AI agents in Indian languages: what actually works

Why English-first models struggle with Indian languages

Most large language models are trained on datasets that are overwhelmingly English, with other languages represented in much smaller proportions. Hindi, Tamil, Telugu, Bengali, and India's other major languages are underrepresented in that training mix relative to how many people actually speak them, which shows up as weaker grammar, weaker idiom handling, and a tendency to fall back on stiff, translated-sounding phrasing rather than natural, native sentence construction.

The problem compounds with code-mixed speech, meaning sentences that blend two languages in a single utterance, most commonly Hindi and English as Hinglish. This is not an edge case in Indian conversation, it is close to the default register in many customer service and sales contexts. A model that handles clean, single-language Hindi reasonably well can still fail badly on a sentence that switches between Hindi and English mid-thought, because that pattern is rarer in training data than either language on its own.

Translating is not the same as answering natively

A common shortcut is to translate the user's message to English, run it through an English-optimized model, and translate the response back. This can work adequately for simple factual questions, but it loses something on anything with nuance: tone, politeness register, regional phrasing, and idiom rarely survive a round trip through translation intact.

A model that answers natively in the target language, without a translation step, tends to produce responses that read as though a fluent speaker wrote them, because it is reasoning and generating in that language directly rather than reasoning in English and converting the output afterward. When evaluating a vendor, ask directly whether their system translates under the hood or genuinely operates in the target language, and ask for a live example in a language you or a colleague can actually judge.

Script handling and the romanization trap

A frequent and easy-to-spot failure mode is a model that understands a query written in Devanagari or Tamil script but responds in romanized text instead, meaning the target language spelled out using the Latin alphabet. This happens because romanized text is more common in casual training data such as social media and chat logs than properly scripted text, so a model can be fluent in romanized Hindi while being noticeably weaker at generating clean Devanagari.

For a business deploying a customer-facing agent, this matters because a response in the wrong script reads as broken or foreign to a native speaker, even if the underlying words are correct. Test any vendor's system specifically for script fidelity, not just comprehension, by sending it a scripted-language query and checking whether the reply comes back in the same script rather than a romanized approximation.

Speech is harder than text

Everything that makes text generation in Indian languages hard applies again, and more severely, to speech. Automatic speech recognition for Indian languages has to handle a wide range of regional accents, dialectal variation within a single language, and background noise conditions typical of real phone calls, on top of the same code-mixing challenge that affects text. A speech pipeline that performs well on clean, scripted test audio can degrade noticeably on a real customer call with background noise, an unfamiliar accent, or a sentence that slides between Hindi and English partway through.

This is one reason a text-only demo of a model's Indian-language ability tells you less than it seems to. Voice deployments need to be evaluated on voice, ideally with audio that resembles your actual customer base rather than a clean studio recording.

How to evaluate a vendor claim honestly

Vendor marketing tends to state language support as a flat list: "supports Hindi, Tamil, Telugu, Bengali, and 18 more languages." That list tells you almost nothing about quality. A model can technically process a language while still handling it noticeably worse than English, and the list does not distinguish between the two.

Push past the list and ask specific, testable questions: does it answer natively or translate internally, does it hold up on code-mixed input, does it return scripted text in the correct script rather than romanized text, and for voice, was it tested on real accented, noisy audio rather than clean studio recordings. Ask for a live demonstration in the specific language and dialect your customers actually use, not a generic showcase language like Hindi if your customer base is primarily Tamil or Telugu.

What open Indic resources actually offer

Two resources come up repeatedly in this space, and both are worth knowing about without overstating what either delivers. AI4Bharat, based at IIT Madras, has released permissively licensed, production-quality speech and language models covering India's scheduled languages, and is a genuine and credible open resource for teams building their own Indic-language pipeline. India's Bhashini programme, run under MeitY's National Language Translation Mission, exposes speech recognition, text-to-speech, translation, and OCR pipelines through an API for the same set of languages, and is free for non-commercial use with paid access for commercial use.

Neither of these is a guarantee of production-grade reliability on its own. Bhashini in particular is actively developed and government-backed, but independent, verified uptime and service-level data for production use is not well established as of 2026. Both are reasonable building blocks or points of comparison when evaluating a vendor, not a finished, drop-in product.

A practical comparison to run before choosing

Use this as a working checklist when comparing systems for an Indian-language deployment.

• Native generation versus translate-then-respond: ask directly, and verify with a live test rather than taking the answer at face value.
• Code-mixed handling: test with a realistic Hinglish or similarly mixed sentence, not a clean single-language sentence.
• Script fidelity: confirm scripted-language queries return scripted-language answers, not romanized text.
• Voice tested on real conditions: ask whether speech recognition was evaluated on accented, noisy, real-world audio, not just clean studio recordings.
• Dialect and regional coverage: test the specific language and region your customers are in, not a generic showcase language.
• Reference points, not endorsements: AI4Bharat's open models and Bhashini's government-backed API pipelines are useful comparisons or building blocks, but confirm any production claim independently rather than assuming either is SLA-backed out of the box.