#voice bots

Contact centres across India handle calls in a dozen languages. Most AI voice bot platforms are still tuned for English and a handful of global languages, and that gap shows up fast. A bot transcribes Hindi spoken with a regional accent as gibberish. A lead qualification flow stalls the moment a caller switches from English to Tamil mid-sentence. It happens more often than most vendors admit.

Business leaders evaluating an AI voice bot for call centres are asking sharper questions these days. Not just "Does it work?" but "Does it work in the languages and accents their customers actually use?" That distinction changes everything about how a platform should be judged. This piece examines six voice automation companies in this space, highlighting their strengths and weaknesses.

Top 6 Multilingual Conversational AI Voice Bot Companies for Indian Contact Centers

The six platforms below take different approaches to voice automation. Some lean into Indian language depth. Others bet on global scale or developer flexibility. Each one fits a different priority, so the right pick depends less on which company is "best" and more on what a contact centre actually needs solved.

1. Devnagri AI

Devnagri operates as a language AI infrastructure layer built specifically for Indian regulated sectors, and its voice stack reflects that focus closely. The platform trains its language bots to handle regional accents in Hindi, Tamil, Telugu, Bengali, and other Indian languages. That matters because a voice bot trained on Indian accents behaves very differently from one adapted after the fact from a global model. Devnagri also connects to existing CRM and core banking systems for onboarding, collections, and grievance calls, with audit logs built in for compliance-heavy industries where that kind of traceability isn't optional.

2. Vapi

Vapi takes a different route entirely. It's a developer-first voice automation platform for teams that want to build custom voice agents through code, not a drag-and-drop console. Low-latency infrastructure is a strong point, and developers can plug in their own language models and speech engines as needed. The catch: Indian language accuracy depends heavily on which ASR and TTS providers a team chooses to wire in. Vapi gives you the scaffolding. You still have to build the language layer yourself.

3. Verloop

Verloop earned its reputation in conversational AI for e-commerce and BFSI support, and its voice offering carries that same customer service instinct forward. The platform runs automated voice-calling software for order updates, collections reminders, and query resolution, with integrations into common helpdesk and CRM tools already in place. Where Verloop shines is high-volume, repetitive queries. It's built for handling simple enquiries at scale rather than complex, multi-turn conversations, which makes it a good fit for support teams trying to deflect routine calls before they hit a human agent.

4. PolyAI

PolyAI is a global name, known for natural-sounding conversational agents deployed by large enterprises across banking, hospitality, and telecom. Its real strength is conversation design. It handles interruptions gracefully, closer to a real human call flow than a scripted IVR ever manages. Indian language support does exist here, but it isn't the platform's primary market. Businesses that care deeply about regional dialect accuracy would do well to test coverage thoroughly before committing to anything long term.

5. Replicant

Replicant positions itself as a platform for automating contact centres end to end, built to resolve entire calls autonomously rather than simply route them somewhere else. Its case studies are strong, particularly in collections and customer service for North American enterprises, with a real focus on measurable containment rates. For Indian businesses, though, capability isn't really the question. Language depth is. Replicant built its core strength around English-first deployments, and that history still shows.

6. Amazon Connect

Amazon Connect is the contact centre infrastructure arm of AWS, offering voice, chat, and workflow tools that scale comfortably to enterprise call volumes. Businesses already running on AWS tend to gravitate toward it, mostly for the ease of integration with existing cloud infrastructure and the pay-as-you-go pricing model. Indian language and accent support comes largely through Amazon's own Lex and Transcribe services. These cover the major Indian languages reasonably well, but they weren't purpose-built around regional accent variation the way dedicated Indian language platforms were from day one.

Key Takeaways

Voice automation stopped being a bet on whether a bot can hold a conversation a while ago. Now it's a bet on whether that conversation actually works in the language and accent a customer speaks in real life, not in a demo. Contact centre leaders evaluating any platform should ask for accent-specific accuracy numbers up front. Test against real call recordings, not polished demo scripts. Confirm exactly how the bot integrates with existing CRM or core systems before signing anything.

Voice is no longer a novelty feature. It's infrastructure. And in 2026, it’s not just about having a speech bot, it’s about having one that speaks your customer’s language, accent, and cultural rhythm and never misses a beat.

From “nice to have” to “mission-critical” have evolved multilingual speech bots, especially for companies operating in South Asia, Southeast Asia, Middle East, and Latin America. Here's what's actually shaping the space right now.

