AI Voice Agent Development

The model is the easy part. The engineering lives in the seams. Streaming ASR has to emit partial transcripts fast enough that the LLM starts thinking before the caller finishes speaking. Endpointing has to tell a thinking pause apart from a finished turn, because getting it wrong means your agent either interrupts people or leaves three seconds of dead air. Function calls into your CRM, calendar or ERP must return inside the same budget, which usually means caching, prefetching and filler speech rather than blocking on a slow internal API.

Then there is the unglamorous work that decides whether the project ships: DTMF handling, warm transfer to a human with context intact, voicemail and IVR-tree detection on outbound, retry logic when a carrier drops the leg, and call recording your compliance team will actually sign off on. We scope these explicitly in week one, because surfacing them late is what turns a two-week pilot into a six-month one.

• Target 600-900ms end-of-speech to first audio; we publish the measured distribution, not the best-case number
• Barge-in handling so callers can interrupt mid-sentence and the agent stops talking and re-plans
• Warm transfer with a spoken summary and full transcript handed to the human agent
• Graceful failure paths: carrier drop, silent caller, hostile caller, and a hard fallback to a human queue

Platforms like Vapi and Retell support 30+ languages out of the box, but out of the box is not the same as production-ready on Indian telephony. Off-the-shelf ASR degrades on regional accents, on 8kHz narrowband phone audio, and on the mixed Hindi-English that most Indian business calls actually use. So we treat every language as its own workstream. We assemble a test set of real recorded calls from your own line, measure word error rate per language and per accent region, and switch ASR vendors where the numbers justify it.

Deepgram, Sarvam, ElevenLabs and Google STT do not rank the same across Marathi, Tamil and Bengali, and vendor marketing will not tell you which wins on your audio. Pronunciation is a separate problem. Indian names, place names, product SKUs and amounts in lakhs and crores need lexicon and phoneme tuning, or the agent sounds foreign and callers disengage. We keep a per-client lexicon and regression-test it on every prompt change.

• Per-language WER benchmarking on your recorded calls before we commit to a vendor
• Code-switching support so a caller can move between Hindi and English mid-sentence
• Language detection and switching on the first turn, or routing by number and region
• Tuned lexicons for Indian names, addresses, SKUs and lakh/crore number formats

An AI calling agent wins on speed-to-lead. It dials a new enquiry within seconds of form submission, asks your qualification questions in the caller's language, writes structured fields back to the CRM, and books directly into a rep's calendar when the lead is real. Leads that do not qualify never consume selling time, and because every call is transcribed and scored, your objection data stops living in individual reps' heads. Inbound is the mirror image: an AI phone agent that answers at 9pm and on Sunday, checks live availability, books, reschedules and confirms without a queue.

No-show reduction is the least glamorous and frequently the highest return. A reminder call two days out, with the option to reschedule by voice on that same call, recovers slots that an unanswered SMS does not. We do not automate the whole funnel on day one. We pick the call type with the clearest success metric, ship it, and expand from a working baseline.

• Outbound qualification with CRM write-back, lead scoring and calendar booking in one call
• 24/7 inbound reception with live availability checks, rescheduling and confirmation
• Reminder and reactivation campaigns with voice-native rescheduling to reduce no-shows
• Every call transcribed, tagged and scored so objections and drop-off points become data

Voice systems carry real exposure. Calls contain personal data and sometimes payment details. An agent with function-calling access can write to your production CRM, cancel a booking or trigger a refund. We design permission boundaries the way we designed smart contract access control: least privilege per tool, hard limits on irreversible actions, explicit human confirmation for anything that moves money, and an immutable audit trail linking every action back to the exact turn of the exact call that caused it.

The same discipline covers prompt injection. A caller who talks your agent into a discount or into revealing another customer's details is a real attack class, not a thought experiment, so we red-team every agent with adversarial call scripts before it goes live. On data residency, we can keep recordings and transcripts inside Indian infrastructure where DPDP obligations or internal policy require it, including self-hosted STT and TTS when a managed vendor is not acceptable to your security team.

• Least-privilege tool scopes with hard caps on refunds, cancellations and data reads
• Immutable, call-linked audit log for every function the agent invokes
• Adversarial red-teaming for prompt injection and social engineering before launch
• DPDP-aware data handling, configurable retention, and self-hosted speech stacks on request