Customer Service Automation
Ringg needed to help enterprises automate large volumes of customer calls and chats with reliable tool use, low latency, multilingual quality, and unit economics that improved rather than worsened at scale.
Reported impact
Ringg helped customers automate routine support at scale, cut model cost for selected workloads, maintain strong satisfaction, and prove that completed outcomes rather than call volume can define next-generation customer operations.
Ringg built an enterprise agent platform around multiple OpenAI models so customer-service workflows could be routed to the best model for each step. GPT-4.1 handles much of the real-time voice and chat traffic, GPT-5.6 Luna is used when its latency or price-performance profile is better for a request, GPT-5.6 Terra powers post-call summaries and sentiment analysis, and GPT-5.6 Sol supports evaluation and prompt improvement. Around those models, Ringg runs an orchestration layer that connects to CRMs, ticketing platforms, payment systems, scheduling tools, internal APIs, and a semantic retrieval stack spanning PDFs, CSVs, and business documents. Specialized subagents manage qualification, verification, scheduling, support, and escalation, while summaries preserve context near token limits. Offline and staged production evals then guide migrations and router decisions, allowing the company to lower costs while maintaining customer outcomes across voice, chat, WhatsApp, and web channels.
Voice or chat input is combined with instructions, customer data, conversation history, knowledge-base context, and available tools inside Ringg's orchestration layer. The router selects the right OpenAI model, executes any CRM or payment actions, and returns a response or escalates with a structured summary when human intervention is needed.
"Migrating certain real-time workloads from GPT‑4.1 to GPT‑5.6 Luna reduced model costs by approximately 90%."
"Ringg’s agents now handle more than 7 million connected calls each month"
"That experience led Ringg to build an enterprise agent platform with high-efficiency models like GPT‑5.6 at its core"
"Up to 65% request resolution via agents"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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