Voice AI
Voice AI development places and answers real phone calls with a streaming speech pipeline, including Hindi and Hinglish, and writes the outcome back to your CRM. WhizCloud usually ships a first production calling workflow in about four weeks for teams that qualify leads or run outbound campaigns.
By WhizCloud · Last updated 22 October 2026
Why do outbound calling teams struggle to scale?
- High-volume outbound calling for lead qualification and reminders is expensive to staff, and coverage is hard to scale predictably as call volume changes week to week.
- Teams in non-English or mixed-language markets face lower quality from off-the-shelf voice tools, especially when conversations switch between languages in the same call.
- Many demos do not hold up in production because latency is too high for natural dialogue or because the system cannot reliably place and manage concurrent calls through compliant telephony providers.
How do we run a voice agent on a real phone line?
We build voice agent systems as production pipelines rather than single-model demos. Telephony integration handles real inbound and outbound calls; speech-to-text is selected and tuned for the target language profile, including code-mixed Hindi and Hinglish where needed; LLM logic manages conversation flow and qualification state; and text-to-speech returns responses that sound natural within strict latency budgets. The architecture is streaming end to end so partial transcripts and responses are processed continuously instead of waiting for full request-response cycles. We include orchestration primitives for retries, scheduling, and concurrent call queueing, plus monitoring dashboards for outcomes and transcript review. This is the pattern we have used for outbound real-estate lead qualification, where operational reliability and language handling determine conversion quality. The result is a system that can run at scale with measurable call outcomes, not an isolated proof-of-concept call bot.
Architecture
Stack selected for this delivery pattern
What did the real-estate qualification proof of concept show?
We built an outbound voice agent for real-estate lead qualification that holds Hindi and Hinglish calls. Calls start through a telephony provider, audio is transcribed as the lead speaks, and the agent follows a defined qualification flow before speaking a reply. When a lead meets the handoff rules, budget, location, BHK, and timeline are written back to the CRM with the transcript. Queueing and retries run through Redis and Arq so many calls can stay in progress together. Staff then work the qualified conversations. The published result of that proof of concept is a coherent Hindi or Hinglish call and those four fields written into the CRM.
What should you know before commissioning a voice agent?
Yes — we've built this specifically for Hindi/Hinglish (code-mixed) conversations; the architecture is language-agnostic given the right STT/TTS providers.
The pipeline is architected for low round-trip latency (streaming STT/LLM/TTS) specifically because latency is what makes voice AI feel robotic — we optimize for this, not just for demo quality.
Yes. Call outcomes, transcripts, and qualification fields are written back to your CRM or system of record through an API.
Most engagements begin within one to two weeks. The published median time to a first production release is about four weeks for one calling workflow, including telephony, the qualification script, and the CRM writeback.
A production telephony provider, with Exotel used on the real-estate qualification build. Speech-to-text, the conversation model, and text-to-speech are separate services so a provider can be swapped.
Budget, location, BHK, and timeline, plus the transcript, after a Hindi or Hinglish qualification call. Consent and suppression checks run before a call is placed.
A senior-led WhizCloud squad. The company is 75+ engineers in Noida, and the first release is one campaign workflow rather than a full contact-centre replacement.