How QueueBuster put its outbound prospecting on autopilot with multilingual voice AI SDRs
QueueBuster sells cloud POS and retail-tech to thousands of Indian merchants — from kirana chains to fashion retail and F&B. Their growth team used Caller Digital's voice AI to qualify inbound MQLs, run nurture cadences on cold outbound lists, and book demos directly into the AE calendar — replacing the bottleneck of building and managing a multilingual SDR floor.
A high-velocity SaaS pipeline that needed to call thousands of retailers a week — in 6 languages, before the lead went cold
QueueBuster's product is a cross-vertical play — fashion retailers, kirana stores, restaurants, salons, and pharmacies all sit in the same TAM. That breadth means the inbound MQL queue is large, language-diverse, and time-sensitive: a retailer who fills out a demo form on a Tuesday afternoon expects a callback before Wednesday morning, or they're already in conversation with a competitor.
Building an SDR floor that could match that surface area was painful. Hiring multilingual SDRs (Hindi, Tamil, Telugu, Marathi, Bengali, Gujarati) at the throughput needed pushed cost-of-acquisition past tolerable bounds, and ramp-time on each new SDR was 6–8 weeks before they could run a discovery call cleanly. Meanwhile, the cold-outbound queue — list-buys, event scans, partner referrals — was barely being touched because all SDR capacity was burned on hot inbound.
Speed-to-lead was the visible KPI under stress, but the deeper issue was unit economics. QueueBuster needed a way to triple top-of-funnel conversation volume without tripling SDR headcount.
Speed-to-lead slipping past the golden hour
Most inbound MQLs were called back hours later, sometimes the next day. Conversion correlates strongly with sub-15-minute callback in the retail SaaS segment.
Language coverage gaps in Tier-2/3 India
The strongest demand was coming from regional retailers who preferred Tamil, Marathi or Gujarati — but the SDR floor was largely Hindi/English-led.
Cold outbound completely ignored
Lists from events and partner channels piled up because hot inbound consumed all SDR capacity. Pipeline was over-indexed on inbound and under-indexed on proactive outbound.
Inconsistent BANT qualification
Different SDRs scored the same prospect differently. Demos handed to AEs were a mixed bag — some genuinely qualified, others wasted AE hours that should have been spent closing.
A voice AI SDR layer that calls every MQL, qualifies on a fixed BANT rubric, and books AEs directly into customer calendars
Caller Digital deployed a voice AI agent that sits between QueueBuster's lead capture and their AE team. Every inbound MQL — from website forms, partner referrals, or event scans — is dialled within minutes by the AI in the prospect's preferred language. The agent runs a structured BANT-style discovery (store count, current POS stack, monthly GMV, decision authority, timing), handles common objections, and books a demo into the right AE's calendar based on territory and vertical.
Cold outbound runs on the same engine. Lists are uploaded with metadata (vertical, store count, region, language preference) and the agent works through the queue in parallel — calling, talking, qualifying, and dropping a context-rich summary into the CRM whether or not a demo gets booked. AEs walk into every demo with a written brief: pain point, current stack, store count, and BANT score.
Multi-touch follow-up is automatic. Prospects who don't pick up get retried at intelligent times based on their region's call patterns; prospects who say 'send me information' get a follow-up call after the email lands; prospects who book a demo get a confirmation call the day before.
Sub-15-minute speed-to-lead
Every inbound MQL gets a callback inside the golden hour, in the prospect's preferred language, regardless of submission time-of-day.
Programmatic BANT qualification
Same 12-point rubric on every conversation. Output is structured data the AE sees in CRM before the demo, not a free-text note from a tired SDR.
Live calendar booking
Agent reads AE availability in-call, proposes 2–3 slots based on prospect timezone and AE territory, and confirms the booking with a calendar invite while still on the line.
Multilingual conversation graph
Hindi, Hinglish, Tamil, Telugu, Marathi, Bengali and Gujarati. Different scripts and conversational tone for fashion retail vs F&B vs kirana — same agent.
Cold outbound at parallel scale
Runs hundreds of conversations in parallel through the cold list while inbound flow is also handled — no queue contention, no human SDR burnout.
Multi-touch nurture cadence
Smart retry timing per region. Voicemail-aware. Email + voice handoffs. Drip cadence that maps to the prospect's earlier responses, not a generic schedule.
Top-of-funnel conversation volume up substantially, AE time concentrated on closing, cold outbound finally getting worked
QueueBuster ran the voice AI alongside the existing SDR floor for the first 8 weeks to compare outcomes head-to-head, then expanded coverage to the full inbound and cold-outbound queues.
| Dimension | Before | After |
|---|---|---|
| Inbound MQL callback time | Same-day or next-day | Sub-15 minute |
| Cold outbound queue | Worked sporadically | Worked end-to-end every week |
| BANT scoring | Free-text, SDR-dependent | Structured 12-point rubric |
| Demo brief for AE | Calendar invite + maybe a Slack note | Full conversation summary + transcript + score |
| Language coverage | Hindi + English (mostly) | Hindi, Hinglish, +5 regional |
| Concurrent conversations | Limited by SDR seat count | Hundreds in parallel |
Frequently Asked Questions
It runs a structured BANT-style discovery — store count, current POS, monthly GMV, decision authority, timing, biggest pain — in the prospect's preferred language. It handles common objections, answers basic product/pricing questions, and books a demo directly into the right AE's calendar based on territory and vertical.
Routing rules in QueueBuster's CRM map vertical (fashion / F&B / kirana / pharma / salon) and region to AE territory. The agent reads live calendar availability, proposes 2–3 slots in the prospect's timezone, and confirms the booking with a calendar invite before ending the call.
Hindi, Hinglish, Tamil, Telugu, Marathi, Bengali and Gujarati in production. Language is detected from the first response and the agent code-switches mid-call when the prospect mixes languages, which is common in retail conversations across India.
Cold lists are uploaded with metadata (vertical, store count, region, language preference). The agent works the queue in parallel using region-aware retry timing and voicemail-aware behaviour. Output goes into CRM regardless of whether a demo is booked, so the next sequence (email, retargeting) has full context.
A written conversation summary, the BANT score, key pain points the prospect mentioned, the current POS stack they're running, and a transcript link. Demos start at minute one of value-discussion rather than recap.
It scales it. Human SDRs focus on the higher-touch enterprise segment and the AI handles velocity-tier inbound and cold outbound — meaning the team can run 5–10x more conversations a week without proportionally more headcount.
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