[ Answers from your documents, not from the internet ]
Support and sales assistants grounded in your own policies, catalogue and past tickets — on your site, on WhatsApp, or inside your admin panel. It answers what it knows, hands over what it does not, and never invents a price.
Support and sales assistants grounded in your own policies, catalogue and past tickets — on your site, on WhatsApp, or inside your admin panel. It answers what it knows, hands over what it does not, and never invents a price.
Send us your last thousand support ticketsAnswers are retrieved from your policies, FAQs and past tickets, with the source shown. A confident wrong answer about a refund is worse than no bot at all.
Angry, complex or high-value conversations go to a person with the full transcript attached, rather than looping until the customer gives up.
WhatsApp first for Indian audiences, plus the site widget. One brain, several front doors, one conversation history.
We report what share of conversations ended without a human, and what share of those the customer came back about. The second number is the one that matters.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Handles the repetitive third of the queue — order status, policies, how-to, documentation — and drafts replies for the rest so the same team clears more.
The channel most Indian customers actually use. Official Business API, template messages, media, and the same knowledge base as the site widget.
Answers pre-sales questions, qualifies the enquiry against your criteria, and books the call — writing the whole conversation into your CRM as it goes.
What people actually ask, in their own words, ranked by volume. This decides what the bot must handle and what it should not attempt.
Policies, FAQs, catalogue and past resolutions indexed and chunked. Where the answer does not exist anywhere, we flag it — that is a content gap, not a bot problem.
Retrieval over your content, with rules about what it may never state on its own: prices, dates, medical or legal advice, anything contractual.
Triggers for escalation, what the agent sees when it lands, and what happens outside working hours. Most bad chatbot experiences are handover failures.
The bot is scored on a held-out set of real tickets before launch, including the awkward ones. Anything below the bar goes to a human by default.
Weekly review of unanswered and escalated conversations, feeding the knowledge base and the refusal rules. A chatbot is a product, not a launch.
The chatbots people hate have one thing in common: they were built from a decision tree someone imagined, not from the questions customers actually send. We start at the other end — with your ticket history — because that tells you what the bot must handle, and just as importantly what it should refuse to attempt.
The second thing that separates a useful assistant from an obstacle is the handover. A bot that escalates cleanly, with the transcript attached and no repetition, is forgiven for what it cannot answer. One that loops is not, however good its answers were.
Every answer is pulled from your own content and can be traced back to it. The model phrases; your documents decide.
Prices, contractual terms and anything regulated go to a person. Knowing what not to answer is most of what makes the rest trustworthy.
Conversations closed without a human matter only if the customer did not come back the next day. We report both.
Mapping hundreds of intents by hand has given way to indexing your content and letting the model find the passage. Faster to build, and it degrades gracefully on questions nobody predicted.
For most consumer businesses here the site widget is now the secondary surface. Building WhatsApp-first changes the design — message templates, session windows, and no room for a long menu.
Anything that reads user input and can act needs defences and a log. We test for it before launch rather than after the screenshot goes around.
Latency and accent handling in Indian English have improved sharply, but for anything transactional the text channel is still where the reliability is.
A grounded support assistant typically runs ₹2.5–7 lakh to build, depending on how many systems it looks up (orders, accounts, CRM) and whether WhatsApp is included. Running costs are the model calls plus WhatsApp conversation charges — usually a few thousand rupees a month at moderate volume. Techtaru Digital projects both before the build starts.
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