We build AI voice agents that answer every inbound call, book appointments, resolve queries from live data, and hand off only what they can't handle. Our production agent at Battery Smart handles driver support in Hindi, with zero human handoff, 24/7.
A voice AI agent is software that holds a real spoken conversation over the phone, understands what the caller actually wants, does the work to answer them, and speaks back in a natural voice. It is not a menu tree and it is not a chatbot with a speaker bolted on. The whole point of AI voice agent development is to replace "press 1 for billing" with "so I can see your last swap was at 4:12 this afternoon, and your balance is clear."
Under the hood, a voice AI agent runs a four-stage loop on every turn of the conversation:
An IVR can only route you. A rule-based chatbot can only follow the script it was given. A voice AI agent resolves the request. That gap is the entire reason AI voice agent development has become a category worth investing in.
The business case follows directly from that. Most inbound calls to a support line are lookups, not judgment calls: where is my order, is this slot free, what is my balance. Every one of those handled by a voice AI agent is a call your trained staff never has to take, at any hour, in any of the languages you serve. The cost of support stops scaling one-to-one with call volume, and your team is freed to handle the calls that genuinely need a human. That is the outcome good voice AI agent development is built to deliver, and it is why we measure a project by resolution rate on real traffic rather than by how polished the demo looked.
Every voice AI agent development engagement follows the same six steps. We built this process on a live system, not a slide deck, so it is tuned to get an agent onto real traffic without breaking the caller experience along the way.
We start by finding the calls worth automating first: the highest volume, most repetitive ones. Support calls, appointment booking, order status, and lead qualification are almost always the top of that list. We listen to real recordings and read transcripts so the first version of the agent targets calls that are common enough to move the numbers and structured enough to automate safely.
Before a line of code is written, we map every intent, every edge case, and every escalation path. This is not a flowchart on a whiteboard. It is a working state machine that defines what the agent knows, what it is allowed to do, and the exact point at which it stops and hands a call to a human. Getting this right is what keeps a voice AI agent from guessing.
We select the STT engine per language: Sarvam AI for Hindi and regional languages, Cartesia Ink 2 for English. On top sits the LLM agent layer for intent recognition and data retrieval, then TTS with voice cloning and natural prosody. We architect the pipeline to a sub-one-second time-to-first-audio-chunk target, because anything slower and the caller starts talking over the agent.
This is where most voice AI agent development stops short. Our agents pull real data mid-conversation: open appointment slots, account status, order tracking, all through your existing APIs. The caller asks a live question and gets a live answer, not a scripted approximation. We build to your stack rather than forcing you to migrate onto ours.
Before go-live we run load tests, edge-case scripts, accent-variation testing, and latency benchmarking. We deliberately try to break the agent with the calls that break call centres: heavy background noise, code-switching between languages, callers who change their mind halfway through. The agent ships when it holds up under those, not before.
We deploy to your cloud or on-prem, wired to a call analytics dashboard with conversation quality scoring. Then we tune continuously from real call data: resolution rates, escalation reasons, and the exact phrases where the agent hesitated. A voice AI agent gets better every week it is live, and we build that feedback loop in from day one.
Battery Smart is India's largest EV battery-swapping network, valued at over $340M, with more than a million IoT devices in the field. When their inbound driver-support line could not scale with the network, we built them a production Hindi-language voice AI agent to answer it.
The agent runs the full pipeline. Sarvam AI handles Hindi speech to text and text to speech, tuned for the noisy, real-world audio of a driver calling from a busy swap station. An LLM agent layer interprets the driver's intent, then calls Battery Smart's internal APIs mid-conversation to fetch live driver, battery, and station data: battery status, station availability, swap history, and payment details. It answers conversationally in the language the driver actually speaks, including natural code-switching between Hindi and English.
The result is a voice AI agent that resolves in-scope driver queries with zero human handoff, runs 24/7 without shift staffing, and has reduced Battery Smart's dependence on a growing call centre. When a query falls outside its defined scope, it escalates to a human with the full conversation context attached, so nothing is dropped and no one gets a wrong answer from an agent guessing.
Building this taught us the things you only learn in production. Drivers call from loud swap stations, so the STT had to be tuned on real field audio rather than clean samples. They switch between Hindi and English mid-sentence, so the agent had to follow the meaning across both. And because a wrong answer about a payment erodes trust fast, the scope guardrails had to be strict about when to escalate. Those lessons are baked into every voice AI agent we build now.
