Hindi, Tamil, Telugu, Marathi, Bengali — switching mid-sentence, the way people actually talk.
Hindi, Tamil, Telugu, Marathi, Bengali, Gujarati, Kannada, Punjabi and English — on the phone and on WhatsApp, switching mid-sentence the way real conversations in India do. Most global voice stacks cannot do this. It is the whole point of building here.
We ask for 30 minutes first — we cannot price a build we have not seen inside. No obligation, and you keep the scope either way.
Built and handed over by AcquihireTech · Delhi NCR
Live client systems: ReRoom.in (Interiors, Mumbai) · ePrep Global (Test prep, New Delhi) · ARVEX (Construction & tiles, Delhi NCR). We publish the names, not invented numbers — read the case studies.
An AI voice agent for Indian languages is the one thing a global voice platform struggles to match, because the hard part is not translation — it is the mixed Hindi-English sentence, the regional accent, and the switch halfway through. This build covers an AI voice agent in Hindi as standard and eight more Indian languages where your market needs them, with every language scripted natively, reviewed by a speaker, and tested against its own scenario set before it takes a single live call. An AI voice agent in Hindi is where most builds start. An AI telecaller in Hindi handling outbound, an AI voice agent in Tamil for a southern market, or a regional language AI chatbot on WhatsApp are the same build with more language cycles in it — which is what a multilingual AI agent India actually needs looks like in practice.
Where an English-only agent loses the sale
English-only loses the buyer
A customer who can read your website in English still wants to negotiate, ask and complain in their own language.
Hiring per language does not scale
A Tamil-speaking agent, a Marathi-speaking agent, a Bengali-speaking agent — each one hired, trained and eventually replaced.
Translated scripts sound wrong
A literal translation of an English script reads as a translation. Customers hear it immediately and trust it less.
Nobody switches languages cleanly
Real Indian conversations move between two languages inside one sentence. Most systems break at exactly that point.
What the AI voice agent for Indian languages does
Nine languages and English
Hindi, Tamil, Telugu, Marathi, Bengali, Gujarati, Kannada, Punjabi, Malayalam
Switches mid-conversation
Handles the Hindi-English mix people actually speak, without restarting
Voice and chat, same brain
The same qualification logic on a phone call and on a WhatsApp thread
Written for the language
Scripts drafted in-language and reviewed by a speaker, not translated from English
Detects and adapts
Picks up the caller's language from the first sentence and stays there
Escalates to the right human
Routes to a team member who speaks that language, where you have one
How the multilingual voice agent gets built
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01
Language map agreed
Which languages, which markets, which channels — scoped against where your revenue actually comes from.
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02
Scripts written in-language
Drafted natively and reviewed by a speaker, so the agent sounds like a person from that market rather than a translation.
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03
Tested per language
Each language runs against its own scenario set — including accents, mixed-language input and regional phrasing.
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04
Live, then tuned per language
Performance is reviewed language by language, because they do not improve at the same rate.
What we will not let it do.
Most of this category sells an outcome it does not control. We would rather show you the rules the build is held to — every one of these is written into the scope, tested before go-live, and yours to change afterwards.
- Every language is tested separately against its own written scenario set. A language that does not clear the bar does not go live, regardless of what the others are doing.
- Scripts are reviewed by a speaker of the language before launch. We do not ship machine-translated English copy as a regional-language experience.
- Mixed-language input is an explicit test case, not an edge case — because it is how most Indian phone conversations actually run.
- Where the agent cannot hold a language reliably, we say so and scope it out rather than shipping a version that frustrates the customer.
Worth building — and worth not building.
A build at the wrong moment is worse than no build. If the right-hand column describes you, say so on the call and we will tell you what to do instead.
Build this if
- A meaningful share of your customers do not transact in English
- You sell across two or more language markets
- You can find a speaker of each language to review the script
- Volume in each language justifies its own build cycle
Do not build this yet if
- Your customers are comfortable in English — buy a single-language build
- You need nine languages live at once on a three-week timeline
- No one on your side can review copy in the target languages
Everything in a multilingual voice agent build
- Language map scoped to your actual markets and channels
- In-language script development, reviewed by a speaker per language
- Voice agent across your chosen languages on Indian telephony
- WhatsApp and web chat in the same languages, same logic
- Mid-conversation language switching and mixed-language handling
- Per-language scenario testing before any language goes live
- Routing to language-matched human team members where available
- Per-language reporting: volume, completion, escalation, booking
- Per-language tuning cycles during the engagement
- Documented SOPs and full credentials handed to you at close
What we need from you: roughly one to two hours a week during the build — access to the accounts involved, a look at how the work is done today, and written sign-off on what the agent may say. Pass-through costs: telephony minutes, WhatsApp conversation charges and model usage are billed to you directly by those providers at their own rates. We do not resell or mark them up, and we size them against your real volume in the written scope.
