Best AI Voice Calling Agents for Telugu, Kannada, Urdu, Malayalam and Other Indian Languages (2026)

The operations head of a two-wheeler finance company in Vijayawada has a spreadsheet open with three columns: vendor, "Telugu supported?", and demo date. Every row in the second column says yes. Last week she sat through four demos, and all four agents spoke clean, confident Telugu. Then she asked one vendor to call her field collections manager, a man from Guntur who switches between Telugu and English mid-sentence, says amounts in English, and answers "haa, cheppandi" before the bot has finished its greeting. The agent talked over him twice, read "₹4,850" back as a string of Telugu digits nobody uses on the phone, and then asked him to repeat himself in a register that sounded like a news anchor reading a government notice.

Every vendor said yes to Telugu. What she actually needed to know was something else: which of the four layers in the call (telephony audio, speech recognition, the reasoning model, and the synthetic voice) actually handle Telugu the way her borrowers speak it on a ₹9,000 phone in a moving auto.

This post is a buyer's map for that question across seven languages: Telugu, Kannada, Urdu, Malayalam, Tamil, Marathi and Bengali, with Hindi covered briefly because every regional deployment ends up touching it. It names the platforms worth shortlisting, shows what each one publicly documents per language, lists the language-specific failure modes we see in production, and gives you a one-week test you can run before signing anything.

Why "supports 20 languages" tells you almost nothing

Language support in voice AI is not one capability. It is four, stacked, and a call fails at the weakest one.

Telephony audio comes in at 8 kHz narrowband on most Indian PSTN and mobile routes. Speech models are mostly trained and benchmarked on 16 kHz audio. A model that is excellent on a podcast clip can lose a meaningful share of its accuracy on a compressed mobile call from a Tier-3 town, and that loss is not evenly distributed across languages.

Speech recognition (STT) has to handle the language as spoken, not as written. That means dialects, English loanwords, code-switching inside a sentence, and numbers spoken in English inside a Telugu or Malayalam sentence.

The reasoning model has to understand the transcript and reply in the right register. Large language models are noticeably better in Hindi than in Kannada or Malayalam, simply because of the volume of training text.

Speech synthesis (TTS) has to produce a voice that sounds like a person from the region, not a textbook. This is the layer where support most often silently disappears. Urdu is the clearest example below.

A vendor who says "we support Kannada" might mean any one of these four layers. The useful question is narrower: which STT and TTS run underneath your Kannada calls, at what sample rate, and can I hear ten recorded production calls from Karnataka?

The size of the opportunity, by language

Regional languages are not a niche. Census 2011 (the most recent language data published) counted these first-language speakers:

Language	Speakers (Census 2011, millions)	Main states
Hindi	528.3	UP, Bihar, MP, Rajasthan, Delhi and others
Bengali	97.2	West Bengal, Tripura, Assam (Barak Valley)
Marathi	83.0	Maharashtra
Telugu	81.1	Andhra Pradesh, Telangana
Tamil	69.0	Tamil Nadu, Puducherry
Gujarati	55.5	Gujarat
Urdu	50.8	UP, Telangana, Karnataka, Maharashtra, Bihar
Kannada	43.7	Karnataka
Malayalam	34.8	Kerala, Lakshadweep

The numbers understate the phone-call reality in two ways. First, many Urdu speakers are concentrated in cities (Hyderabad, Lucknow, Bhopal, Bengaluru's older neighbourhoods), which is exactly where lending, e-commerce and healthcare call volumes are densest. Second, second-language speakers matter: a large share of Bengaluru's customers will happily take a call in Kannada, Tamil, Telugu, Hindi or English, and the right first language depends on the pin code, not the state.

For a lender or D2C brand, the commercial point is simple. If you call South India in Hindi or English only, you are not reaching a sizeable slice of your book, and the ones you do reach answer in their second language, which costs you comprehension and trust on exactly the calls (payments, delivery confirmations, consent) where both matter.

