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.

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.

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.

Users often need to manually input and manage contacts from Excel spreadsheets into their phone, which can be time-consuming and error-prone.

Users need to manually split large Excel files (XLS, XLSX, CSV, etc.) or rely on Excel-dependent tools, which is time-consuming and requires Excel installation.

Users face tedious data processing tasks in Excel

Manually analyzing data in Excel is time-consuming and error-prone

Users manually enter data from images or PDFs into Excel, which is time-consuming, error-prone, and inefficient.