It has to fit the stack you already run.
- Telephony
- Exotel, Ozonetel, Knowlarity, MyOperator, Twilio
- Messaging
- WhatsApp Business API, SMS via DLT-registered templates
- CRM
- HubSpot, Zoho CRM, LeadSquared, Pipedrive
- Automation
- n8n, Make, Zapier
- Calendar
- Google Calendar, Calendly, Cal.com
- Analytics
- Per-language dashboards, GA4, call-source attribution
Anything else with a documented API or webhook can be connected — the connectors are named in the written scope, and anything discovered later is priced before it is built.
Two ways to start.
Agent builds are scoped and priced individually — the figure moves with channel count, integration count, volume and how much logic sits behind the conversation. Published pricing for the four engines is on the Engagements page and is a fair guide to the order of magnitude. The audit produces a written scope and a written price, and both are yours to keep.
Pilot
Two languages, one channel, one flow. Enough to compare completion and booking rates against your English-only baseline.
Live in 4–5 weeks
Get a written scopeProduction
Full language map, voice plus WhatsApp, language-matched routing, per-language reporting, and 90 days of per-language tuning.
Live in 8–12 weeks
Get a written scopeSystems Audit
Thirty minutes, free. We map your pipeline live and name the single biggest constraint — including when the honest answer is that you do not need this agent.
30 minutes · No charge
Book the auditYou own it. You can switch it off.
Data stays yours
Scoped against India's DPDP Act. Records sit in accounts you own, with retention rules you set. Your data is not used to train third-party models.
Approvals before it speaks
Everything the agent may say about price, scope and commitments is approved by you in writing before launch, and changeable afterwards.
A human is always reachable
Escalation paths are defined with you and tested before go-live. There is a documented off switch, and it is yours.
AI voice agents in Indian languages — what buyers ask first
Hindi, Tamil, Telugu, Marathi, Bengali, Gujarati, Kannada, Punjabi, Malayalam and English, on voice and on WhatsApp. Which of those go live depends on where your customers actually are — we scope that rather than switching all of them on.
Yes, and that is treated as a core test case rather than an edge case. Most Indian phone conversations move between two languages inside a single exchange, and an agent that breaks there is not usable.
No. They are drafted in-language and reviewed by a speaker before launch. A translated English script sounds like a translated English script, and customers trust it less.
Each language is tested against its own scenario set including accent variation and regional phrasing. Where a language does not hold up reliably, we scope it out rather than ship it.
Yes — the same qualification logic runs on a phone call and on a WhatsApp thread, so a customer who starts on one and moves to the other does not start again.
It routes to a team member who speaks that language where you have one, with the conversation already summarised. Where you do not, it captures everything and schedules a callback.
It is more work — every language needs its own scripting, review and testing cycle. The scope reflects the number of languages rather than a flat multiplier, which is why it is quoted per build.
Yes, in every language, along with the recordings, transcripts and credentials, transferred to you at project close.
No, and that distinction is the entire product. A literal translation of an English script reads as a translated English script, and customers hear it immediately. Every language here is drafted in-language and reviewed by a speaker before launch. Where a language does not hold up reliably in testing, we scope it out and tell you rather than shipping a version that frustrates your customer.
For two languages and steady volume, hiring is often cheaper and better — we will say so. It stops being the obvious answer at four or five languages, where you are hiring, training and eventually replacing a person per language for demand that may only justify a few hours a day each. The audit compares the two against your actual per-language volume.
Yes — each language needs its own scripting, native review and test cycle, so scope tracks language count rather than a flat multiplier. That is quoted in the written scope, not guessed here. Running costs are pass-through telephony and model usage at the providers' rates. Tuning runs per language during the engagement because languages do not improve at the same rate, and afterwards you own every script in every language.
Agents that work alongside this one.
AI Calling Agent (India)
A calling agent that picks up on the first ring, from an Indian number.
AI Receptionist for Clinics
The clinic phone that never rings out.
AI Telecalling Agent
Outbound calling that does not depend on who turned up today.
WhatsApp CRM Agent
Your WhatsApp CRM is currently one person's phone.
Put a Multilingual Voice & Chat Agent to work this quarter.
The 30-minute Systems Audit maps your pipeline live, names the single biggest constraint, and tells you whether this is the right agent to start with. You keep the diagnosis either way.
We ask for 30 minutes first — we cannot price a build we have not seen inside. No obligation, and you keep the scope either way.