What each platform publicly documents, per language

Before the shortlist, here is what the main speech layers that Indian voice agents are built on say on their own documentation pages, as checked in October 2026. This is the floor of what you can rely on. Anything not on this table is a vendor claim to test, not a fact.

Language	Sarvam STT (Saaras)	Sarvam TTS (Bulbul)	Google STT (Chirp 3)	Google STT telephony model	ElevenLabs Multilingual v2 / Flash v2.5	Gnani (site)	Bolna (site)
Telugu	Listed	Listed	Listed	Not listed	Not listed	Listed	Named
Kannada	Listed	Listed	Listed	Not listed	Not listed	Listed	Not named
Urdu	Listed	Not listed	Not verified	Not listed	Not listed	Not listed	Not named
Malayalam	Listed	Listed	Listed	Not listed	Not listed	Listed	Not named
Tamil	Listed	Listed	Listed	Not listed	Listed	Listed	Named
Marathi	Listed	Listed	Listed	Not listed	Not listed	Listed	Not named
Bengali	Listed	Listed	Listed	Not listed	Not listed	Listed	Not named

Three things jump out.

Urdu is the asymmetric language. Sarvam's speech-to-text lists Urdu among its 20-plus languages, but its Bulbul text-to-speech lists eleven language codes and Urdu is not one of them. A platform built on that stack can understand an Urdu speaker and then answer in a different voice entirely. Ask any Urdu vendor which TTS produces the voice, and listen to it.

Google's dedicated telephony STT model lists Hindi only among Indian languages. The Chirp models cover the rest, but they are general models, not ones tuned for 8 kHz phone audio. That does not make them unusable on calls, but it does mean the vendor has to have done the narrowband work themselves.

ElevenLabs' recommended low-latency agent models list Hindi and Tamil among Indian languages. Their newer models list more. If a vendor's regional demo uses ElevenLabs voices, ask which model, because the one that sounds best in a demo is not always the one fast enough for a live call.

"Not named" for Bolna means the public homepage claims "10+ vernacular Indian languages" and names Hindi, Hinglish, Tamil and Telugu, without listing the rest. That is not evidence the others are missing. It means you should ask.

The shortlist: who to evaluate, and for what

This is a buyer's shortlist, not a ranking. The right answer depends on whether you want to buy a working agent or build one, and on which languages carry most of your volume.

1. Caller Digital

The platform this blog belongs to, so weigh this entry accordingly. Caller Digital runs outbound and inbound voice agents across Hindi, English and the major South and East Indian languages, with routing that picks the opening language from CRM data (state, pin code, past call language) rather than asking the customer to "press 2 for Telugu". The workflows that carry most regional volume are COD verification, EMI reminders, appointment reminders and lead qualification, with numbers, amounts and dates handled as spoken English inside regional sentences, which is how customers actually say them.

Best fit: lenders, D2C brands and healthcare networks who want the agent, telephony and CRM integration delivered as one system rather than assembled. Ask us for recorded production calls in your language and region, and hold us to the same one-week test described below.

2. Sarvam AI

Sarvam publishes India-specific speech models: Saaras for speech recognition (listing more than 20 Indian languages including Urdu, with code-mix and transliteration output modes) and Bulbul for speech synthesis (eleven language codes including all the major South Indian languages). Its documentation includes guides for building voice agents and an integration guide for running agents on Exotel telephony.

Best fit: teams with engineers who want to build or customise their own agent on strong Indian-language components. Not a fit if you want a managed outbound programme without an engineering team.

3. Gnani.ai

Gnani is a Bengaluru-based speech company whose site claims support for 40+ languages and explicitly lists Hindi, Bengali, Kannada, Gujarati, Punjabi, Marathi, Telugu, Tamil and Malayalam, with its own speech-to-text, text-to-speech and language models. It has a long history in BFSI contact centres.

Best fit: large BFSI and enterprise contact centres that want an established Indian speech vendor. Urdu is not on the site's named list, so confirm it if Urdu matters to you.

4. Bolna

Bolna is a voice agent platform aimed at builders and fast-moving teams. Its site claims 10+ vernacular Indian languages and names Hindi, Hinglish, Tamil and Telugu. It orchestrates multiple underlying speech and model providers, which means language quality depends on the providers you configure.

