Conversational AI · Voice AI assistants
Voice AI assistants that finish the call, not just answer it
A voice AI assistant that mishears the first sentence, stalls on an unfamiliar term, and dead-ends with "I didn't catch that" is worse than the IVR system it replaced — callers hang up faster and your support queue fills, not empties. Banao builds voice agents that read real speech, act on live systems, and pass the call to a person with context when they should.
We develop to the number that matters for voice: first-call resolution rate. Every design decision — ASR model choice, latency budget, turn-taking logic, escalation trigger — is made to move that metric, not to make the demo look good.
Elisa— our voice callbot absorbed a national contact surge the carrier's legacy queue could not hold.
What a Banao voice AI assistant includes
A voice agent that survives production is more than a speech model and a prompt. It needs fast ASR, low-latency response, clean turn-taking, live-system integrations, and an escalation path that keeps the caller's context.
ASR tuned to your domain vocabulary
We select and fine-tune speech recognition models on your product names, account types, and industry terms — so the agent catches what callers actually say, not clean benchmark speech.
Sub-second response latency
We architect the pipeline — streaming ASR, parallel intent detection, pre-generated common responses — to stay inside the window where voice conversation feels live, not like waiting on hold.
Turn-taking and interruption handling
The agent detects when a caller has finished speaking, when they are interrupting, and when they are thinking aloud. It does not talk over callers or sit in silence when the answer is ready.
Telephony and contact-centre integration
We wire the voice agent into your existing platform — SIP, PSTN, or cloud telephony APIs — so it appears in the same routing layer as your human agents, not as a separate bolt-on.
Live CRM and policy lookups
The agent answers from your real data — account status, order history, policy terms, appointment slots — retrieved at call time so the caller gets a current answer, not a scripted guess.
Warm escalation with context passed
When the voice agent reaches its limit, it passes the caller's identity, stated intent, and the conversation so far to the human agent. The person who picks up does not start from scratch.
Multilingual and accent handling
We build voice agents that detect the caller's language and switch without a menu prompt — critical for GCC deployments where Arabic, English, Hindi, and Urdu reach the same queue.
Call analytics and resolution tracking
Every call is transcribed, scored against first-call resolution, and reviewed for patterns the agent mishandled — so each month the agent is measurably better than the last.
Why voice AI fails where text chat succeeds — and what to build instead
A text chatbot can wait while you re-read the question, surface a button to click, and recover from a misread by asking you to rephrase. None of that is available in voice. The caller hears a pause as a dead line, has no button to press, and will say "what?" once before they hang up. Voice AI punishes every gap, every mishear, and every dead-end harder than any other interface — and those failures show up immediately in your support call volume.
The ASR model is the start, not the product. Real production voice AI requires a latency pipeline measured in milliseconds, a turn-taking model built for how people actually speak (not how clean transcripts read), intent understanding that survives background noise and regional accents, and an escalation path the caller experiences as a handoff rather than a wall. We design for all of it before a line of code is written.
Latency budget set before architecture begins
We fix the end-to-end response-time target at the start of every voice AI build — because an agent that thinks for three seconds is an agent callers hang up on, regardless of how accurate the answer is.
ASR accuracy tested on your actual call recordings
We benchmark speech recognition against samples from your real contact-centre calls, not clean studio audio, and tune until accuracy on your specific callers meets the bar — not accuracy on generic benchmarks.
Resolution rate as the north-star metric
We track how many calls the voice agent closes without a human, and run every design change against that number. It is the only metric your finance team will recognise as value from the deployment.
We run the systems we build — including conversational AI
Banao uses the conversational AI it sells on its own 300-person engineering operation. InterviewGod — the agent Banao built and runs to screen its own engineering hires — applies the same evaluation discipline we bring to every voice AI assistant: test on real inputs, measure first-call resolution, and do not widen autonomy faster than the data allows.
Our Elisa engagement — a voice callbot that absorbed a national contact surge — was built by the same team that runs Banao's own conversational AI infrastructure. When we tell you a voice AI design will hold under call volume, the team that tells you that has stood behind the same design under real production load.
- InterviewGodConversational AI Banao built and runs to screen its own engineering applicants — every week.
- Elisa voice callbotAbsorbed a national carrier contact surge under the same engineering standards Banao applies to every voice build.
Where we build voice AI assistants
India
Bangalore and Chandigarh hold the delivery bench. Voice AI builds start in weeks and run close to the engineers who maintain them, covering Hindi, regional language ASR, and the DPDP Act data requirements.
