Stop letting support agents dig through hundred-page manuals to answer the same questions twice. Deploy a WhatsApp chatbot that indexes your documentation once, then answers any query, text, voice, image, or PDF, with accurate, source-grounded responses.
Documentation is only useful if someone can find the right line fast, and agents rarely can. The result is slow, inconsistent answers. This template fixes the root cause. It ingests your product docs, chunks and embeds them into a searchable vector store, then uses retrieval-augmented generation to answer WhatsApp queries from that knowledge base, pulling the exact context each question needs.
A knowledge base is only as good as how it’s indexed. This template imports your product documentation from Google Docs, splits long files into searchable chunks, generates vector embeddings with OpenAI, and stores them in a MongoDB Atlas vector store. Run it once and your entire manual becomes instantly searchable by meaning, not just keywords.
Real users don’t always type. This workflow listens for incoming WhatsApp messages in any form, plain text, voice notes transcribed to text, images analyzed for context, or PDFs and files parsed for content, and routes each to the right processing path. However the question arrives, the engine turns it into something it can actually answer.
A confident wrong answer is worse than none. The engine converts each query into embeddings, runs a similarity search against your vector store, and feeds the matched context to GPT-4o-mini for a concise, source-grounded reply. A memory buffer holds context across turns, so follow-up questions stay coherent instead of starting from scratch.
A plug-and-play blueprint to map, trigger, and automate the workflows leaking your team’s hours. Free. Yours in one click.