Ringg’s AI agents resolve up to 65% of customer calls with OpenAI

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.

One evaluation involved Ringg’s post-call analysis workflow. The company tested GPT‑5.6 Terra against alternatives like Gemini 2.5 Flash, and Terra came out on top. Ringg moved summaries and sentiment classification to GPT‑5.6 Terra. Terra maintained high accuracy for summaries and sentiment analysis while materially improving unit economics.

At Practo, a global healthcare platform, Ringg’s agents help customers book healthcare appointments and complete service requests. Following deployment, Practo achieved an 85% first-call resolution rate and response times below three seconds. Operating costs declined by 70% compared with its previous human-led workflow, while Ringg now completes more than 1,000 appointment bookings each day.

Ringg can also divide work among specialized subagents for qualification, support, verification, scheduling, and escalation. The platform coordinates those steps while maintaining a consistent conversation with the customer across voice, chat, WhatsApp, and the web.

Using OpenAI’s computer-use capabilities, Ringg is developing browser agents for platform onboarding, Know Your Customer (KYC) processes, IT troubleshooting, on-call incident support, and claims processing.

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.

For real-time interactions, Ringg uses GPT‑5.6 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.