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Anish Korat

How chatbots work · Vatdi team

Anish explains how retrieval-based chat works in plain words: what the assistant reads, why it can say "I don't know", how languages are detected, how to test accuracy, and where it still fails. He cites the research and vendor documentation rather than borrowed statistics.

Checklist contrasting when to pick a no-code chatbot builder and when to pick an AI trained on your own content
Chatbot fundamentals
64 views

A no-code chatbot builder lets you draw the conversation: menus, branches, forms. An AI trained on your own data reads your content and answers whatever is asked. They solve different problems, and picking the wrong one means either weeks of flow-building or a bot that wanders. This guide shows what each is good at, the real effort to build and maintain each, prices as of September 2026, and a decision list.

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Decision flow between an AI chatbot and live chat: content and order questions to the bot, judgement calls to a person
Chatbot fundamentals
103 views

Chatbot vs live chat is a false choice for most stores: the questions with an answer in your content or order data belong to a retrieval-based chatbot, at any hour, and the ones needing judgement belong to a person. This guide compares the two on availability, cost unit, answer quality and exceptions, and sets out the handover rules and pricing that make running both practical.

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Large statistic tile: 39.6 percent of store chat conversations started outside 9 to 6 UTC, from 884 real conversations
Chatbot fundamentals
129 views

Most ecommerce chatbot statistics online cannot be traced to a dataset. These can: 884 real conversations across 63 stores on Vatdi over 90 days, eval traffic excluded, with the method stated. They show when shoppers ask, what they ask about, how long conversations run, how often a person is needed, where shoppers are, and how often the answers were good, plus the one external figure we consider sourced.

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Flow of a store chatbot: question, retrieval from catalogue and policies, answer with product cards, grade, content fix
Chatbot fundamentals
279 views

Nine techniques that move a store chatbot from "answers something" to "answers correctly": grounding every reply in retrieved content, structuring the catalogue so attributes are findable, writing policy pages a retriever can use, steering with quick replies, handover rules, per-language testing, coupon conditions, guardrails on prices and promises, and a 30-question quality loop you re-run after every content change.

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Pipeline of a RAG chatbot: question, retrieval over your content, generation from the passages, answer or a decline
Chatbot fundamentals
768 views

A RAG chatbot answers in two steps: it retrieves the passages of your own content that match the question, then writes an answer from those passages only, declining when nothing matches. This guide walks one question through both steps, explains chunks, embeddings and keyword search in plain words, lists the ways retrieval still fails on a real store and the content fix for each, and shows how to test it.

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Chat widget answering a French shipping question and a Portuguese follow-up in each visitor's language
Chatbot fundamentals
655 views

A multilingual AI chatbot does four separate things: detects the language of each message, retrieves the answer from content that may be written in another language, writes the reply in the visitor's language, and switches the widget's own labels. This guide explains how each step works, where it breaks (short messages, product names, mixed-language chats), what you configure, and how to test it before launch.

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