We Built a Bot Detection Engine Because Our Own Marketing Data Was Bad
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.
So we built something different. We analyze every single click in real-time against 100+ bot detection signals. We're looking for browser automation flags, Selenium and PhantomJS and Puppeteer signatures. Rendering engine discrepancies. Timing inconsistencies. Headless browser signatures. Impossible geographic travel patterns. Click velocity, if someone's generating 10 clicks per second from the same fingerprint, that's a bot. Suspicious referrer patterns. Real-time threat feeds to block known bad actors.
When we detect a bot, we handle it. Harmful traffic gets blocked. Sophisticated bots get flagged and redirected. Your data stays clean.
It's called Lynkbee. Free forever, paid plans when you need more.
We're pretty proud of it. Would love to hear if this is a problem you've run into too.
Replies
Sounds strong to give it a try. It is lynkb.ee, right?
@lreverchuk Appreciate it. Yes, Lynkb.ee
Great to know. Yes lot's of bot are out there scraping and ruining site traffic! How has the results been after launching this product? Did you see a significant drop in clicks but better lead gen?
@calvin_lim_1Definitely. Since we launched Lynkbee, bot and low-quality clicks dropped significantly, and our lead gen improved.
Here's why: We're analyzing every click against 100+ bot detection signals, Selenium signatures, headless browser patterns, impossible geographic travel, click velocity spikes.
When we block sophisticated bots and flag suspicious traffic, what's left is actually real.
That means campaigns show which traffic sources, geos, and audiences genuinely convert.
Customers stop wasting budget on click farms and rotated IP farms. Targeting gets way sharper because of the data the product gives. Cost-per-lead drops because we're optimizing based on what we prove is real.
@nkosilathi_nyoni1 Amazing! Bot farms are a huge problem in many lines often giving you inaccurate data. When this happens most people think their the problem without checking data quality. You essentially cleaned up your data to get a better picture of your business. Love it!
@calvin_lim_1 100% That's the core mission. To help as many businesses achieve this (that rely on clean data)
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 pollute your hiring decisions. Both cost real money before anyone notices.
Solid build. The 100+ signal approach is the right way to do it — single signals are too easy to game.
— Navneet, WackoWave · AI forensic verification for candidate profiles