MailsRAG is built for teams that handle repetitive support and sales email but do not want raw model output going straight to customers. It combines knowledge retrieval, AI drafting, and human review so teams can automate email in a more reliable way. The best fit is support and sales teams with existing docs, FAQs, policy pages, or internal knowledge that can ground replies.
AI email automation, customer support, sales automation, knowledge base, bilingual support
AI email automation, customer support, sales automation, knowledge base, bilingual support
4.8
6 ratings
Mailsrag
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15Mailsrag
MailsRAG is built for teams that handle repetitive support and sales email but do not want raw model output going straight to customers. It combines knowledge retrieval, AI drafting, and human review so teams can automate email in a more reliable way. The best fit is support and sales teams with existing docs, FAQs, policy pages, or internal knowledge that can ground replies.
first to comment, great product
Mailsrag
Thank you for your support. We hope you can provide suggestions for improving our product
Nice execution on a real pain point. Review-first by default is the right call for LLM-drafted customer replies, to build trust before going full-auto. RAG-grounded answers + multilingual support should keep quality consistent across agents. 30 seconds vs. 3-5 minutes per email is a strong number if it holds up. Curious how it handles questions outside the KB, is it by human handoff?
Mailsrag
Our system first reviews the content of the email, and each customer can upload their own knowledge file for EMBEDDING, building a private vector knowledge base, and generating targeted preliminary answers based on the email content. LLM will polish the preliminary answers. We also have a basic knowledge base that can meet general email reply requirements, but under normal usage, we hope that customers can turn off public KB to prevent it from interfering with the correctness of answers. Each customer is involved in a different professional field, so a private knowledge base becomes very important, which is a prerequisite for ensuring the correctness of answers. We can provide assistance to customers on how to establish a private knowledge base. For issues outside the knowledge base, we improve them by calling LLM. Each email processing needs to automatically go through RAG and LLM, so it takes some time and token. LLM itself is a universal knowledge base, and there may be fictitious answers, so it is best to conduct manual review before the official release of the email. After the system is improved and new knowledge bases are added, automatic sending can be achieved. We hope this system can help everyone improve office efficiency.
Keeping a human review step between the draft and the customer is the part most email automation skips. Does the retrieval pull from live docs or a snapshot?
Mailsrag
After receiving the email, the system will first vectorize the email content, and then retrieve the corresponding content fragments from the vectorized private knowledge base to form a preliminary draft. The preliminary draft will then be sent to LLM to form a draft, which will be manually reviewed. Therefore, the system processes all information in real-time email documents, not extracts it from snapshots.
MailsRAG strikes a great balance between AI automation and human oversight. Grounding responses in trusted company knowledge while keeping a review step makes it much more practical for support and sales teams that need both speed and accuracy.
Mailsrag
This is what our team hopes for. Currently, there are many teams working in this area. We hope to establish our own private knowledge base, automatically connect to emails, and automate replies to make it more convenient. We hope you can provide suggestions and continuously improve our products.
Great idea, the utilisation are really good, i will use it
Mailsrag
Thank you for your support. We hope you can provide suggestions for improving our product
great use of automation. really helpful
Mailsrag
Thank you for your support. We hope you can provide suggestions for improving our product
Mailsrag
Users may receive customer emails in various languages, and the system supports all languages, including very small language families, to ensure consistency between the reply language and the email source language. This feature is already technically easy to implement and brings great convenience to users
MailsRAG Your product has strong potential, but I found a few key improvements that could make it even better. I'd love to share my feedback and suggestions—please contact me at [email protected]