Best fit: startups and product teams that want to configure agents themselves and are comfortable choosing STT and TTS providers per language. Test Kannada, Malayalam and Urdu specifically.

5. Exotel

Exotel is primarily cloud telephony, and a large share of Indian voice agents (including ones built on Sarvam) run on Exotel numbers. Its own GenAI voicebot product page describes Hindi, English and Hinglish. For other regional languages, Exotel is usually the telephony layer under someone else's agent rather than the agent itself.

Best fit: as the telephony partner under an agent platform. For how Indian telephony providers compare on that layer, see our comparison of telephony partners for voice AI in India.

6. Google Cloud and ElevenLabs as components

Neither is an Indian calling agent on its own, but many agents use them as the speech layer. Google's Chirp models cover the major Indian languages; ElevenLabs is often chosen for voice quality. The table above shows where their documented coverage stops. If a vendor's stack depends on them, the per-language gaps in that table are the vendor's gaps too.

On the long tail of "best Telugu AI agent" pages

Search for any of these languages and you will find dozens of vendor pages titled "best AI voice agent in Kannada" or "Urdu voice agent", many generated from one template with the language name swapped in. Some are real products. Treat a language landing page as a marketing claim and a recorded call from that region as evidence. One industry roundup we read for this piece openly noted that no public dialect-by-dialect accuracy benchmark exists for Telugu. That is accurate, and it is why the one-week test below matters more than any listicle, including this one.

Language by language: what actually breaks

Every language has its own failure pattern on phone calls. These are the ones we see most often, and the question to ask a vendor for each.

Telugu

Telugu on the phone is rarely textbook Telugu. Hyderabad callers mix in Dakhini Urdu and English; Coastal Andhra, Telangana and Rayalaseema speech differ noticeably in vocabulary and rhythm. English nouns ("loan", "EMI", "delivery", "address") are used as-is, and amounts are almost always spoken in English.

The common failure is a TTS voice in formal written Telugu that sounds like a government announcement. Customers respond to it as an official notice and either hang up or become guarded. The second failure is reading numbers aloud in Telugu number words when the customer said them in English.

Ask: play me five production calls from Telangana and five from Coastal Andhra, and show me how amounts are spoken back.

Kannada

Bengaluru is the hardest city in India for language routing. A single collections book there can include Kannada, Tamil, Telugu, Hindi, Urdu and English first-language speakers, and many of them will switch mid-call. Outside Bengaluru, North Karnataka (Hubballi-Dharwad, Belagavi) and coastal Karnataka sound quite different from Mysuru Kannada.

The common failure is language-locking: the agent picks Kannada from the pin code, the customer answers in Tamil, and the agent carries on in Kannada. The fix is detecting language from the first caller turn and switching, not from the CRM field alone. For Bengaluru-specific deployment considerations, see our voice AI in Bangalore page.

Ask: what happens when the customer replies in a different language from the one you opened in? Show me a recording.

Urdu

Spoken Urdu and spoken Hindi are acoustically close; the differences are vocabulary, formality and script. That makes Urdu recognition more achievable than the Urdu TTS question, which is where vendor stacks thin out (as the table above shows for one major Indian TTS). Hyderabad's Dakhini Urdu and Lucknow's Urdu are also very different registers.

The common failure is an agent that understands an Urdu speaker and replies in Hindi-flavoured speech with Sanskritised vocabulary, which reads as careless at best. On debt and healthcare calls, that tone mismatch costs cooperation.

Ask: which TTS voice speaks Urdu on my calls, and is it the same provider that speaks Hindi?

Malayalam

Malayalam is agglutinative: words are long, carry a lot of grammatical information, and are spoken fast. That makes it harder for both recognition and turn-taking, because a pause that looks like end-of-turn may just be the middle of a long word sequence. Kerala's large Gulf NRI population also means many calls are about remittances, family members abroad, and deliveries to households where the buyer is not the recipient.

