We build custom AI chatbots for Indian businesses — for WhatsApp, your website, or inside your app. Trained on your real documents (RAG), multilingual, with guardrails so they don't make things up.
A useful AI chatbot for an Indian business is not a generic ChatGPT widget. It is a chatbot trained on your product catalogue, FAQ, policy docs and order data, that speaks the languages your customers actually type in (Hindi, Tamil, Bengali, Marathi, Telugu — not just English), and that knows when to escalate to a human. Big Helpers builds these for ₹60,000 to ₹5,00,000, live in 4-10 weeks.
You bought a no-code bot. It cheerfully invents prices, return policies, and product features that don't exist.
Real Indian queries are typed in mixed Hindi-English, regional script, or voice notes. Generic bots fail on the first message.
Your bot lives on a website widget no one uses. The 200 daily WhatsApp queries still go to a human at ₹15-25 each to answer.
It can chat about anything except your products, policies, and orders — exactly the things customers ask about.
When the bot is stuck, the user is stuck. No clean handover to a human, no record of what was already discussed.
Customer complains the bot promised a refund. You have no log of what was said, when, or why.
Most queries are about order status, return policy, sizing, availability. Perfect chatbot territory.
Course details, fee structure, batch timing, admission process — repeated 1,000 times a week to the same support team.
Bot qualifies the lead (budget, location, intent, timeline) before a sales rep ever touches it. Saves 60-70% of rep time.
Symptom intake, document checklist, appointment booking — before a doctor or lawyer is engaged.
Citizen FAQ in Hindi + English, scheme eligibility checker, document checklist for an application — with strict guardrails so the bot only says what's in the official document.
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 index your product catalogue, FAQs, policy PDFs, manuals, and past tickets. The bot answers from those documents — not from the internet, not from imagination.
Bot lives where your customers type. Meta-approved templates for proactive messages, full inbound conversation handling. Works for above-256-contact lists.
Detects the language of the incoming message (including Hinglish, Tanglish), responds in the same. We test against real Indian phrasings, not academic translations.
Check order status, book appointment, generate invoice link, raise complaint ticket, look up dealer pricing — bot calls your APIs and returns the answer.
Bot refuses to answer outside its trained scope. Returns 'I don't have that information, let me connect you to a human' instead of inventing answers.
When the bot escalates, a human agent gets the full conversation context, suggested response, and a one-tap takeover. No customer repeats themselves.
Every message in, every message out, every document the bot used to answer — searchable, exportable, retained per your DPDP retention rule.
Daily volume, top intents, escalation rate, languages used, satisfaction rating. Built to your specific KPIs, not generic chatbot vanity metrics.
We pull 30 days of your real customer queries (WhatsApp + email + ticket logs). Categorise them. Identify which 10-15 intents cover 70% of volume — those are the bot's day-1 scope.
List every document the bot needs to answer from. Surface gaps (we always find some — a price list that's 3 months old, a policy doc no one updated since 2024). You fix the source documents; the bot stays accurate.
Backend in Python (FastAPI), vector database (Postgres pgvector or Qdrant), LLM via Anthropic / OpenAI / Sarvam (for Indic languages). WhatsApp via Meta's Cloud API or Gupshup/AiSensy. Weekly demo.
We try to break it. Trick it into hallucinating, into giving discounts it shouldn't, into leaking data. Patch every gap before customers see it.
Soft launch to 10-20% of inbound traffic. Daily review of every escalation. Tune scope and tone.
Open to 100% traffic with human-handover safety net. We watch the dashboard daily for 30 days, ship tweaks weekly. After that you own it.
Indicative range: ₹60,000 — ₹500,000 (excl. GST). Final estimate after a free 30-min scoping call.
Pvt Ltd since 2008. We're not an AI bandwagon shop — we've shipped production AI chatbots in healthcare, legal, distribution, and citizen-facing portals.
Hinglish, voice notes, regional scripts, typos, code-mix. Our test suite uses actual customer messages from real businesses, not lab translations.
Source code in your GitHub. Vector index on your infrastructure. Prompts in version control. No vendor-lock LLM platform.
