PH Deck

Users previously relied on traditional A/B testing platforms that required PR processes and incurred delays, PR bottlenecks, bloated systems, and high platform tax.

Users rely on traditional key-value (KV) stores like Redis for data storage and retrieval in high-performance applications.

The drawbacks of these old solutions include limited speed and efficiency under heavy load and lack of persistence capabilities.

Creators, writers, and authors struggle to generate high-quality content across text, images, video, and audio, which can be time-consuming, labor-intensive, and may lack creativity.

Users struggle with traditional data science processes that involve coding, lack of collaboration, and time-consuming experiments.

Drawbacks: Traditional processes hinder productivity, limit collaboration, and slow down the development and deployment of machine learning use cases.

Businesses, agencies, and freelancers currently create and manage advertisements manually or with basic tools. This process requires considerable effort and technical expertise.

Enterprise developers and IT teams build workflow applications using traditional coding methods, which are time-consuming, costly, and require specialized technical expertise.

Users need to manage fragmented tools for different AI/ML development stages (data preparation, model training, deployment), leading to integration complexity, workflow inefficiency, and delayed deployment cycles.

The current situation for users involves manual API testing, which is commonly time-consuming and prone to human error. Moreover, the traditional process of running API tests can be fragmented and lacks integration with modern tools. The drawbacks include the need to manually test APIs, leading to inefficiencies, and the inability to automate and schedule these tasks effectively.

Users rely on manual resume screening and traditional interviews for hiring processes, leading to inefficient candidate evaluation and high time consumption

Users needing to launch crypto exchanges face high development costs, long time-to-market, and regulatory compliance challenges with traditional custom-built solutions.