PH Deck

Knowledge workers spend excessive time researching complex data manually, leading to inefficient workflows and time consumption.

Developers struggle to navigate the fragmented and rapidly evolving AI tooling ecosystem, leading to inefficiency in identifying relevant tools and staying updated with new solutions.

Users struggle to provide efficient and effective customer support, API documentation, guides, and sales chat.

Users require advanced large language models (LLMs) for commercial applications but face limitations with proprietary models such as high costs, restrictive licenses, and limited customization.

Users currently rely on manual methods or basic search functions within traditional knowledge bases to access business-critical information, leading to inefficient search processes, time-consuming information retrieval, and inability to leverage contextual understanding for accurate results.

Users rely on traditional AI note-taking tools like Fireflies AI and OtterAI, which are proprietary systems leading to limited customization, potential data privacy concerns, and dependency on closed-source platforms

Users struggle to identify whether they have been naughty or nice in the open-source community based on their GitHub activity alone.

Enterprises currently rely on custom-coded AI solutions requiring significant development resources. Drawbacks include requiring significant development resources, time-consuming deployment, and limited extensibility.

Users struggle to efficiently manage and integrate large language models (LLMs) into their applications, facing complexities in handling prompts, operations, and datasets.

Users face challenges in generating comprehensive AI-powered reports from web search results.

Drawbacks of the old solution: Manual report generation is time-consuming, prone to errors, and lacks the scalability needed for large datasets.