Direct Answer: Independent evidence is one objective fact about a visit, such as a blocked challenge iframe mismatch. Cross-checked context means BotRefund tests whether other signals support the same story. A single clue is only the starting point. The final classification comes from an...

Direct Answer: IP blocking relies on static blacklists, which modern botnets can easily bypass with rotating proxies. Client-side detection analyzes behavior and browser signals in real time. BotRefund uses 110+ on-site telemetry signals to identify bots that IP blocking misses. This approach also provides...

Direct Answer: Rule-based detection relies on fixed thresholds or single signals, which often miss sophisticated bots or generate false positives. AI-based detection, like BotRefund model, evaluates the complete pattern of evidence from 106 independent checks. The AI weights all signals together, learning which combinations...

Direct Answer: The free Starter tier allows up to three hundred bot detections per month. It includes the full verification dashboard and self‑service refund report generation. If you exceed the limit, you can upgrade to a paid plan for unlimited coverage.

Direct Answer: Independent evidence adds objective facts about a visit. BotRefund collects 106 such facts, including the Error Stack Proxy check. Each fact is cross-checked against others. This corroboration is what allows the tool to reach 99% accuracy instead of relying on a single...

Direct Answer: The ‘Visited History’ check compares the browser’s reported history state with what a real user would have after navigating the web. Automated browsers often expose missing or placeholder history entries. BotRefund flags these inconsistencies as part of its evidence. It’s a strong...

Direct Answer: BotRefund uses a prediction AI that weighs the complete pattern of signals from browser, network, device, and behavior evidence. Instead of relying on raw rules, the model evaluates how all signals fit together. This allows it to identify visits as bot or...

Direct Answer: Bot detectors look at hardware, graphics, fonts, audio, and operating-system details to see if they naturally fit together. A real browser usually reports details that match the device, while an automated browser may claim one device but reveal another through its processor...

Direct Answer: You need detailed forensic evidence that proves the clicks were automated. BotRefund provides verified GCLID/FBCLID click IDs, millisecond timestamps, campaign names, session replays, and explanations of why the session was automated. This is the exact format that Google and Meta's review teams...

Direct Answer: It is important to know that your tools are built by experienced professionals. SEATEXT AI is led by a team with a 20-year background in online marketing, CRO, and technology. This deep understanding of conversion rate optimization practices is baked into the...

Direct Answer: BotRefund looks for unnatural patterns such as perfectly straight mouse paths, sub‑millisecond click speeds, and sessions that never scroll or interact. These behaviors are rare in real users and signal automated clicks. When the system flags these sessions it adds them to...

Direct Answer: BotRefund’s experts prepare the evidence package, write the dispute, and negotiate directly with Google or Meta on your behalf. They handle all communication with platform reviewers, so you don’t need to draft technical arguments yourself. The service includes priority support for high‑value...

Direct Answer: They often build partial or placeholder implementations that do not hold up when checked from multiple angles. BotRefund's Background Navigation check looks for these internal inconsistencies. Then it cross-checks the anomaly with independent browser, network, device, and behavior data. That is why...

Direct Answer: Automated browsers often patch or hide browser APIs to look more like a real user. But those patches can be detected when the same APIs are checked from another angle. BotRefund's toString Patch Shadow check is designed to catch that mismatch. It...

Direct Answer: Automated browsers often patch or stub browser APIs to hide their automation, which can leave incomplete or inconsistent navigation timing data. BotRefund's check looks beyond the surface to find these stubs. Even sophisticated bots may miss this detail. The signal then becomes...

Direct Answer: Automation tools often patch only the surface APIs and leave other parts incomplete. When BotRefund checks beyond the surface, it finds missing or simplified capabilities that a real browser would expose consistently. This mismatch is reflected in the Chrome API Stub signal.

Direct Answer: Automation tools often patch browser APIs to hide, but those patches do not always hold up under a different kind of check. The Iframe Webdriver Leak check in BotRefund is designed to spot that kind of break. Because it is one of...

Direct Answer: Browser API inconsistencies can reveal automated browsers that patch or hide their true environment. BotRefund uses a native patching check to spot these mismatches, comparing what a real browser shows versus what an automated one reveals. Because many legitimate situations can cause...

Direct Answer: Because a single browser tell can be caused by a real user. Privacy tools, corporate networks, travel, and unusual devices can all create unexpected behavior. BotRefund uses 106 independent checks to build a reliable picture. The AI model only makes a decision...

Direct Answer: Because a single network signal is not enough. BotRefund uses 106 independent checks and sends them into an AI model that evaluates the whole pattern. This catches bots that would slip through a tool relying only on IP reputation or one browser...