PH Deck

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.

Architects, real estate developers, and urban planners manually create physical scale models for presentations, which is time-consuming, resource-intensive, and requires specialized craftsmanship.

Users previously relied on generic AI models lacking domain-specific reasoning, transparency, and multilingual capabilities, limiting accuracy and adaptability in specialized tasks.

Users rely on traditional AI models (LLMs) for decision-making which lack transparent reasoning processes. Lack of transparent reasoning processes, no audit trails, dependency on neural networks

Users rely on proprietary language models with restricted licenses, leading to limited customization, high costs, and dependency on vendor-specific ecosystems.

Users currently rely on older, less efficient machine learning models that may not offer optimal performance for specific applications. Users experience difficulties with obtaining high-performance outputs and adapting models for specialized tasks.

Users manually compare data models with existing databases and generate SQL synchronization scripts, which is time-consuming and error-prone.

Designers, brands, and e-commerce businesses struggle with creating lifelike digital fashion models for showcasing clothing and designs.

Development teams struggle with manually managing and resolving software issues, leading to delays in diagnosing and fixing problems.

Lack of automated root cause analysis and issue triaging results in slower problem resolution.