Spott is the AI-native ATS/CRM built for staffing and recruiting firms to manage candidates, automate workflows, and close placements faster. AI at the core, not bolted on. Match candidates by context, not keywords. Auto-enrich profiles. Generate candidate presentations in seconds. One platform replacing your entire stack: outreach, note-taking, analytics, scheduling, and more. Connected to mail, WhatsApp, LinkedIn, calendar, and VOIP. Backed by Y Combinator (W25).
Spott started with three friends: Manu, Lander, and Samuel. They met eight years ago studying business engineering at KU Leuven, all ended up in management consulting (McKinsey, BCG, and Bain), and all independently rotated through projects at staffing and recruiting agencies across Europe, the UK, and the Middle East.
They each saw the same thing: recruiters spending half their day logging activities instead of making placements. ATS systems that stored data but never did anything with it. When they started comparing notes, they realized they'd all arrived at the same frustration and the same idea.
Their first instinct was the sensible one: add AI on top of existing platforms like Bullhorn and Salesforce. It didn't work. You can bolt a chatbot onto a relational database, but you can't make it truly understand context. So they made the harder choice. Quit their jobs. Start from zero.
They applied to Y Combinator with a candidate report writer. By demo day, they'd pivoted to a full AI-native ATS built on a vectorized database that understands the meaning of everything stored in it, not just keywords. 60 investor meetings later, they raised $3.2M led by Base10 Partners.
Today Spott is used by recruiting firms across every continent. We're building toward a platform that handles the bulk of recruitment workflows autonomously, so recruiters can focus on what actually requires human judgment: relationships, negotiation, and knowing whether someone will thrive in a role.
solid idea. ats and crm tools tend to become cluttered quickly with data and workflows. are you doing anything specific to keep the system clean and usable over time
Building on a vectorized database from scratch instead of bolting AI onto Bullhorn was the right hard decision. Most recruiting AI startups try to add a layer on top of legacy data models and it never really works. The semantic matching for non-linear career paths alone is a huge unlock for recruiters. Congrats on the YC batch and the Base10 raise.
The WhatsApp and LinkedIn integrations are interesting too, since that's where most recruiting conversations actually happen. Curious about something - when a recruiter logs a quick voice note or informal chat summary, does Spott's AI pick up on those soft signals too, or does it mainly work with structured profile data?
@kevin_vandeputte, the founding story is the homepage. Three McKinsey/BCG/Bain consultants who all independently hit the same wall. That's not a feature. That's a category origin story.
Right now the homepage opens with "Recruiting, rebuilt for today and tomorrow." Every ATS on the internet says a version of that.
But your PH comment tells the real story. Three consultants. Same frustration. Quit their jobs. Built from zero because bolting AI onto existing platforms was never going to work.
That story is sitting in a comment section instead of your hero. And that's exactly where conversions are being lost.
Congrats on the launch.
The attention to detail in every nook and cranny of the UI is simply astounding.
ATS tools I worked with were useless for career switchers — someone moving from sales to customer success would never show up in a keyword search. Context-based matching sounds like it could fix that. How well does Spott handle non-linear career paths where the relevant experience is not in the job title?
Interesting take on rebuilding the ATS around semantic search instead of a relational model. How does that change the way recruiters actually navigate candidate data day to day?
Kevin Vandeputte
Hey Product Hunt! Kevin here from the Spott team.
Spott started with three friends: Manu, Lander, and Samuel. They met eight years ago studying business engineering at KU Leuven, all ended up in management consulting (McKinsey, BCG, and Bain), and all independently rotated through projects at staffing and recruiting agencies across Europe, the UK, and the Middle East.
They each saw the same thing: recruiters spending half their day logging activities instead of making placements. ATS systems that stored data but never did anything with it. When they started comparing notes, they realized they'd all arrived at the same frustration and the same idea.
Their first instinct was the sensible one: add AI on top of existing platforms like Bullhorn and Salesforce. It didn't work. You can bolt a chatbot onto a relational database, but you can't make it truly understand context. So they made the harder choice. Quit their jobs. Start from zero.
They applied to Y Combinator with a candidate report writer. By demo day, they'd pivoted to a full AI-native ATS built on a vectorized database that understands the meaning of everything stored in it, not just keywords. 60 investor meetings later, they raised $3.2M led by Base10 Partners.
Today Spott is used by recruiting firms across every continent. We're building toward a platform that handles the bulk of recruitment workflows autonomously, so recruiters can focus on what actually requires human judgment: relationships, negotiation, and knowing whether someone will thrive in a role.
Would love your feedback!