This is the part no competitor in voice AI agent development can copy. They have claims and demos. We have a named client, a live system on real traffic, and outcomes we can point to. If you want a voice agent that works the way a production system has to, that track record is the difference.
The same core pipeline adapts to very different jobs. These are the voice AI agent use cases we build most often, each wired into the live system that makes the answer real.
Handle FAQs, account queries, and status checks without tying up trained human agents. The voice AI agent looks up the real record, answers in plain language, and escalates only the calls that genuinely need a person, with full context attached.
Real-time calendar integration means the agent offers slots that are actually open, confirms the booking, reschedules on request, and sends reminders. Callers book at 2am the same way they would with a receptionist, and your calendar never double-books.
The agent engages inbound callers the moment they ring, captures the details that matter, scores the lead against your criteria, and routes hot prospects straight to sales. No lead sits in a voicemail queue going cold overnight.
Patient intake, appointment scheduling, after-hours triage, and treatment FAQs answered from a RAG knowledge base grounded in your own clinical content. The voice AI agent covers the front desk when the front desk is closed, without inventing medical answers. See how we build voice AI agents for healthcare clinics →
Property inquiries answered instantly, viewings booked into the agent's calendar, and follow-ups scheduled automatically. An AI voice agent means no serious buyer ever hits a full voicemail box on a Sunday afternoon. See voice AI agents for real estate →
Accent-neutral, multilingual, and built for high volume, the voice AI agent absorbs the repetitive inbound and outbound work so human staff handle only the calls that need judgment. It scales with call volume instead of with headcount. See voice AI agents for BPOs and call centres →
We are not tied to a single vendor. We pick each layer of the stack for the language, latency, and integration the job actually needs. Here is what we build voice AI agents on.
We have shipped a voice agent that handles real support calls at scale at Battery Smart. Most agencies can show you a demo that works once in a controlled room. We deploy agents that hold up on live traffic, in the language your callers actually speak, every hour of every day.
The engineer who built Battery Smart's voice agent is the one who builds yours. There is no account manager, no offshore handoff chain, and no junior team learning on your budget. You work directly with the person doing the voice AI agent development.
Hindi, English, and Indian regional languages, including natural code-switching, are built in from the start. We architect on Sarvam AI alongside OpenAI, not on an English-first stack that treats every other language as an afterthought. That is why our production agent runs Hindi-first.
Real-time voice falls apart above a one-second response time. Our pipeline is engineered for it: WebSocket streaming, delta-diff transcript handling so the agent reasons on partial speech, and persistent connections that avoid per-call cold starts. Latency is a first-class design constraint, not something we hope to fix later.
We scope to a defined outcome and quote a fixed price before we start, so there is no open-ended hourly billing. These ranges give you a sense of where a project lands.
A single use case, one language, and basic integration. The right starting point to automate one high-volume call type and prove the value.
Multi-intent handling, CRM and calendar integration, and human handoff logic. For teams ready to automate a real slice of their inbound and outbound calls.
Multi-language, high-volume, custom brand voice, and full telephony integration. Built for networks running serious call volume across regions.
Every engagement starts with a free consultation to scope your specific needs before any number is committed.
Working out whether to build a voice AI agent in-house or bring in a specialist? These resources go deeper on the decisions that shape a voice AI agent development project.
An IVR routes you through fixed menus and can only send your call somewhere. A voice AI agent understands free speech, works out what you actually want, and resolves it by calling your live systems. One transfers the request; the other answers it.
Yes. We call your production APIs at answer time: CRM records, booking calendars, order systems, and account data. We integrate with your telephony provider and your existing stack rather than forcing a migration, exactly as we did with Battery Smart's internal systems.
English, Hindi, and major Indian regional languages, including natural code-switching between them. Our production agent at Battery Smart runs Hindi-first for driver support, built on Sarvam AI rather than an English-only stack.
We architect to a sub-one-second time-to-first-audio target, which is the threshold where a spoken conversation still feels natural. We hit it with WebSocket streaming, partial-speech reasoning, and persistent connections that avoid per-call cold starts.
It depends on scope and integration complexity, but we roll out in stages: a narrow slice of call types first with humans monitoring, then widen as resolution quality holds. You see the agent working on real traffic before it takes full volume.
Yes. Inbound handles support, account queries, and booking. Outbound handles lead qualification, reminders, and follow-ups. The same voice AI agent pipeline runs both, wired to the same live data.
Book a call and we'll demo a live voice agent, then map what automating your inbound and outbound calls would look like. Every engagement starts with a free consultation to scope your needs.