PH Deck

Users currently rely on manual processes for document collection during onboarding, leading to delays, missed documents, and inefficiencies in organizing data securely.

Users face challenges in testing complex AI agents that reason, use tools, and make decisions using traditional evals, which lack effectiveness for dynamic real-world interactions.

Users rely on manual or fragmented tools for tasks like writing, SEO, and data processing, leading to inefficient workflows and lack of unified automation.

Users manually search for AI tools across scattered platforms, leading to time-consuming research and inefficient tool comparisons.

Users struggle to organize, customize, and showcase physical or digital collections (e.g., coins, comics) using manual methods (spreadsheets, social media) or generic tools, facing limited customization, privacy risks, and lack of dedicated cloud sync.

Developers and businesses face challenges in creating natural language-based interactions for their documents, data, or applications due to the complexity and technical requirements. The drawbacks include the need for specialized knowledge, high development costs, and the time-consuming nature of building personalized LLM (Large Language Models) based agents from scratch.

Users struggle to find and integrate various AI agents into their projects

Users need to manage and optimize processes across various functions and industries; the old solution involves manual efforts or disparate tools that are not integrated, leading to inefficiencies.

Users rely on traditional document editors (e.g., Word, Google Docs) for creating resumes, essays, and reports, which lack AI-driven automation. Manual formatting, content generation, and iterative editing lead to inefficiency and time wastage.

Users repeating solutions, documenting patterns, and training new devs the same way lead to inefficiency and knowledge silos within engineering teams.