PH Deck

Users manually process and clean data in Google Sheets, which is time-consuming and error-prone for tasks like data extraction, summarization, and structured formatting.

Businesses often struggle to manage recurring work, create efficient automations, and optimize their business processes effectively, leading to decreased productivity and operational inefficiencies.

Users are tired of copying and pasting charts, formatting slides, and making the same presentation every week for their Google Analytics reports. These manual tasks are time-consuming and redundant.

Users struggle to write content efficiently within Google Docs, facing issues like writer's block and time consumption.

Businesses often struggle with interpreting and structuring complex data, leading to inefficient data management and missed insights. The inefficient data management and missed insights from complex data are the main drawbacks.

Users struggle with manual data entry, complex data enrichment, and lack efficient ways to conduct research or validate information directly in spreadsheets. These processes can be slow, error-prone, and inefficient, hindering productivity and decision-making.

Data science tasks require specialized knowledge, which makes it difficult for non-experts to perform advanced analytics. This leads to barriers in leveraging data-driven insights for business or personal use, resulting in missed opportunities and slower decision-making processes. The specialized knowledge requirement is a significant drawback.

Users previously relied on separate tools for image and video creation, leading to fragmented workflows, higher costs, and time-consuming processes.

Users struggle with managing and querying unstructured data using traditional data warehouses, which require complex Python scripts. The inability to handle unstructured data easily and the need for technical script writing are significant drawbacks.