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We capture who candidates actually are, what teams actually want, and match on the result.

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Recognize this?

You've watched application volume explode. AI-generated resumes, mass-apply tools, and lower-friction sourcing have flooded every role. The hiring system can’t keep up.

You've tried tools: ATS upgrades, AI screeners, sourcing platforms. Each promises to speed up the funnel. None fixes the real problem. Even after you push through the volume, the right candidates still don't surface.

The work that matters is finding the person who's actually right for the role, and it's gotten harder, not easier. Sourcing means digging through LinkedIn for someone worth talking to. Inbound means scanning resumes for signal that resumes never carried in the first place. The tools speed up the motion. They don't change what you find at the end.

The system isn’t broken because people aren’t trying. It’s broken because the inputs are wrong. Resumes compress people into keywords. Job descriptions miss how teams actually evaluate. Every tool layered on top optimizes the wrong thing.

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Volume up, signal down

Application volume per role is up ~60% in a single year. AI tools are accelerating noise on both sides: generated resumes in, automated filtering out. Diamond in the rough candidates get missed in the wash

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Tools that optimize the wrong layer

Workable filters faster. Juicebox translates LinkedIn. Every existing tool is racing to manage broken inputs faster instead of fixing what the inputs are. Spend on recruiting tech climbs; outcomes don't.

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Sourcing is still manual. That's where the work lives.

The biggest time sink isn't processing applications. It's hunting through LinkedIn, networks, and inbound for the rare candidate worth a conversation. Existing tools don't help here; they translate keywords and filter resumes. The judgment work: who's actually right for this role, still happens one profile at a time.

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The shift

Hiring is treated like a volume problem. It's actually a data problem.

The signals that matter, how someone thinks, where they're strong, what a team actually values, are rarely captured. So the system defaults to proxies like AI resumes and keyword filters.

Kora flips the model: capture the real data on both sides, then match on it.

What Kora looks like

Kora helps teams find the right candidate by understanding who they actually are.

For the role: we capture how teams actually evaluate, from hiring discussions, decision criteria, and examples of past hires you'd want to clone, and turn it into a structured rubric. Not a job description. The actual evaluation framework.

For the candidate: we capture what matters from everything they produce: interviews, work samples, online profiles, even resumes. Not a self-edited summary. The full signal.