PH Deck

Users rely on traditional SMS spam filters (like Apple's) that are keyword-based and cannot detect sophisticated, personalized phishing attacks. sophisticated phishing attacks slip through due to lack of context understanding.

Users rely on manual scrutiny or basic email filters to detect phishing attempts, which are time-consuming and error-prone, leaving them vulnerable to sophisticated attacks.

Users face challenges in preventing bots, fraud, and abuse from accessing their apps, leading to security breaches, compromised user data, and decreased trust in the platform.

Users rely on manual detection and basic email filters to identify phishing threats, which lead to high risk of human error and inefficient detection of sophisticated attacks.

Users currently rely on traditional fraud detection systems that generate high false positives and lack adaptive learning, leading to inefficient security measures and operational delays.

Users rely on manual or less efficient methods to detect phishing URLs, leading to slower response times and higher security risks. Traditional solutions may lack real-time analysis, accuracy, or privacy safeguards, compromising user data and threat prevention.

Users manually check emails for spam triggers or use basic tools, leading to inefficient spam detection and potential deliverability issues due to lack of multi-language support and detailed analysis.

Users are frustrated with LinkedIn profiles that may belong to fraudulent or questionable CEOs. The old solution involves manually assessing LinkedIn profiles, which can be subjective and imprecise. Manually assessing LinkedIn profiles leads to inaccurate judgment about the credibility and trustworthiness of the individuals.