PH Deck

Users relying on base LLMs with limited vision, reasoning, and long-context capabilities face challenges in handling multimodal AI tasks efficiently

Users rely on traditional VLMs with limited context windows and dense architectures, leading to inefficient multimodal reasoning and inability to handle long-context tasks.

Users rely on existing large language models (LLMs) with limited coding proficiency and shorter context windows, leading to inaccuracies in long-text scenarios and inefficient code-related tasks.

Currently, users struggle with creating comprehensive, multi-file AI prompts, which hampers the efficiency in tasks such as writing, coding, and research.

Creating comprehensive, multi-file AI prompts is a complex task leading to inefficiencies.

Users struggle to create complex Excel formulas as they require specific syntax and logic understanding, leading to time-consuming formula creation and potential errors.

Users manually convert JSON to Excel or use external tools, leading to time-consuming processes, data privacy risks with online tools, and inability to handle nested JSON structures.

Users manually convert Excel data to JSON using external tools or custom scripts, which is time-consuming and error-prone, requiring technical coding skills and risking data privacy during file exports.

Users struggle with manual formula creation and data updates in Excel, leading to time-consuming processes and potential errors.

Traditional night vision devices often provide low-resolution images, limited color range, and restricted view distance low-resolution images, limited color range, and restricted view distance.

Users seeking advanced AI models for research or application are often limited by closed, proprietary solutions with high costs and restricted flexibility. These restrictions can stifle innovation and limit customization.