PH Deck

Developers and AI engineers spend $500/month on cloud services for AI experiments, facing high costs and dependency on external infrastructure for prototypes, demos, and interviews.

Users facing challenges in experimenting with AI models due to the necessity of setting up a complex machine learning (ML) stack, and the high costs associated with GPU requirements. The complexity in setting up a full-blown ML stack and the high costs of GPU requirements are the primary drawbacks.

Users previously managed local AI models through platforms requiring complex server setups and installations, leading to time-consuming deployments and limited accessibility.

Users rely on human teams for product development cycles, leading to slow and inefficient discovery-to-launch processes due to manual coordination and resource constraints.

Users manually design and deploy AI agents without a structured framework, leading to inefficient development, lack of rigorous testing, and suboptimal agent performance

In the current situation, users struggle with customer interactions that are impersonal and slow. This affects customer experience and relationship building with clients. The primary drawbacks are the inability to quickly engage with customers leaving them dissatisfied, leading to potential loss of conversions.

Developers face challenges in building full-stack applications quickly and efficiently due to the complexity of modern web development stacks and the time required for coding. This leads to slow development cycles and increased project timelines. The drawbacks include complexity of modern web development stacks and increased project timelines.

Users struggle to create personalized running training plans based on their workout data.

Drawbacks of the old situation: Lack of personalized feedback and training plans, manual generation of interval workouts, inefficient technique improvement, and difficulty in tracking progress.

The current situation involves developers and project managers trying to code and manage projects while on-the-go. This often requires them to have access to a full-fledged desktop or laptop setup to run integrated development environments (IDEs), manage code via version control systems like Git, handle dependencies with tools such as npm, and test their code execution. The drawbacks of this old situation include the inability to efficiently work from a mobile device, which limits productivity and flexibility.

Users rely on non-optimized AI platforms that lack environmental focus and transparency, facing high computational costs, limited transparency, and non-eco-friendly infrastructure.