Using GPT‑5.6, Ringg powers multilingual agents across voice, chat, WhatsApp, and web for 90% less cost vs. GPT‑4.1.

When call volume rises, customer service operations typically scale by adding people, but this increases the cost and complexity of every interaction. Ringg⁠(opens in a new window), a voice and chat agent platform, saw this problem firsthand while working with large consumer businesses in India.

These companies were not only struggling to answer more calls, their customer service agents were also battling fragmented, manual systems to help customers complete tasks, from buying insurance to booking appointments.

That experience led Ringg to build an enterprise agent platform with high-efficiency models like GPT‑5.6 at its core, spanning voice, chat, WhatsApp, and the web. Migrating suitable real-time workloads from GPT‑4.1 to GPT‑5.6 reduced model costs by approximately 90% while delivering the required quality and latency.

Ringg’s agents now handle more than 7 million connected calls each month, and its customers have an average customer satisfaction (CSAT) score of 4.8.

For agents to complete a customer request, it may require checking a policy, retrieving an account record, scheduling an appointment, updating a CRM, or transferring the conversation to a specialist with the relevant context intact.

6 Luna and other models to interpret customer requests, select tools, and guide customers through multi-step workflows. Ringg’s orchestration layer executes actions across CRMs, ticketing platforms, payment systems, scheduling tools, and internal APIs, escalating cases to a human with a conversation summary when needed.

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