PH Deck

Users manually track work hours and productivity using spreadsheets or basic software, leading to inefficient tracking of work hours, productivity, and project progress in real-time, resulting in delayed insights and potential project mismanagement.

Users manually check Product Hunt votes and trends, which is time-consuming and lacks historical tracking.

Users manually track team skills, performance, and project assignments via spreadsheets or fragmented tools, causing inefficient resource allocation and delayed risk detection.

Product managers and teams rely on traditional training methods or self-learning for AI skills, which are time-consuming and inefficient upskilling and struggle to integrate AI into product development workflows, leading to slow adoption and limited ROI.

Users manually calculate team efficiency using spreadsheets or basic tools, which is time-consuming, error-prone, and lacks real-time industry benchmarks.

The current situation is that users rely on traditional methods for monitoring employee productivity, which often lack accurate and real-time insights into work habits.

Drawbacks of the old situation include: lack of accurate and real-time insights into work habits, inefficient process tracking, and limited accountability measures.

Users struggle with manual and time-consuming product data management processes when searching for specific products.

Drawbacks of the old situation: Manual data entry, outdated product information, and lack of insights for decision-making.

Product teams currently rely on scattered tools and inconsistent templates for managing product requirements and reviews, which can lead to inefficiency and lack of cohesion. The drawback of this old situation is the reliance on scattered tools and inconsistent templates, making it cumbersome to maintain streamlined product management processes.

Users need to manually monitor webcam feeds for specific objects or activities, which is time-consuming and prone to human error. Manual monitoring and lack of automated detection reduce efficiency and reliability.

Users conducting product team meetings or user interviews face difficulties in recording meetings and transcribing them accurately without the intrusion of bots into the calls. This often leads to privacy concerns and inefficient post-meeting reviews due to unorganized recordings and poor-quality transcriptions.