The conversation around voice AI in India has long been dominated by a simple assumption: build it in Hindi, and the market will follow. Our data tells a very different story. After analyzing language configurations across 3,366 active AI voice agents deployed on the Edesy platform, we found that businesses are not choosing single languages at all. They are choosing code-switching pairs, regional languages, and increasingly, multi-language agents that can handle four or more languages in a single conversation.
This post presents the raw data, the patterns we see emerging, and what it means for businesses planning their voice AI strategy in 2026 and beyond.
The Data Set
Our analysis covers 3,366 AI voice agents actively deployed between January and March 2026. These agents span industries including e-commerce, financial services, healthcare, logistics, education, and customer support. Each agent has a primary language configuration chosen by the business deploying it. Some agents support multiple languages and can switch between them mid-conversation.
We tracked which language configurations businesses selected, how often each was used, and how conversation volumes distributed across languages.
The Language Distribution Table
| Language Configuration | Share of Agents | Notes |
|---|---|---|
| Hindi-English (code-switching) | 12.0% | Largest single category |
| Telugu | 10.8% | Strong standalone adoption |
| Assamese | 10.6% | Surprising growth leader |
| Bengali | 10.5% | Consistent demand across sectors |
| Tamil-English (code-switching) | 10.3% | Code-switching preferred over pure Tamil |
| Odia | 10.2% | Emerging market for voice AI |
| Kannada | 10.1% | Driven by Bengaluru-area businesses |
| Gujarati-English (code-switching) | 10.0% | Commerce and logistics heavy |
| Pure Hindi | 1.2% | Strikingly low standalone adoption |
| Multi-language (4+ languages) | 8.1% | Fastest growing segment |
| International (Hebrew, Sinhala, Albanian, Lao, others) | 6.2% | Cross-border use cases |
The numbers challenge several assumptions that have shaped voice AI product development in India for years.
Hindi-English Code-Switching Dominates, Pure Hindi Does Not
The single most important finding is the gap between Hindi-English code-switching at 12% and pure Hindi at just 1.2%. That is a 10x difference. Businesses deploying voice AI agents in Hindi almost universally want their agents to handle the natural mixing of Hindi and English that defines how most urban and semi-urban Indians actually speak.
This is not a niche preference. It is the default expectation. When a customer in Delhi calls about a loan application, they say things like "mera application ka status kya hai, jo maine last Tuesday submit kiya tha." That sentence is neither Hindi nor English. It is Hinglish, and it is the real language of Indian commerce.
For businesses evaluating voice AI platforms, the question is not "do you support Hindi?" but rather "do you support Hindi-English code-switching without degraded accuracy?" The difference matters enormously. A system trained only on pure Hindi will stumble on the English fragments that appear in nearly every real conversation. A system that genuinely handles code-switching will understand both the Hindi syntax and the English vocabulary embedded within it.
We have documented the performance characteristics of our Hindi-English voice agents in detail. For more on how code-switching works in practice, see our case study on Hindi-English code-switching across 400 agents.
Regional Languages Are Not Secondary Markets
Perhaps the most striking pattern in the data is how evenly distributed adoption is across regional languages. Telugu, Assamese, Bengali, Tamil-English, Odia, Kannada, and Gujarati-English each command between 10% and 11% of total agent deployments. There is no dramatic drop-off from Hindi to regional languages. The market is genuinely distributed.
This has major implications for product strategy. Businesses that treat regional languages as "phase two" are leaving a substantial market unaddressed. In many sectors, the regional language deployment is the primary deployment, not a localization afterthought.
Assamese at 10.6% is a particularly notable data point. It is not typically listed among the top commercial languages for AI products, yet businesses deploying voice agents in Assamese are finding strong engagement rates. The explanation is straightforward: when you offer customers the option to interact in their native language, they engage more deeply, complete more transactions, and report higher satisfaction.
For businesses interested in deploying voice agents in specific Indian languages, our language-specific pages provide detailed information on capabilities, accent support, and performance benchmarks for each supported language.