For e-commerce, the "malayalam voice bot for ecommerce" use case is real: Kerala buyers respond well to COD confirmation calls in Malayalam, and English-only confirmation calls are routinely ignored. See how the workflow runs in our COD order confirmation use case.

Ask: what is your end-of-turn timeout for Malayalam, and does the agent interrupt long answers?

Tamil

Tamil has the sharpest gap between written and spoken forms of any language here. Formal written Tamil (used in news and documents) sounds stiff and distant on a phone call; Chennai colloquial Tamil, Kongu Tamil around Coimbatore and Madurai Tamil all differ. Tamil speakers also tend to resist Hindi on the phone, so a Hindi fallback is usually worse than English.

The common failure is a TTS that renders formal Tamil from an LLM response written in formal register. The fix is generating the response in spoken style, not just choosing a better voice. For city context, see voice AI in Chennai.

Ask: is your Tamil response text generated in spoken or written register?

Marathi

Mumbai Marathi is heavily mixed with Hindi and English, while Vidarbha and Marathwada Marathi differ from the Pune standard. The bigger practical issue is that many Mumbai and Pune customers prefer Hindi on commercial calls even when Marathi is their first language, and some take offence at the wrong guess either way.

The common failure is a fixed Marathi opener for every Maharashtra pin code. Use call-history language where you have it, and open with a short bilingual greeting where you do not.

Ask: how do you choose between Marathi and Hindi for a Pune number with no call history?

Bengali

Bengali calls from West Bengal, Tripura and the Barak Valley differ in accent and vocabulary, and urban Kolkata Bengali carries plenty of English. Bengali is well covered by the major speech providers, so the real differentiator is voice warmth and correct handling of honorifics (aapni versus tumi registers), which affects how respectful the agent sounds to older customers.

Ask: which register does your Bengali agent use with a 60-year-old customer, and can I change it?

Hindi (briefly)

Hindi is the best-covered Indian language at every layer, but "Hindi" in a demo is usually Delhi Hindi. Bhojpuri-influenced Hindi in Bihar and Awadhi-influenced Hindi in Lucknow are where accuracy drops. We cover this in detail in our best Hindi voice AI agent platform guide.

How a regional call should actually flow

The architecture matters more than the vendor logo. A regional-language call that works follows this sequence:

1. Pick the opening language from data. Use call history first, then the customer's stated preference, then pin code. State alone is a poor proxy in cities.
2. Open short, and bilingual where uncertain. A one-line greeting in the predicted language with a two-word English or Hindi tail lets the customer choose without a menu.
3. Detect language on the first caller turn. Do not wait for a full sentence. Short answers ("haan", "aamaa", "howdu", "athe") carry strong language signal.
4. Switch once, cleanly. If the customer answers in a different language, switch STT, the response language and the TTS voice together, and do not switch back unless the customer does.
5. Keep amounts, dates and IDs in the form customers use. Usually that is English digits inside a regional sentence. Confirm them back the same way.
6. Log the language per turn. This is what lets you see where calls fail by region, and it is the evidence you need to tune the routing.

This is the difference between a demo and a deployment. A demo is one language, one speaker, one clean line. A deployment is fifty thousand calls a month across mixed books, and the language decision is made, and occasionally corrected, on every one of them. For the architecture behind multi-language routing in more depth, see our guide to multilingual voice AI across Hindi, Tamil, Telugu and Bengali.

The numbers that matter

Accuracy benchmarks for regional languages are scarce and mostly published by vendors about their own models. We maintain our own measurements in our WER benchmarks for Indian languages, and we would rather point you there than repeat numbers out of context. For buying decisions, four operational metrics are more useful than word error rate:

Metric	What it measures	What good looks like in our deployments
Language match on first turn	Share of calls where the opening language was the one the customer answered in	85–95% with call-history routing; often 60–75% with state-level routing in metro books
Task completion in language	Share of connected calls that reach the workflow outcome (confirmed, paid, rescheduled) without switching to a human	Within 5–10 points of the Hindi or English baseline for the same workflow
Number capture accuracy	Share of amounts, dates and order IDs captured correctly on the first attempt	Above 95% on the confirmation step; below 90% means the agent is reading numbers in the wrong form
Talk-over rate	Share of turns where the agent speaks over the customer	Under 5%; Malayalam and Telugu usually need longer end-of-turn tuning than Hindi

The most expensive mistake is measuring only Hindi and English and assuming the regional numbers will be similar. They usually are not on day one. The gap closes with tuning, but only if you measure it per language from the start.

