PH Deck

Users previously relied on cloud-based tools or manual methods to benchmark LLM performance, which required external infrastructure dependencies and delayed feedback

Users rely on cloud-based AI services requiring internet and uploading sensitive documents/images, leading to privacy risks and dependency on internet connectivity

Users struggle to assess and compare network speeds effectively, resulting in potential dissatisfaction with mobile network providers and inadequate connectivity solutions.

Users struggle to showcase their app or product across different devices in a visually appealing way

The old solution involves manually adding device frames or bezels to screenshots or videos, which is time-consuming and requires design skills

Users need to integrate different LLM providers manually, leading to complex integration processes and high development overhead when switching models

Users struggle to manually research and compare LLM models across multiple sources, leading to inefficiency, difficulty in identifying optimal models, and potential mismatches for their projects.

Users previously had to use different apps for speed measurement and internet testing, leading to inconvenience and fragmented data management.

Users measure real-world objects and spaces using traditional tools like tape measures, laser measures, or manual estimation. Traditional methods are time-consuming, require physical tools, and often lack precision, especially for large or irregularly shaped areas.

IT teams and developers currently rely on traditional tools and methods to benchmark and optimize Local LLMs (Large Language Models), which lack precise benchmarks and standardized performance measurement metrics across different hardware setups.

Decision-makers face difficulty in choosing the appropriate hardware to deploy LLMs due to insufficient data-driven insights.

Users manually switch between multiple LLM tools and platforms, leading to inefficient workflows and fragmented experiences.