The Code-Switching Pattern Extends Beyond Hindi
Hindi-English is not the only code-switching pair in demand. Tamil-English at 10.3% and Gujarati-English at 10.0% show that the code-switching pattern is consistent across language families. Businesses in Tamil Nadu want their voice agents to handle the natural mixing of Tamil and English. Businesses in Gujarat want the same for Gujarati and English.
This pattern suggests that as voice AI adoption grows in other Indian languages, we should expect code-switching variants to emerge for Kannada-English, Telugu-English, Bengali-English, and others. The underlying driver is the same across all these markets: Indian professionals and consumers naturally mix their regional language with English, and they expect AI systems to understand them when they do.
Building reliable code-switching support is technically challenging. It requires training data that reflects real conversational patterns, not the artificial separation of languages that most academic datasets impose. It requires speech recognition models that can detect language boundaries mid-sentence. And it requires natural language understanding systems that can parse meaning across those boundaries. The investment is significant, but the data makes clear that it is not optional for any platform serious about the Indian market.
Multi-Language Agents Are the Fastest Growing Segment
Agents configured to support four or more languages now account for 8.1% of deployments, and this segment is growing faster than any single-language category. These are agents deployed by businesses with pan-India or international operations that need a single agent to handle conversations in Hindi-English, Tamil, Telugu, Bengali, and sometimes additional languages.
The operational advantage is clear: instead of maintaining separate voice agents for each language, a business can deploy one agent that detects the caller's language and responds accordingly. This reduces maintenance overhead, ensures consistent business logic across languages, and simplifies reporting.
The technical challenge is equally clear. A multi-language agent must not only support each language individually but must also handle the transitions between languages gracefully. A caller might start in Hindi-English, switch to pure Hindi when discussing a sensitive topic, and then revert to English for technical terminology. The agent must follow these transitions without losing context.
International Adoption Signals
The presence of Hebrew, Sinhala, Albanian, and Lao in our deployment data at a combined 6.2% reflects a growing pattern of international businesses using Indian voice AI platforms for their own markets. The cost advantages of Indian AI infrastructure, combined with the multilingual capabilities developed for the Indian market, make platforms like Edesy attractive for businesses in smaller language markets that the major global AI providers have not prioritized.
Hebrew deployments are concentrated in customer service automation. Sinhala deployments serve the Sri Lankan market from Indian operations centers. Albanian and Lao represent early-stage deployments that we expect to grow as these markets develop their voice AI strategies.
What This Means for Businesses
The data points to several actionable conclusions for businesses planning voice AI deployments in India.
First, default to code-switching configurations rather than pure single-language setups. The data overwhelmingly shows that businesses get better results when their agents can handle the natural language mixing that real customers use. If you are deploying a Hindi voice agent, configure it for Hindi-English code-switching unless you have a specific reason not to.
Second, do not treat regional languages as secondary. The adoption data shows that Telugu, Bengali, Tamil, and other regional languages have deployment volumes comparable to Hindi-English. If your customer base spans multiple Indian states, a multi-language deployment should be your starting point, not a future roadmap item.
Third, evaluate voice AI platforms on their code-switching performance, not just their language list. Many platforms claim to support Hindi, but their actual performance on Hindi-English code-switching varies dramatically. Ask for demos with real code-switched conversations, not scripted single-language samples.
Fourth, consider multi-language agents if you operate across three or more language markets. The operational simplification of a single agent that handles multiple languages is significant, and the technology has matured to the point where quality does not suffer.
Looking Ahead
We expect several trends to accelerate through the remainder of 2026. Code-switching support will become a baseline expectation rather than a differentiator. Multi-language agents will grow from 8% to potentially 15-20% of deployments as businesses consolidate their voice AI infrastructure. And new code-switching pairs including Kannada-English, Telugu-English, and Bengali-English will emerge as distinct deployment categories.
The Indian voice AI market is not converging on a single language. It is expanding to reflect the genuine linguistic diversity of Indian commerce. Businesses and platforms that embrace this complexity will outperform those still building for a simplified, single-language model of the market.
For a deeper look at how voice AI performs across Indian languages, explore our AI voice agent platform or review our Hindi language capabilities as a starting point.