Compliance specifics for regional-language calling

Language is not only a customer-experience question in India. It has regulatory weight.

DPDP Act 2023. Section 5 requires that the Data Principal be able to access the consent notice in English or in any language specified in the Eighth Schedule of the Constitution. All seven languages in this post are Eighth Schedule languages. If your agent collects consent or reads a privacy notice on the call, plan for doing it in the customer's language, and keep the recording. See our DPDP compliance guide for AI calling.

RBI Fair Practices Code. RBI's fair practices guidance for lenders expects key loan terms to be communicated in a language the borrower understands, and the conduct rules for recovery calls apply regardless of whether the caller is a person or an agent. A Telugu-speaking borrower receiving collections calls only in English is a conduct risk, not just a conversion problem.

TRAI DLT and calling hours. Header and template registration, DND scrubbing at dial time, and calling-window rules apply identically across languages. Regional campaigns do not get a separate regime; they just need the same controls wired in.

IRDAI. Insurance sales and renewal calls need disclosed recording and clear product disclosures. If you sell in Kannada, the disclosure needs to be intelligible in Kannada.

A one-week test you can run before signing

Every vendor will pass a demo. This test is designed so that only a production-ready platform passes.

Day 1: build the test set. Pull 200 numbers from your own book, split across your top three languages and at least two regions per language (for example, Telangana and Coastal Andhra for Telugu). Include customers with known code-switching behaviour.

Day 2: script one real workflow. Use a workflow with numbers in it: COD confirmation with an order value, an EMI reminder with an amount and a due date, or an appointment confirmation with a date and time. Do not test on a generic greeting.

Days 3 to 4: run live calls. Have the vendor run the calls on your numbers, or on a consented internal test group if your compliance team prefers. Record everything.

Day 5: score per language. Measure the four metrics in the table above, per language and per region. Listen to at least 20 calls per language yourself, and have a native speaker from the region rate tone on a simple 1–5 scale.

Days 6 to 7: ask for the fix. Give the vendor the failures and see how quickly they can change routing, timeouts, register or voice. How fast they iterate in week one predicts how the next twelve months will go.

If a vendor will not run this test, or will only run it on their own numbers, that tells you most of what you need to know.

What changes in the next twelve months

Three shifts are worth planning around.

Indian speech models keep moving fast. Sarvam, Gnani, AI4Bharat and the global providers have all expanded Indian-language coverage over the past year. Languages that are thin today, Urdu TTS in particular, are likely to fill in. Build your stack so you can swap the speech layer per language without rebuilding the agent.

Speech-to-speech models will arrive for regional languages. Models that go from audio to audio, without a separate transcript step, are improving latency and naturalness. Expect them first for Hindi and then for the larger South Indian languages. Keep the logging and compliance controls that a transcript step gives you when you adopt them.

Regional-language calling becomes table stakes in South India. As more lenders, insurers and D2C brands run regional calls, customers who receive Telugu or Tamil calls from one brand will notice when another brand calls in English. The advantage goes to whoever builds language-level measurement now.

Bottom line

There is no single "best AI voice calling agent" for Telugu, Kannada, Urdu or Malayalam, because language support is four layers deep and vendors differ at each layer. The publicly documented coverage tells you where to look: Urdu text-to-speech is the thinnest layer, telephony-tuned recognition outside Hindi is rare, and the most popular low-latency international voice models list only Hindi and Tamil among Indian languages. Shortlist two or three platforms that fit your build-or-buy preference, run the one-week test on your own customers, and choose on per-language task completion and number accuracy, not on the length of a vendor's language list.