UAE and GCC
From Dubai we develop for GCC enterprises — Arabic, English, Hindi, and Urdu in a single voice agent with auto-language detection, and call data kept inside UAE boundaries where PDPL and client policy require it.
US and UK
For US and UK contact centres we build to SOC 2 and UK GDPR expectations, with audit logging and resolution-rate reporting that satisfies the compliance requirements of financial, healthcare, and regulated clients.
When a voice AI assistant is not the right investment
Voice AI earns its build cost in specific conditions. We will tell you before you commit budget whether yours fits:
- Call volume is too low: the ROI maths for voice AI close at contact-centre scale. If your team handles fewer than a few hundred calls a day, a trained human agent costs less and performs better.
- Your callers expect a person: in some industries and markets, an automated voice response damages trust faster than it saves cost. A Discovery Sprint surfaces this before a build commitment.
- Queries are too variable: voice AI performs well on the top intents by call volume. If every call is genuinely unique, the agent will escalate most of them and the containment rate will not justify the investment.
- The telephony stack cannot be integrated: a voice AI agent that cannot reach your CRM or policy data answers from a static script — which a cheaper IVR already does.
How we start — prove the voice agent before we build it
Voice AI builds that fail usually fail because nobody tested ASR accuracy on real caller audio before the contract was signed. We fix that in week one.
- AI Discovery Sprint2 weeks · fixed price
We benchmark ASR accuracy on a sample of your real call recordings, map the top intents by call volume, and return an agent architecture, a latency plan, and ROI maths — yours to keep whether you proceed or not. If you proceed, the Sprint fee is credited against the build.
- Build and integration
We develop the voice pipeline — ASR, NLU, response generation, telephony integration, CRM lookups, escalation logic — with latency and accuracy targets fixed at the start, not negotiated at the end.
- Pilot and continuous improvement
We launch on a contained call slice, measure first-call resolution rate weekly, and expand as the numbers allow. Every call is scored; every month the agent improves on the cases it previously missed.
Frequently asked questions
What kinds of voice AI assistants do you build?
Inbound callbots for customer support, outbound voice agents for appointment reminders and follow-ups, IVR replacement agents that understand natural speech instead of button presses, and internal voice agents for employee-facing workflows. In every case the agent acts on live systems rather than reading from a static script.
How do you handle different accents and languages?
We select ASR models with strong multilingual coverage, test them against recordings from your actual caller base, and fine-tune on your domain vocabulary. For GCC deployments we build a single agent covering Arabic, English, Hindi, and Urdu with automatic language detection — so callers do not select a language from a menu.
What response latency can we expect, and why does it matter?
We target end-to-end response times under 1.5 seconds for most queries, achieved through streaming ASR, parallel intent detection, and pre-computed responses for common queries. A two-second pause in voice reads as a disconnected call — callers hang up before the answer arrives, regardless of its accuracy.
Can you integrate with our existing contact-centre platform?
Yes. We integrate through SIP, PSTN, or cloud telephony APIs depending on your platform. The voice agent appears in your existing routing layer, so calls can move between the agent and human agents without the caller being transferred to a separate system.
How do you measure whether the voice agent is working?
The primary metric is first-call resolution rate — how many calls the agent closes without human escalation. We also track misrecognition rate, average handle time, and escalation trigger distribution. All metrics are reported weekly during the pilot so you can see whether the agent is improving, not just a snapshot at go-live.
What happens when the voice agent cannot answer?
The agent transfers the call to a human agent and passes the caller's name, account context, stated intent, and the conversation transcript. The human agent starts with full context rather than asking the caller to repeat themselves. The escalation trigger is configurable by intent type, confidence threshold, or explicit caller request.
How long does a voice AI build take?
A typical path is a 2-week Discovery Sprint (ASR benchmark, intent mapping, architecture plan), then an 8–12 week build depending on integration complexity and language coverage, then a contained pilot before full rollout. Banao's ~300-engineer bench means the build starts in weeks, not the months that recruiting a team from scratch would require.
What does it cost?
The Discovery Sprint is a fixed price and includes the ASR accuracy benchmark on your call recordings, the agent architecture recommendation, and the ROI model you need to size the full build. Build cost depends on the number of intents covered, the integration surface, and the language requirements — all of which the Sprint pins down before you commit to the build.
Bring your contact-centre problem to us
Tell us the top three reasons callers escalate to a human agent. In 45 minutes we will tell you which a voice AI assistant can close — and what it would take to build one that moves first-call resolution.
Book a 45-min scoping call