PH Deck

Users face challenges with traditional AI models that lack multimodal capabilities, particularly in visual math, science, and complex reasoning tasks, leading to limited accuracy and contextual understanding.

Users face challenges in tackling complex problems with traditional reasoning methods

Drawbacks of the old situation: Traditional methods may not efficiently handle complex problems, leading to time-consuming processes and limited learning capabilities.

Parents looking for the best organic cot mattress in Australia might struggle to find a high-quality option that is non-toxic, allergy-controlled, temperature-regulated, made with coconut coir, and 100% latex breathable.

Users manually generate embeddings, detect objects, infer attributes, and query multimodal data, which is time-consuming, error-prone, and requires complex coding/scripting.

Current situation: Parents looking for organic cot mattresses in Australia face challenges in finding non-toxic, allergy-controlled, and temperature-regulated options.

Drawbacks: Limited availability of organic cot mattresses meeting the specified criteria.

Users rely on large language models (LLMs) for reasoning tasks in math, science, and code, but these models require high computational costs and resource-intensive infrastructure.

Users need AI models capable of complex sequential reasoning tasks but rely on larger, less efficient models requiring multiple processing steps or more parameters, leading to higher computational costs and slower performance.

Users need to understand the context or story behind visual elements but current solutions recognize but don’t explain them, leading to incomplete insights.

Users currently manually track health data across multiple apps and struggle to gain personalized insights from fragmented information.

Enterprise AI solutions like GPT-OSS-120B require high computational resources and lack private deployment options, leading to uncontrolled operational costs and security risks.