PH Deck

Users need to test AI voice agents manually, which is time-consuming and inefficient

Users rely on manual or fragmented tools for tasks like writing, SEO, and data processing, leading to inefficient workflows and lack of unified automation.

Users are currently facing challenges in building efficient voice AI agents due to the need for complex coding skills. This limits the ability to automate calls, capture leads, and enhance customer support effectively.

Users manually calculate costs and latency for voice AI agents across multiple providers (STT, LLM, TTS), leading to time-consuming, error-prone comparisons and lack of integrated cost-latency optimization.

Users rely on traditional call centers or basic chatbots, which lack personalization, scalability, and 24/7 availability, leading to higher operational costs and inconsistent customer experiences.

Users face high operational costs and inefficiency in traditional contact centers using human agents. Drawbacks include high costs and inefficiency.

The current situation for users involves manual API testing, which is commonly time-consuming and prone to human error. Moreover, the traditional process of running API tests can be fragmented and lacks integration with modern tools. The drawbacks include the need to manually test APIs, leading to inefficiencies, and the inability to automate and schedule these tasks effectively.

Users rely on human agents for customer calls and inquiries, facing high labor costs and limited availability (e.g., only during business hours).

Users previously needed to develop custom real-time Voice AI solutions from scratch, facing high development costs, complex cross-platform integration, and limited scalability for voice agents and virtual avatars.

Users face challenges in testing complex AI agents that reason, use tools, and make decisions using traditional evals, which lack effectiveness for dynamic real-world interactions.