PH Deck

Developers often struggle with building and implementing customizable, powerful workflows for file-based import use cases, leading to inefficient data import processes and a poor user experience due to lack of flexibility and robustness.

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.

Tech-first organizations face challenges optimizing data quality, cost, failures, data volumes, and durations for specific Data and AI products, and consolidating tooling is difficult. Data Lineage is also a concern.

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.

Current situation with the old solution involves developers and businesses struggling to access and integrate various datasets and APIs efficiently.

The drawbacks of the old situation include the difficulty for developers to locate and leverage high-quality APIs and structured datasets efficiently.

Users need to provision and integrate data services across different platforms like Windows, Unix, or Cloud.

Traditional solutions often require significant technical knowledge and coding skills.

Difficulty in accessing high-quality synthetic data for organizations

Developers face challenges in building and scaling in-app experiences due to the need to integrate multiple 3rd-party data sources, leading to increased complexity and development time.

Users face challenges in deploying AI applications with their data. Current solutions often involve complex setups with multiple tools and technical expertise required. The drawback is the difficulty to ingest data from multiple sources and deploy applications quickly, as well as concerns over privacy and unnecessary features.

Users currently rely on traditional methods of gathering financial data, such as manual searches on financial websites or outdated data platforms. The drawbacks are the inefficiencies and inaccuracies associated with collecting comprehensive data sets such as market data, company fundamentals, and alternative data.