When call volume rises, customer service operations typically scale by adding people, but this increases cost and complexity. Ringg, a voice and chat agent platform, saw this firsthand with large consumer businesses in India. These companies struggled with fragmented manual systems, leading Ringg to build an enterprise agent platform with high-efficiency models like GPT‑5.6 at its core. Migrating from GPT‑4.1 to GPT‑5.6 reduced model costs by approximately 90% while delivering required quality and latency.

Ringg’s agents now handle more than 7 million connected calls each month, with an average customer satisfaction (CSAT) score of 4.8. The platform uses GPT‑5.6 Luna and other models to interpret customer requests, select tools, and guide multi-step workflows. Ringg’s orchestration layer executes actions across CRMs, ticketing platforms, payment systems, scheduling tools, and internal APIs, escalating to humans with conversation summaries when needed.

Ringg routes work to specific OpenAI models: GPT‑4.1 handles most real-time voice and chat traffic; GPT‑5.6 Luna is used when its performance or price-profile suits; GPT‑5.6 Terra handles post-call analysis; GPT‑5.6 Sol supports evaluation and prompt improvement. For longer interactions, the system creates structured summaries near 80,000 tokens to preserve context without resending history.