PH Deck

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

Businesses attempting to integrate AI and automation technologies often struggle with the complexity and rigidity of traditional RPA tools, which rely heavily on brittle code selectors or computer vision.

Users struggle with developing AI systems independently because the traditional method requires a vast amount of coding expertise and significant time investment. The drawbacks of this old situation include significant time investment and a vast amount of coding expertise.

Enterprises struggle to deploy AI agents that securely handle multi-step workflows across tools and data while respecting permissions. Handling multi-step workflows across tools and data and ensuring data permissions are key drawbacks.

Users manually navigate to Google Meet's website, create a meeting, and copy-paste the link every time they need to start or share a video call, leading to time-consuming and repetitive steps.

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.

Users manage browser operations and system integrations through manual, time-consuming processes that require constant oversight and lack automation.

Users previously relied on manual automation or less efficient frameworks for task automation, facing time-consuming processes, limited collaboration capabilities, and insufficient toolkits.

Design teams often struggle with the routine creation, construction, and maintenance of the Design system, leading to inconsistencies and inefficiencies in the design process.