PH Deck

Users need to set up and manage their own infrastructure for deploying time-series foundation models, which requires significant technical expertise and time investment, slowing adoption in industry.

Users rely on expensive AI inference APIs like DeepSeek, facing high costs and limited operational flexibility for scaling AI applications.

Developers previously relied on cloud-based AI models which require sending data externally, leading to higher costs, latency issues, and privacy risks.

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 struggle to calculate the time between two dates accurately and quickly

Users (companies) need to handle structured data (tabular, time-series, recommender systems) with AI models but face inefficient inference, fine-tuning, and scaling due to fragmented tools or manual processes.

Users managing separate pipelines for text, image, video, and audio face slower inference speeds and complex scaling challenges due to fragmented workflows.

Users manually calculate time intervals or use basic tools like spreadsheets/standard calculators for time-related tasks, leading to human error and inefficiency in tracking flight hours, work shifts, or date differences

Designing visually consistent graphics demands significant time and expertise, with challenges in maintaining style consistency across various images and creating complex scenes.

Users currently deploy AI models using traditional cloud services requiring manual infrastructure management, leading to high operational costs and inefficient scaling challenges.