End-to-end autonomous
Places outbound calls or answers inbound. Handles the full conversation without any human on standby.
AI voice agents · Kanpur, Uttar Pradesh
Autonomous AI voice agents for Kanpur teams.
AI voice agents that place or answer calls end-to-end — no human agent required for the routine 80%. Speaks Hindi, English, and regional Indian languages. Warm-transfers to a human when the conversation needs one.
Why Kanpur teams pick dialque
Built for Kanpur, not adapted to it.
One of India's oldest industrial cities and the country's leather-export capital — Jajmau tanneries alone account for roughly a quarter of national leather output — layered with a Hindi-first manufacturing SME base and a small but sharp IIT-Kanpur alumni fintech and agri-tech ecosystem.
Business districts we serve
- Civil Lines
- Swaroop Nagar
- Panki Industrial Area
- Kalpi Road
- Jajmau
- Naveen Market
Industries in Kanpur
- Leather manufacturing
- Textiles
- BFSI
- Education
- Agri-tech
What Kanpur teams use dialque for
Payment-follow-up nudges for Panki textile suppliers, EMI reminders and PTP capture for agri-NBFCs lending into eastern UP, appointment desks for Civil Lines healthcare chains, and inbound triage for D2C brands serving the Kanpur-Lucknow-Varanasi consumer belt — the routine Hindi conversations that would otherwise burn tele-caller hours on a Swaroop Nagar floor.
What you get
Everything a Kanpur contact centre needs, in one platform.
Hindi + regional languages
Auto-detects the language from the caller's first utterance. Handles Hinglish + code-mixing natively.
Warm handoff to humans
When the caller frustrates or asks something out-of-scope, transfers with a real-time conversation summary.
Same TRAI compliance
DLT + NDNC + 9-21 window enforced at the dialer, just like your human-agent campaigns.
Compliant with TRAI, DPDP Act, and every Indian carrier requirement.
DLT template enforcement, NDNC scrub at queue import, 9 AM – 9 PM calling-window guard, TRAI 3% abandon-rate pacing, DPDP-compliant recording storage. Everything a Kanpur leather manufacturing operator would flag in an audit is handled at the platform level, not documented as an add-on.
Languages in Kanpur calls
dialque handles code-mixed conversations natively. For Kanpur calls this typically means English and Hindi— English-only IVRs fail Tier-2 and Tier-3 callers, and AI call summaries are produced in the language the call actually happened in.
Support for Kanpur teams
dialque is a remote-first team serving Kanpur contact centres India-wide. Video demos same day; async support over email + WhatsApp; the product runs the same way whether your team is in Civil Lines or anywhere else in Uttar Pradesh.
FAQ
Kanpur-specific questions we get asked.
- Can the AI voice agent register on the same Airtel or Tata trunk our Panki textile mill already uses, or does the AI layer need its own carrier contract?
- Same trunk. Dialque runs the AI voice agent on whichever Indian SIP trunk your Kanpur textile mill or leather exporter already terminates on — Airtel Business, Tata Communications, Vi Business, Knowlarity, or Plivo — so a Panki or Jajmau back office does not need to sign a second contract for the AI layer. Human and AI legs share the same DIDs, so the customer's caller-ID always shows the same Kanpur 0512 number they'd recognise from a human agent. Media terminates at Indian PoPs so RTT to UP handsets stays low enough for Hindi turn-taking to feel natural — cross-region routing puts TTS out of sync with the caller mid-sentence.
- Does the AI agent cope with the Bhojpuri-influenced Hindi common across the Kanpur-Bihar belt, or does it stumble on regional accents?
- Standard Hindi is handled end-to-end — recognition, LLM reasoning, and TTS — and the model copes well with the Bhojpuri-influenced Hindi common across the Kanpur-Bihar-Jharkhand belt for routine intents like appointment confirmations, EMI reminders, and delivery-status checks. Heavy Bhojpuri dialect on complex intents (dispute resolution, nuanced grievance) still trips STT confidence more often than mainstream Hindi does, and in those cases the agent hands off to a human collector with the Hindi transcript attached so the person picking up does not restart the conversation. For campaigns dialing deep Purvanchal, we typically recommend Hindi-fluent human backstops on the disposition queue rather than leaning on the AI for edge-case comprehension.
- Our agri-lending NBFC pushes early-bucket collections into rural UP and Bihar — will the AI voice agent actually engage borrowers who won't answer an English call?
- Rural UP and Bihar borrowers who won't engage on an English call will pick up a Hindi AI voice agent that opens with a familiar greeting and identifies itself as an automated agent on behalf of your NBFC (which is the mandatory disclosure under TRAI's automated-caller rule anyway). The agent runs early-bucket reminders, captures PTP dates, and escalates disputes or hardship pleas to a human collector on your recovery desk. Most Kanpur agri-lenders see engagement rates on Hindi AI calls close to Hindi human calls on early-bucket work; the delta shows up mostly on 60-90 DPD where borrower objections get more emotionally loaded. Consent capture, DND scrubbing, and RBI Fair Practices Code compliance work the same as on your human dialer.
- How does the AI voice agent honour RBI's Fair Practices Code for a Kanpur recovery desk chasing agri and MSME loans?
- RBI's Fair Practices Code and the recovery-agent Master Direction cap contact hours (typically 08:00-19:00 for recovery calls), prohibit intimidation, and require verifiable agent identification at the start of every call — all three the AI voice agent honours by design because the script is auditable in a way a human agri-loan recovery floor genuinely is not. The agent identifies itself as an automated agent on behalf of your NBFC (satisfying both the RBI disclosure and TRAI's automated-caller rule), captures PTP dates, and warm-transfers hardship, settlement, and dispute conversations to a human collector. Recording, transcript, and disposition logs are retained per whatever RBI-outsourcing audit window your compliance team has committed to — 5 years is the common default for recovery-related evidence. The collections-specific configuration is documented at /virtual-recovery-agent.
- We're an IIT Kanpur alumni fintech running a lean SDR pod out of a Civil Lines co-working space — how quickly can we spin up the AI voice agent, and how API-first is the console?
- An IIT Kanpur alumni fintech running a lean SDR pod out of a Civil Lines or Swaroop Nagar co-working space can spin the AI voice agent up in under a week — SIP trunk connection and DLT header approval are the bottleneck (usually 3-5 working days on your carrier's side), the AI configuration itself is a same-day exercise. The console is API-first so a founding engineer can wire lead-push and disposition webhooks against a Postgres or Firebase backend without waiting on integration support. Prompt and persona tuning is self-serve, and the Hindi + English voice options ship out of the box — no separate voice-vendor contract, no TTS negotiation to run.
- We migrated from Exotel to MyOperator two years ago and we're rethinking again — what does a second cutover look like for a Kanpur outbound-collections desk?
- Two migrations in two years usually means the underlying trunk contract has stayed with the same Tier-1 carrier throughout, which is the easy part — dialque re-points that trunk rather than re-issuing DIDs. Lead lists, dispositions, and script content export from MyOperator via CSV and API; historical recordings can stay in the old system for reference without forced re-upload. For a Kanpur outbound-collections desk, most teams migrate one campaign at a time — start with a low-risk early-bucket agri or MSME recovery flow, validate connect rates and PTP capture against your MyOperator baseline for a week, then move the rest of the book across. Per-conversation cost typically drops materially versus a human-only floor because the AI voice agent absorbs the volume-heavy Hindi calls tele-callers would otherwise chew through.
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