PH Deck

Users previously had to manually integrate and manage LLM, storage, and orchestration components when building AI agents, leading to time-consuming setup, fragmented infrastructure, and limited scalability.

Users face time-consuming and inflexible development processes when creating AI agents, struggling with challenges in integrating various LLMs and workflows using traditional coding methods.

Developers building AI agents face challenges managing state and scaling due to stateless architectures and limited real-time communication capabilities in traditional serverless solutions.

Developers and engineers building multi-agent systems face challenges with time-consuming setup, lack of integrated tooling, and difficulty in evaluating system performance using fragmented or custom-built frameworks.

Users rely on basic chatbots that only handle conversations without driving conversions. Basic chatbots that only handle conversations lead to missed opportunities for sales and customer engagement.

Users struggle with building safe and trusted Agentic RAG workflows manually

Users need to build intelligent agents but face challenges with existing tools that are not optimized for Go, leading to complex setup, limited scalability, and lack of native Go integration.

Current situation: Enterprises face challenges in building AI agents due to complexity and resource requirements.

Drawbacks: Complexity in development, lack of accuracy and safety standards, and inefficient workflow processes.

Users need coding expertise to build AI agents and workflows, which requires programming skills and is limited to those without coding expertise

Developers and businesses face challenges in creating use-case specific agents that can robustly perform tasks due to the complexity and limitations of existing Large Language Model (LLM) frameworks, leading to inefficiencies and a lack of customization capabilities.