AI Voice Bot Software for Enterprise Contact Centres

A contact centre that receives lakhs of calls each month has a capacity problem that hiring cannot solve. Call demand arrives in sudden bursts, customers switch from Hindi to English within a sentence, and the system receives compressed 8 kHz telephony audio, not studio-quality sound. Pilgrim, a D2C beauty brand, saw support requests rise five to seven times during sales periods. A fixed team cannot absorb that without overstaffing for the other eleven months or letting resolution times grow.

Gartner predicts that by 2029 agentic AI will autonomously resolve 80% of common customer service issues without human intervention. Whether that holds depends on how voice AI performs under real call conditions: recognising accented and noisy speech, responding fast enough to feel like a normal conversation, and handing over smoothly when a human is needed. That is the job of a voice bot for call centre operations.

This page explains how AI voice bot software works, the types of voice agents available, where each fits across support, sales and e-commerce, and how Convozen deploys AI-powered voice agents for enterprise teams.

How AI Voice Agents Work: Convozen’s Voice Agent Architecture

A voice interaction passes through speech-to-text (STT), an orchestration layer, large language model (LLM) inference and text-to-speech (TTS). The first, second and fourth stages are almost fixed. The LLM stage is the one that varies. In any voice-to-text AI chatbot for a call centre, accuracy at the first stage decides everything downstream.

The quickest setup, a Light model with light context, has an end-to-end latency of 850 ms. Where the model or context is heavier, Convozen can play natural filler remarks during processing, which keeps perceived latency at about 800 ms.

Two proprietary models sit underneath. Akshara is the voice agent speech-to-text engine, designed for telephonic audio. Ragini is the text-to-speech model, trained on 800+ hours of proprietary Indian voice acting, with audio generation in under 200 ms. Calls reach the agent through a media gateway over SIP trunks, and the conversation runs over WebSocket, so the agent can listen while speaking and handle barge-in.

Types of Voice Agents and Where Convozen Fits

Convozen sits in the third category. Its agents serve customers over voice, WhatsApp and email, and its Multi-Session Omni-Channel (MSOC) architecture maintains a persistent user state, so a customer who calls today and messages next week does not start again.

Key Features and Benefits of AI Voice Bots for Customer Support

AI voice bots for customer service succeed or fail on a handful of capabilities.

• Telephony-grade recognition. In February 2026, Akshara was tested across nine Indian languages on 16 hours of audio. Its overall word error rate was 16.8%, against 24.6% and 37.2% for two other leading Indic speech models, on identical audio segments.
• Multilingual voicebot coverage. Convozen’s June 2025 benchmark recorded a WER of 0.05 in English and 0.07 in Hindi.
• Natural turn-taking. Interruption handling, active listening sounds and tonal matching keep calls from feeling scripted.
• Action, not just answers. Through APIs, agents connect to a knowledge base and business tools and complete tasks inside existing systems.
• Human escalation. Calls transfer to a human agent with context retained.
• Full visibility. Dashboards and transcripts show how each interaction progressed, which feeds continuous improvement.

Giridhar Amerlai, Head of AI & Innovation at Jana Bank, described the problem Convozen solved: “We couldn’t get the latency and orchestration right in-house… with Convozen, it became seamless to test and run multiple use cases with a much more human-like experience.”

Convozen AI Voice Agents Across Industries

• Customer support (BFSI). Jana Small Finance Bank used multilingual voice agents for pre-approved loan and collections journeys in 9+ Indian languages, achieving a 10% boost in resolution rate across use cases.
• Sales. At NoBroker Builders, which handles about one million calls a month, voice agents extended outreach across the long tail of buyers. They now handle 6% of overall business, total property visits rose 8%, and 12.5% of rejected customers were revived.
• E-commerce. Pilgrim deployed a WhatsApp AI agent for high-volume D2C queries. Agent transfer rate fell 34%, bot resolution rate rose 73%, and CSAT reached 4.25. Nilesh Kambli, Sr. VP, Customer Experience at Pilgrim, said: “We’ve seen agent transfers drop drastically.”

The same architecture runs as an AI voice bot for customer support, an AI voice bot for sales or an AI voice bot for ecommerce. Convozen’s AI voice agents for customer support, AI voice agents for sales and AI voice agents for e-commerce differ only in knowledge base, tools and conversation design.

AI Voice Bots vs. AI Chatbots: Which Does Your Business Need

Most businesses need both. Convozen agents manage voice calls, WhatsApp voice notes (received, transcribed, understood and answered), text, images and documents on one platform, so the decision becomes one of routing, not of choosing between platforms.

How to Deploy a Voice Bot with Convozen

1. Set up the agent. Define the use case, language and tone on the Convozen platform.
2. Configure knowledge and tools. Attach the knowledge base and the actions the agent can take in your systems. For voice, keep context under 4,096 tokens where possible. The Latency Reference Guide recommends splitting the agent into specialised sub-agents beyond about 16,000 tokens.
3. Connect telephony and systems. Use APIs to link your SIP trunk, CRM and contact centre software.
4. Select the latency profile. Choose a model tier and decide whether to enable filler masking.
5. Start a campaign. Run inbound or outbound calling through the campaign manager.
6. Monitor and improve. Use dashboards and transcripts to adjust flows.

Why Enterprises Choose Convozen’s AI Voice Bot Platform

• Scale. 40M+ voice AI calls and 50M+ conversations audited every month.
• Built for telephony. Akshara and Ragini are proprietary models developed for noisy call centre audio and Indian voices.
• One platform. Conversational agents, copilots for live calls and supervisor agents that review every interaction run on one stack.
• Data control. Every customer gets a dedicated stack of AI models, with VAPT, ISO, GDPR and HIPAA coverage and SOC 2 in progress.

Want to see it on your call flows? Book a demo.