An example use case showing the shape and economics of a typical Big Helpers AI chatbot build — RAG over real documents, Hindi + English, with strict guardrails so it never invents discounts.
Note: this is an example use case illustrating a typical Big Helpers engagement of this shape — not a named client. Real client names and numbers are kept private.
An education and coaching institute with 11 centres across two North Indian states was processing roughly 1,400 WhatsApp queries per day across admission enquiries, fee status, batch timing, exam result lookups, and refund questions. A 6-person support team worked split shifts including weekends. Median response time to a parent's WhatsApp message was about 4 hours during the day and 14 hours overnight. Parents complained on Google reviews. The institute had previously bought a no-code WhatsApp chatbot which answered course details correctly but invented a 20% scholarship that didn't exist — leading to about 40 angry parents at one centre demanding the discount they had been promised by the bot.
Three problems compounded: (1) volume was rising faster than the institute could hire support staff trained on the institute's specific course catalogue and policies; (2) the previous bot's hallucination had eroded trust to the point where the institute's owner banned chatbots internally — but the support load made some form of automation unavoidable; (3) parents typed in mixed Hindi-English (Hinglish), often as voice notes, and English-only systems failed at the first message. Any new system had to handle Hinglish + voice notes, refuse to discuss anything outside the official course catalogue and fee policy, and hand over cleanly to a human when needed.
Send us 50 of your typical customer messages and a link to your FAQ or policy doc. We send back a 1-page sketch of what the bot would handle, what it would escalate, and what it would cost. No sales call required.
We built a custom RAG-backed WhatsApp chatbot in 7 weeks. We indexed 380 pages of the institute's official course catalogue, fee structure, refund policy, batch schedules, and exam-pattern documents into a Postgres pgvector index. The bot was built on Anthropic Claude Sonnet via API, with a strict system prompt that limited responses to indexed-document content. Function calls were added for fee-status lookup (against the institute's existing ERP), batch-timing lookup, and exam-result lookup by enrolment number. Voice notes were transcribed via Sarvam-Hi-ASR (better Hindi than Whisper at the time of build). Languages: Hindi + English with full Hinglish handling. Two strict guardrails: (a) any response containing a price, discount, or commitment was passed through a final safety classifier; (b) a one-tap human-handover button was always visible in the conversation. WhatsApp delivery via AiSensy (Meta-approved BSP). DPDP-compliant: explicit opt-in capture for marketing messages, easy unsubscribe, conversation retention capped at 90 days.
Within 60 days of launch, the bot was handling roughly 72% of total inbound WhatsApp queries end-to-end. Median response time dropped from about 4 hours to under 30 seconds. The 6-person support team was reassigned to outbound counselling calls — a higher-value activity — without layoffs. Hallucination incidents dropped to zero in the first 90 days because the bot was strictly RAG-bound and the safety classifier blocked any unverified price commitment. Estimated monthly running cost: about ₹38,000 (LLM ₹22K + WhatsApp messaging ₹12K + hosting ₹4K). Estimated saved support hours: roughly 480 staff-hours per month, equivalent to 3 full-time support agents at the institute's local salary scale. Payback on the ₹2.8 lakh build was inside 5 months.
It's an example use case representative of typical Big Helpers AI chatbot builds for Indian education and coaching institutes. Specific client names and exact numbers are kept private. The shape, technical choices, and rough economics are accurate to real engagements of this size.
Yes. Single-centre or single-product builds drop to ₹80K-1.4L because the document corpus is smaller and there's only one ERP to integrate. The economics still work as long as you have at least 100-200 queries per day; below that, a tuned canned-reply WhatsApp Business app may be cheaper.
That's exactly why the safety classifier and the hard refusal-to-go-out-of-scope rules matter. The bot is contractually a Data Processor, not a decision maker; it cannot grant discounts, scholarships, or refunds. Any commitment is escalated to a human and logged. We documented this in the institute's terms of use page that parents see at first contact.
In our internal testing at the time, Claude Sonnet handled Hinglish refusal-with-empathy better than GPT-4o, and was more reliable at staying inside RAG documents. The institute's owner could test both and chose Claude. The architecture is LLM-agnostic — switching to a future model is a config change, not a rebuild.
Asynchronous architecture, queued message processing, and pre-warmed connection pool. We load-tested at 5,000 concurrent conversations before go-live. On the actual exam-result day in cycle 1, the bot handled about 11,000 queries in a 4-hour window without any human escalation backlog.
Explicit opt-in capture before any non-transactional message. Conversations stored on the institute's own AWS account in ap-south-1. 90-day retention by default, configurable. Parents can request deletion via a documented endpoint. We signed a DPDP-aligned data processing agreement with the institute, and the institute did the same with parents in their privacy notice.
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