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. 4-layer forensic verification in under 30 seconds. Before you present. Before you decide.

We're DPIIT recognised, patent pending, and running pilots across US staffing firms and enterprise hiring teams.

I built an AI forensic engine that catches resume fraud in 30 seconds AMA before we launch on April 7

So here's what happened. We were running campaigns, watching our click metrics climb, feeling pretty good about performance. Then we started digging into where those clicks actually came from.

Half of them were bots.

Not simple ones either. Headless browsers mimicking human behavior perfectly. Selenium scripts automating clicks at scale. Click farms using mobile devices. Advanced stuff rotating IPs, spoofing geolocation, faking mouse movements, generating realistic referrer patterns. Fingerprinting evasion. Timing tricks. Some were so good they looked completely human.

We realized most link tools just count clicks. They don't ask if those clicks are real.

We re all builders here, which usually means at some point we looked at something clunky, slow, or frustrating and thought, there has to be a better way. Most products don t start with a grand vision; they start with irritation, curiosity, or firsthand pain.

I d love to learn more about how others here have navigated that journey:

How did you uncover the problem you decided to work on?
What signals told you this problem was worth solving?
How did you validate (if at all) whether people would actually pay for a solution?
Has your product stayed true to the original problem, or did it evolve into something different?
What surprised you the most along the way?