AdeptID’s API makes it easy for anyone building a Future of Work application to include ML in the way they discover talent without college degrees. We connect talent to jobs and training on the basis of transferable skills (rather than job titles).

Changing jobs is incredibly hard, particularly for folks without college degrees, and I think this is connected to a lot of problems we struggle with... AdeptID has built models that identify transferable skills people have picked up that would make them successful in new roles. They’ve already helped some big employers rethink the way they find and promote talent. Now they’re releasing this API to make it easy for anyone building a Future of Work app to connect hidden talent to jobs and training. They’re a driven team with big hearts and an inclusive mission and they’re eager for feedback from you! ♥️

Thanks for hunting @nikkielizdemere! ?makers I was reading a report on the labour market by ILO and can truly relate to your mission, to provide an opportunity to the hidden talent to realize their potential. Have you collaborated with some skill and training institutes, which fall outside the traditional 'institutions'?

@nikkielizdemere @adityavsc We're fortunate to work with some great vocational training institutions (like Year Up https://www.yearup.org/) that offer apprenticeship programs for Opportunity Talent. They've taught us a lot about potential pathways into high-growth industries. A lot of the employment outcomes data we use to train our models come from these partners.

Good morning Product Hunters (and thanks @nikkielizdemere )! Brian and I started AdeptID last year because we think economic mobility for people without college degrees is profoundly messed up and it’s causing A LOT of problems. We’re not the only ones who think so, and there are a lot of great Future of Work product people out there building software to solve parts of the problem. All of these builders need matching to personalize experiences and connect hidden talent with jobs and training - using real skills, not job titles or degrees. It turns out the ML approaches required to do this well are similar to what @briguy609 and I have worked on our whole careers. So we went out to build a recommendation engine that uses multiple skill taxonomies, real employment outcomes and ML techniques to identify talent in non-obvious places. Skills-based matching and technology isn’t a new idea, but it’s hard to use, siloed, and way too expensive - which is crazy because it's a technology that will only improve with more adoption! We want to take our models, which we’re proud of, and make them accessible to large and small organizations - like what Stripe has done for payments. Why we think it’s great: 💡 Cutting-edge ML models (previously only available to big companies) ⚖ Reduces bias (by focusing on skills rather than titles or degrees) 📃 Transparent, thorough docs (not hidden behind paywalls or NDAs) 📈 Learns from outcomes (quality of recommendations improves over time) We’ve got lots of work to do, but we’re excited to do that work with partners among the awesome maker community on ProductHunt!

AdeptID

One API to connect hidden talent to training and jobs

One API to connect hidden talent to training and jobs

AdeptID

One API to connect hidden talent to training and jobs

One API to connect hidden talent to training and jobs

Do you use AdeptID?

Do you use AdeptID?

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