PH Deck

Users face challenges in building voice-to-voice AI agents with low latency

Drawbacks: Limited access to commercial and open models, lack of flexibility in incorporating tools like telephony and vision

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 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.

The current situation involves creating voice agents manually, which is time-consuming and requires expertise in voice technology. The drawbacks include high costs associated with development and deployment, lack of natural-sounding speech, and slow integration processes.

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.

Bank customer service traditionally relies on human agents (50–500 per call center), which incurs high labor costs, scalability limitations, and inconsistent service quality.

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 struggle to find suitable and authentic AI agents for their specific needs, leading to inefficiencies in operations, task automation, and delivery of complex solutions.

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

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