Contextual Voice AI for EdTech at Scale: JK Shah Classes
TL;DR: JK Shah Classes - one of India's leading CA / CS / CMA coaching institutes ran 30 outbound voice campaigns through Alchemyst AI's Voice OS, covering career guidance, parent-teacher outreach, course enrollment, feedback collection, and multilingual regional re-engagement. Across tens of thousands of calls in six Indian languages, the AI voice agents achieved connection rates roughly 2x the best-in-class AI-outbound benchmark and meaningful-conversation rates that outpaced typical EdTech organic conversion by an order of magnitude.
The Challenge: Multilingual EdTech Outbound at Scale
EdTech outbound calling in India hits a wall that generic dialers cannot solve. JK Shah Classes needed voice agents fluent in Hindi, English, Gujarati, Kannada, Telugu, and Marathi. A Gujarati parent expects a Gujarati conversation, and the call is lost in the first ten seconds otherwise. The institute also ran several distinct conversational use cases (career guidance, parent-teacher follow-ups, course enrollment, feedback collection, and re-engagement of leads who had previously been contacted), each with its own opening, tone, and decision logic. A single script does not work, and manual dialing across the lead base would have required a large, expensive team.
The Alchemyst Solution: Context-Aware Voice OS
Alchemyst's Voice OS sits on top of Indian telephony infrastructure and draws on the Context Engine to deliver personalized, multilingual conversations at scale. When the agent dials a lead, it does not work from a flat script. It operates with a live, filtered view of the lead's history, the campaign's objective, the language preference, and the prior interaction trail. The agent calling a Gujarati parent about a PTM event operates with a different context set than the agent calling a Delhi-based CA student about exam preparation.
Six languages handled natively (Hindi, English, Gujarati, Kannada, Telugu, Marathi), with regional phrasing, not English scripts dubbed through TTS.
Per-campaign behavior tuning so that career guidance, PTM, enrollment, feedback, and retargeting campaigns each follow their own conversational logic.
Persistent context across attempts. Retargeted leads are not called from zero.
TRAI-compliant caller ID, time-window enforcement, and complete audit trails.
Campaign Results Across Six Languages
30 campaigns ran across six languages, covering career guidance, parent-teacher outreach, professional certification enrollment, regional language outreach, retargeting, and feedback collection. Aggregate performance sat well above published industry benchmarks for both traditional and AI-enhanced outbound.
38.7% aggregate connection rate against an 8–15% traditional industry baseline and a 20–25% AI-enhanced benchmark, roughly 2x best-in-class AI.
21.3% meaningful-conversation rate (calls sustained beyond one minute), versus the 2–3% range typical of EdTech organic lead conversion.
Best-performing cohort
a Gujarat retargeting wave hit a 57.3% connection rate and 28%+ meaningful-conversation rate.
Highest success rate observed in a regional Telugu cohort, well above the cross-campaign average.
Retargeting consistently beat cold outreach on connection rate. Context accumulates signal over time.
Why Context Engineering Matters for Voice
Most voice AI platforms treat each call as a blank slate. Alchemyst's voice agent operates on a context layer that persists across interactions. When the system retargets a Gujarat-based CA student who previously rejected an enrollment offer, the next call already references the right course, the prior objection, and the language preference, so the agent doesn't start from zero. For EdTech specifically, where the student lifecycle spans career guidance, enrollment, PTM events, exam prep, and feedback, an AI voice system that carries context across interactions builds a compounding advantage over one that treats each campaign as isolated.
“Working with Agentyic completely transformed our operations. We had tried standard AI chatbots before, but they always failed because they lacked memory and context. They engineered a system that actually understands our business logic. The ROI was apparent within the first 30 days.”
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