Why Multilingual Voice Bots Will Define Customer Experience in 2026

With companies entering new language markets, multilingual voice bots are moving from being a support tool to a critical customer engagement infrastructure. The new generation of voice AI developers builds it around the way humans naturally communicate, using regional accents, mixed languages, emotional cues, and cultural context. Organisations that develop these competencies are experiencing increased customer trust, higher resolution rates, and wider digital adoption among underrepresented populations. Success in 2026 won’t be evaluated by how many languages a bot speaks, but how well it can understand and respond to every consumer conversation, naturally and precisely.

1. Accent-Aware Models Are Finally Getting Real

For years, 'multilingual' meant 'multi-language', but that's a half-measure. A bot trained on textbook Spanish struggles with a Bogotá caller. When faced with Bhojpuri-inflected speech, a Hindi model built on formal Khari Boli stumbles.

Not universal models trying to cover everything, but localized acoustic profiles calibrated for regional dialects. The difference in first-call resolution rates is measurable and significant.

2. Code-Switching Is Now a Feature, Not a Bug

Real people don't speak one language per conversation. A Mumbai customer slides between Hindi and English in the same sentence. A Nairobi caller might mix Swahili, Sheng, and English mid-thought.

Multilingual voice bots in 2026 are being designed to detect and respond to mid-sentence language shifts without dropping the conversational thread. This task is technically difficult. Platforms that have solved it are quietly winning enterprise contracts in markets where code-mixing is the norm, not the exception.

3. Voice, Vernacular: Reaching the Next 500 Million

Literacy rates and smartphone penetration don't always travel together. Voice is often the most accessible interface for first-time digital users, and that's a massive market.

At this junction of sovereign language AI and regional script intelligence, companies like Devnagri have been attempting to make speech interactions really native and not merely translated. Once a vocal bot can handle Marathi as smoothly as it can English, the trust barrier for digital adoption changes dramatically.

4. Emotional Tone Detection Is Going Mainstream

A frustrated caller sounds different from a confused one. In 2026, multilingual voice bots aren't just parsing words; they're reading prosody, pace, and pitch to infer emotional state and adjust accordingly.

This isn't science fiction. Sentiment-aware routing has existed in contact centers for a few years. What's new is that it's becoming standard in multilingual contexts, which is harder, because emotional expression itself is culturally variable. A raised voice means anger in one context and enthusiasm in another.

The bots getting this right are reducing escalations and improving CSAT without adding human agents.

5. Real-Time Neural Translation Is Replacing Rule-Based Systems

Old-generation multilingual bots ran on translation layers that were, frankly, brittle. Idioms broke them. Slang confused them. Formal scripts misfired in casual conversations.

Neural machine translation now powers the best multilingual voice systems, as it is trained on real conversational data rather than formal text corpora. The result is more natural responses, better context retention across turns, and fewer awkward silences while the system figures out what was just said.

For businesses running high-volume voice operations, telecom, banking, and e-commerce, this shift materially changes the customer experience.

6. Voice Bots Are Being Audited for Language Bias

This is the trend no one talks about enough. A voice bot that performs well in English but poorly in Telugu isn't just a UX problem; it's an equity problem. Regulators in certain markets are beginning to pay attention.

Forward-looking enterprises are now including language performance parity in their AI governance frameworks. Building multilingual voice bots isn't just a growth play anymore. In some sectors, it's becoming a compliance consideration.

The Takeaway

The global voice AI market is projected to cross $50 billion by 2029, and a significant chunk of that growth lives in multilingual, multiregional deployments. But the real winners won't be the ones with the most languages on a feature sheet; they'll be the ones who built for how people actually speak.

For years, businesses treated voice bots like automated receptionists. Press 1 for billing. Say “yes” to continue. Repeat your account number because the system didn’t catch it the first time.

Most people tolerated these systems rather than liked them.

That’s changing quickly. The rise of conversational AI voice bots is reshaping how companies handle customer communication, especially in industries where speed, personalization, and multilingual support matter. What used to feel robotic now sounds surprisingly natural. In many cases, customers no longer realize they are speaking with software until the conversation ends.

But despite the hype, traditional voice bots haven’t disappeared. Many firms still use them because they are predictable, inexpensive and easy to use for recurring tasks.

So what is it actually that divides a standard voice bot from a conversational AI speech bot? But more importantly, which one makes sense for today’s businesses?

The Old Model: Scripted and Rule-Based

Traditional voice bots operate on predefined flows. They follow scripts built around fixed commands and limited responses.

