Better Ventures, and Jobs for the Future. The enterprise generates its revenue through B2B software licensing and direct API integrations with major applicant tracking systems like Greenhouse. AdeptID was founded in 2020 by Fernando Rodriguez-Villa and Brian DeAngelis.
AdeptID has raised $4.0M across 1 funding round. Most recently, it raised $4.0M Seed in December 2021.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Dec 1, 2021 | $4M Seed | Zeal Capital Partners | Better Ventures, JFF | Announced |
Key people at AdeptID.
AdeptID has raised $4.0M in total across 1 funding round.
AdeptID's investors include Zeal Capital Partners, Better Ventures, JFF.
AdeptID is a Boston-based Public Benefit Corporation developing an AI-powered talent matching platform that identifies transferable skills to connect hidden talent—especially non-college-educated workers—to in-demand jobs and training faster and more equitably.[1][2][3] Its core product uses machine learning models trained on millions of real-world hiring decisions to analyze work history, skills, education, and seniority, delivering accurate, explainable recommendations via APIs that integrate into workforce apps for employers, training providers, and job platforms.[1][2][4] Serving clients like Avionte, UKG, Year Up United, and Education Design Lab, AdeptID reduces screening time by 90% (from 4 hours to 40 minutes per role), expands talent pipelines 5x, and powers millions of matches monthly while focusing on middle-skilled roles in sectors like healthcare and renewable energy.[1][2][7]
The platform solves the problem of overlooked talent in traditional resume screening, which relies on keywords and credentials, by surfacing "non-linear" career paths and non-obvious transitions for the 80 million U.S. workers without degrees vulnerable to displacement amid tech changes and longer careers.[3][4][5] Growth momentum includes high customer confidence (90% in match accuracy), feedback-driven model improvements, and a mission-driven approach emphasizing fairness through third-party audits.[2][7]
AdeptID emerged from founders' expertise in machine learning, data science, computational neuroscience, big data, and workforce development, driven by the need to make job transitions easier for non-degree holders amid rising displacement risks.[1][4] Co-founder and CEO Fernando Rodriguez-Villa, Chief Data Scientist Brian DeAngelis, and Head of Product Dan Restuccia led the team, founding the company as a Public Benefit Corporation headquartered in Boston, MA, with worldwide activity.[1][3] The idea crystallized around using proprietary hiring outcome data—unmatched by competitors—to train models for middle-skilled roles, starting with a focus on healthcare and renewable energy where transferable skills are key but underrecognized.[4]
Early traction came via MIT Solve challenge participation, highlighting their recommendation engine for high-likelihood transitions, and partnerships with workforce organizations, building on the belief that "everyone is adept" with latent skills for new roles.[3][4] This data flywheel—serving employers to collect outcomes and refine models—pivoted them from general analytics to specialized, API-first talent matching.[1][4]
AdeptID rides the skills-based hiring wave and AI democratization in HR tech, addressing labor market inequities as automation displaces 80 million non-degree workers amid longer careers and tech shifts.[3][4][5] Timing aligns with post-pandemic talent shortages, regulatory pushes for fair AI (e.g., bias audits), and enterprise demand for explainable models over opaque LLMs.[2][7] Market forces like rising upskilling needs in healthcare/renewables and data flywheels from employer adoption favor them, as no competitor matches their outcome-trained models for middle-skilled segments.[4]
They influence the ecosystem as a "keystone" connector for talent apps, enabling inclusive matching that boosts NPS for partners and shares insights with job seekers/training providers, fostering a virtuous cycle of better jobs and data refinement.[1][3][4]
AdeptID is poised to scale as AI talent matching becomes table stakes, potentially expanding beyond middle-skilled roles into global markets with its API edge and fairness creds amid stricter regs.[2][7] Trends like multimodal AI (e.g., inferring skills from unstructured data) and outcome-based pricing will sharpen their moat, while partnerships could ingest even richer datasets for predictive upskilling.[1][4] Influence may evolve from niche enabler to ecosystem standard, amplifying equitable mobility if they sustain the data loop—ultimately proving that surfacing hidden adeptness transforms labor markets, starting with those long overlooked.[3][6]
