Qualifai

Track

Evolving Workplaces

Inspiration

Your ATS would reject Steve Jobs. A Reed College dropout with resume gaps and no mention of scrum or cross-functional collaboration would not make it past filters, despite his adaptability and vision. He represents the 66% of non-traditional candidates wrongly excluded from job consideration.

In 2024, 88% of hiring managers admitted they overlook non-traditional applicants due to ATS filters, even when those candidates show potential. These roles then stay open for months, costing up to $10,000 per unfilled position each month and over $1.5 million annually in lost productivity. Non-traditional hires are not fallback options. 91.1% of companies that hire based on skills report improved diversity, and those with above-average diversity generate 45% of their revenue from innovation.

At the same time, 44% of job seekers have been unemployed for over a year. This drives mass applications, with some roles on LinkedIn receiving more than 1,000 submissions.

We built this system to fix that. Your AI should act as your assistant, not your gatekeeper. Let it surface top-fit candidates from all backgrounds so you fill roles faster, strengthen your team, and never miss the next Steve Jobs.

What it does

Qualifai uses AI to analyze applicants’ resumes, cover letters, links, and screening questions to build a holistic view of who they are and what their unique qualities are. Instead of using AI to continuously automate and autoreject in a binary fashion, Qualifai surfaces the unicorns of your talent pool to ensure that the unique stories are told and considered. You don't want to pass on the next Steve Jobs just because he doesn't fit in the cookie-cutter career journey. Increasingly, non-traditional applicants bring a wealth of valuable skills to the workforce, yet hiring practices have not evolved to accommodate this shift. We are.

How we built it

• Concept ideation, back and forth ideating with Oli, did a comp analysis to see what are the common gaps and real root causes of the problem, system architecture, UX UI, feature list, scope, project management

Challenges we ran into

• How the hell do we showcase the uniqueness of every single person fast enough to remain an effective tool to hiring managers? Speed but depth?
• Having to develop both sides of the user experience as its a dual problem, we cant just solve one side.
• Applying API’s with no budget, having to localize data for the demo without having to pay for this project.

Accomplishments that we're proud of

• Applying the depth and capabilities of nlu models for semantic reasoning to solve the depth requirements we needed.
• Really innovating the space of these typically plain and just speed focused tools to bring back the humanity and appreciation of human journeys.
• Having the agentic applicant screening to reduce anxiety and easy of use for the application process. Tell your story to a listening ear.
• Doing all this in a week while having events every day and only 3 of us.

What we learned

I learned to apply fast iterative prototyping for proving proof of concept of the project, rather than doing the UI from scratch, I developed the app from a detailed user flow using Replit to then refine and clean up after. Saved a lot of time for both design and build.

What's next for Qualif.ai

• Refine and build out each feature. Expand the AI capabilities to provide even more depth for hiring managers, not just for applicants, but for job trends, market trends, company insights, etc.
• Flesh out the application side of the experience.
• Integrate into existing systems to see how easy it would be to set up on sites like LinkedIn, Indeed, etc.

Built With

• figma
• openai
• replit