AI Voice Chatbot for Enterprises

Customers rarely stay on one channel. They ask on WhatsApp, follow up by phone and write an email when neither resolves the issue. When each channel runs on a separate tool, every handoff loses context, and the customer repeats the story.

An AI voice chatbot closes that gap. It speaks and listens on calls, reads and writes on chat, and works from one shared record of the customer. ConvoZen builds this as a conversational AI voice bot service on a single agent stack, with the same agent handling voice, WhatsApp and email. The platform runs more than 40M voice AI calls a month.

One Agent for Calls, WhatsApp and Email

ConvoZen’s agents run on MSOC, a Multi-Session Omni-Channel architecture that holds a persistent state for each customer. The same agent identity works on every channel, so context carries across them.

In voice, “true duplex” means the agent listens while it speaks. Customers can interrupt it, and it adjusts its tone to match the conversation. An AI calling bot that talks over the customer, or goes silent when interrupted, loses the call. That is why turn-taking sits at the centre of the design.

Understands Hindi, English and Mixed Speech on Calls

Telephone audio is noisy and compressed. ConvoZen’s speech-to-text model, Akshara, is built for 8kHz telephony rather than clean studio recordings. It was pre-trained on more than 50,000 hours of audio and fine-tuned on more than 4,000 hours of hand-annotated data.

In ConvoZen’s speech-to-text benchmark across 9 Indian languages, the word error rate is 0.05 in English and 0.07 in Hindi. The model handles short utterances, regional accents and code-switching.

The reply is spoken by Ragini, ConvoZen’s text-to-speech model. It was trained on more than 800 hours of proprietary Indian voice acting and generates audio in under 200ms. It converts Hindi-English code-switched text into natural speech and reads UPI IDs, Aadhaar numbers and addresses correctly.

Speed matters as much as accuracy. End-to-end latency starts at 850ms for a light model with light context, and filler masking caps perceived latency at 800ms.

Remembers Each Customer Across Calls and Chats

A customer sends a photo of a damaged product on WhatsApp, then calls to discuss it. Because MSOC keeps one state per customer, the agent sees both. Customers can switch modality mid-conversation without starting over.

Customer 360 and Agent Memory hold the history of the relationship. An agent that knows what happened last week can open the next call at the right point, not at the greeting.

More Than Chat: What the Agent Does on WhatsApp

A chatbot that only trades text on WhatsApp uses a fraction of the channel. ConvoZen’s agent works in all of its formats: it transcribes and answers voice notes, reads images and customer uploads, and sends walkthrough videos, invoices and policies.

Pilgrim, a D2C beauty brand, deployed the WhatsApp AI Agent against support demand that jumped 5 to 7 times during sales. Its results:

• 34% decrease in agent transfer rate
• 73% increase in bot resolution rate
• CSAT of 4.25

Nilesh Kambli, Sr. VP, Customer Experience at Pilgrim, said: “We’ve seen agent transfers drop drastically.”

Build, Test and Launch Your Agent

A voice agent in production needs more than a model. ConvoZen provides the surrounding workflow:

• AI Agent Studio to configure the agent, its knowledge base and its tools
• Actions Framework to connect the agent to your systems through APIs
• Agent Simulator and A/B Framework to test conversations before and after launch
• Campaigns to run outbound calls and chats at scale
• Dashboards and transcripts to track results

Giridhar Amerlai, Head of AI & Innovation at Jana Bank, said: “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.”

Hands Over to Your Team, With Full Context

Some conversations belong with a person. When the agent transfers a call through SIP agent transfer, the human agent receives the conversation context. ConvoZen’s Copilot then supports that agent in real time with next best actions, context and compliance guardrails. Interactions sync with your CRM and contact centre software, so the record stays in one place.

Every Call and Chat Reviewed for Quality

Manual quality assurance covers a small share of conversations. Before ConvoZen, Cars24 audited about 4% of its calls. NoBroker Builders reviewed barely 3 to 4%.

ConvoZen’s Supervisor AI Agent reviews every interaction for sentiment, compliance risk and resolution gaps. Cars24 reached 100% automated quality audits and 2X higher violation detection and tracking. Across the platform, ConvoZen audits more than 50M conversations a month.

Used Across Industries

Results From ConvoZen Customers

Kumar Abhishek, Associate VP, Strategy at NoBroker Builders, described the change in oversight: “Earlier, we could review barely ~3-4% of conversations, and feedback was often subjective. With ConvoZen, auditing became systematic and scalable, and our RMs started getting tailored coaching that improved performance across the board.”

For enterprises evaluating an AI voice agent, the useful questions are measurable ones. How accurate is recognition of your callers’ speech? How fast does the reply start? Does context survive a change of channel? ConvoZen publishes its benchmarks for each.

To see an agent running on your own calls and chats, book a demo.