If a customer says something outside the expected path, the system usually breaks.

We have all experienced it:

“Sorry, I didn’t understand that.”

The issue is not that these bots are unintelligent. They were never designed to understand human conversation in the first place. Their job was efficiency, not engagement.

The model did reasonably well on structured tasks such as:

Checking your account balances.

Delivery status confirmation

Basic appointment scheduling

Directing calls to departments

For organisations with large call volumes these bots eased the load on support workers and decreased operational costs.

According to Deloitte Insights, automation in customer operations has historically focused on standardization and cost reduction rather than conversational quality.

And honestly, that made sense for the time.

Conversational AI Changes the Experience

Conversational AI voice bots take a completely different approach.

They use natural language processing, machine learning and contextual awareness to interpret intent, not just to run scripts. That is, they listen in a more human-like way.

If you want to break in, re-word a statement, change tracks in the middle or just talk normally the system can follow.

That changes the entire interaction.

Rather than forcing people into menus, conversational AI systems guide conversations dynamically. The experience feels less like operating a machine and more like talking to an informed assistant.

A healthcare patient might say:

“I need to reschedule my appointment because my train got delayed.”

A traditional bot might fail because the exact keyword “reschedule appointment” wasn’t detected in sequence.

A conversational AI voice bot understands the intent instantly.

That difference sounds small on paper. In customer experience, it’s enormous.

The Biggest Shift Is Context

The real breakthrough is not speech recognition. It is memory and context.

Traditional bots treat every response as isolated. Conversational AI systems can carry context across an interaction.

If a customer says:

“I’m calling about the order I placed yesterday.”

and later adds:

“Can you change the delivery address too?”

The AI remembers what “the order” refers to.

This reduces repetition, shortens call duration, and creates smoother interactions. It also lowers customer frustration, which matters more than most companies realize.

Research from Harvard Business Review has repeatedly shown that reducing customer effort often improves satisfaction more effectively than adding flashy features.

People simply want conversations that flow naturally.

Multilingual Support Is Becoming a Major Advantage

One area where conversational AI voice bots are pulling far ahead is multilingual communication.

Traditional systems often require separate workflows for every language. That becomes expensive and difficult to maintain.

Modern conversational AI platforms can switch languages on the fly, and understand regional accents with far more precision. For global businesses, especially in areas like India, Southeast Asia and the Middle East, this is becoming a practical necessity rather than a premium feature.

This is where the focus is coming to businesses working on language AI infrastructure. Businesses want voice systems that can naturally converse in several languages, but not at the expense of a fragmented support experience.

And the demand is there.

The World Economic Forum has noted that AI-enabled communication tools are becoming necessary for digital accessibility and broader customer inclusion.

Traditional Bots Still Used

While conversational AI has taken off, old-school voice bots aren't extinct.

In fact, they can still be the smarter option for jobs that are highly repetitious and compliance-heavy.

Why not?

Because they are stable.

A scripted bot just does the same thing over and over. But that sort of regularity is very useful in regulated businesses, like insurance verification or controlled financial reporting.

Conversational AI is more flexible, but it requires more supervision, better training data and continuous optimisation.

Not every corporation requires a sophisticated AI conversation engine only to validate office times.

The smarter decision is often a question of complexity.

Traditional bots can still provide a significant ROI if interactions are short, repetitive and transactional.

If conversations require nuance, personalization, multilingual support, or emotional intelligence, conversational AI is becoming difficult to ignore.

What Businesses Should Actually Focus On

Many companies approach voice AI with the wrong question.

They ask:

“Which technology is better?”

The better question is:

“What type of customer experience are we trying to create?”

Because customers do not care whether a system uses NLP models or decision trees. They care whether the interaction feels fast, clear, and painless.

That’s the real benchmark.

A poorly designed conversational AI bot can still frustrate people. A well-designed traditional voice bot can still be useful.

Technology alone is not the differentiator anymore. Conversation quality is.

Final Thoughts

Voice automation is no longer just about reducing call center workload. It is becoming part of how brands communicate, support, and build trust at scale.

Traditional voice bots helped businesses automate tasks. Conversational AI voice bots are helping businesses improve conversations.

That distinction matters.

Many teams track chatbot activity but miss impact. Message volume alone says nothing about value. ROI begins with understanding what the chatbot actually resolves or influences. Resolution rate is the foundation. It measures how many conversations end without human involvement. High resolution proves that smart AI chatbots are reducing workload and delivering real support value.