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Navneet Chalanaleft a comment
25 years in US technology staffing. The pain was always there. I just finally got angry enough to build. A client called one day — a candidate we presented had fabricated their entire employment timeline. Three companies, four years, complete fiction. Passed every manual screen. Cost us the relationship. Signals that told me it was worth solving: — 1 in 3 profiles had some form of misalignment...
What Pain-Point are you Solving and How did you discover it?
Jake FriedbergJoin the discussion
Navneet Chalanaleft a comment
This hits close to home. We built WW Intelligence for the exact same reason — bad data upstream destroying decisions downstream. Our version: staffing firms running campaigns, submitting candidates, feeling good about their pipeline. Then the client calls. The resume was fabricated. The timeline was fiction. Same problem, different domain. Fake clicks pollute your marketing data. Fake profiles...
We Built a Bot Detection Engine Because Our Own Marketing Data Was Bad
Nkosilathi NyoniJoin the discussion
Navneet Chalanastarted a discussion
Hey PH! 👋 I'm Navneet — Founder of WackoWave
25 years in US technology staffing taught me one uncomfortable truth: 1 in 3 candidate profiles carry verifiable risk — fabricated timelines, credential mismatches, location anomalies that pass every manual screen. One bad placement costs a staffing firm their client. One bad hire costs an enterprise 6× the annual salary. So I built WW Intelligence — the AI layer between resume and reality....
Navneet Chalanaleft a comment
1 in 3 candidate profiles carry hidden risk. How is your team currently catching it before the hire?
<p>AI forensic engine</p>
Navneet ChalanaJoin the discussion
Navneet Chalanastarted a discussion
<p>AI forensic engine</p>
I built an AI forensic engine that catches resume fraud in 30 seconds — AMA before we launch on April 7