Most clients start on Claude or GPT-4o. We architect so you can swap to Sarvam, open-source Llama, or future models without rebuilding. Costs drop 30-60% over the bot's life.
Some queries should never be answered by a bot (medical diagnosis, legal advice on complex disputes, refunds above ₹5,000). We design the escalation rules with you, not around you.
Consent capture, minimal data retention, on-shore vector database, breach process, data principal rights — built in, not bolted on.
Note: illustrative example — not a specific client engagement.
A coaching institute in Lucknow with 11 centres was handling roughly 1,400 WhatsApp queries per day across admission enquiries, fee status, batch timing, and exam result questions. A 6-person support team worked weekends to keep up, and parents complained about response delay (median 4 hours). The institute had tried a no-code chatbot the previous year — it answered course details correctly but invented a discount that didn't exist, leading to 40+ angry parents at a single branch.
We built a custom RAG-backed WhatsApp chatbot in 7 weeks. Indexed 380 pages of official course catalogue, fee structure, refund policy, and FAQ. Languages: Hindi + English (Hinglish handled). Function calls: fee status lookup (against the institute's existing ERP), batch timing lookup, exam result lookup by enrolment number. Strict guardrails: bot refuses to discuss discounts or admissions criteria not in the source documents, with a one-tap human handover button. Built on Claude Sonnet via Anthropic API, vector index on Postgres pgvector, WhatsApp via AiSensy.
Within 60 days the bot was handling about 72% of total inbound queries end-to-end. Median response time dropped from 4 hours to under 30 seconds. The 6-person support team was reassigned to outbound counselling calls (a higher-value activity) without layoffs. Estimated monthly LLM + WhatsApp cost: ₹38,000. Estimated saved support hours: roughly 480 staff-hours per month. Build cost: ₹2.8L one-time. Payback inside 5 months.
Yes. We support Hindi, Tamil, Bengali, Marathi, Telugu, Gujarati, Kannada, and Punjabi — including code-mixed forms like Hinglish and Tanglish. Modern LLMs (Claude 4.x, GPT-4o, Sarvam-2) handle these well. We test against real customer messages from your business, not academic translations.
Three layers: (1) RAG — the bot answers from your indexed documents, not from the LLM's general knowledge; (2) explicit prompt instruction to refuse out-of-scope queries; (3) a final safety classifier that flags any answer with a price, discount, or commitment for review. If the bot is uncertain it says so and offers human handover.
Three components: LLM (typical: ₹15-50K/month for 5-15K conversations), WhatsApp Business API (₹0.40-1.20 per message in India, depending on category), and hosting (₹3-8K/month). For a typical 10K conversation/month deployment, expect ₹35-90K/month total. We size this in discovery so you know before you commit.
Yes. Standard integrations into Freshdesk, Zoho Desk, Zendesk, and Help Scout — when the bot escalates, a human-friendly ticket appears in your existing helpdesk with full conversation context. Custom integrations for in-house helpdesks add 1-2 weeks.
Yes — supported. We auto-transcribe inbound voice notes (Whisper or Sarvam-Hi-ASR for Indic), respond in text, and optionally send a voice reply via TTS. Voice handling adds about ₹0.30-0.60 per minute of audio in infrastructure cost.
Yes — same backend, different channel. We add a website chat widget (or embed in your existing app) with the same RAG, same guardrails, same handover. Multi-channel deployments cost 20-30% more than single-channel.
Conversations are stored on your infrastructure with the retention period you choose (default 90 days, configurable). Vector index lives on-shore. Customer can request data export or deletion via a documented endpoint. We sign a DPDP-aligned data-processing agreement before any build.
Yes — that's a deliberate design choice. The bot's prompt, RAG layer, and integrations are LLM-agnostic. Swapping Claude for GPT-4o (or for a self-hosted Llama) is a config change, not a rebuild. We've helped clients cut LLM cost 40-60% by switching providers as the market moved.
Talk to a senior engineer in 24 hours — no juniors, no sales reps, no jargon. Just a clear scope, an honest estimate, and a build plan.