Whismer

Whismer is a web-based platform for building a customized AI question-and-answer assistant. Instead of relying only on a General-purpose chatbot, users supply their own source material so the assistant can respond with answers grounded in that content. The platform is aimed at people who manage large amounts of reference material and want faster retrieval of specific Information.

What it does

At its core, Whismer turns uploaded material into a private knowledge base that an AI assistant can draw on. You add files or web pages, and the system learns from that material to answer questions in a more targeted way. The result is a specialized assistant rather than a generic one, which can be useful when the subject matter is technical, internal, or otherwise not well represented in public data.

Key capabilities

• Document ingestion: Supports multiple file formats, including PDF, DOC, CSV, and TXT, so different kinds of reference material can be consolidated in one place.
• Web page addition: Users can bring online content into the knowledge base by supplying links, which helps capture published guidance or reference pages.
• note editing: Notes can be written and edited to further steer the assistant's answers and fill gaps in the source material.
• Knowledge-base learning: The assistant answers queries based on the uploaded resources rather than general assumptions, improving relevance for domain-specific questions.
• Sharing: A finished AI project can be shared with team members, CLIents, or students so others can query the Same curated knowledge base.

Who it is for

Whismer suits teams and indiViduals who need to organize and query substantial reference collections. A project manager, for example, could load project documents, meeting notes, and research into the platform and then let colleagues ask questions about that material. educators could share a subject-specific assistant with students, while support or consulting teams could give clients access to an assistant built from relevant documentation. It is also a reasonable fit for anyone who wants a Lightweight internal knowledge assistant without building a custom system from scratch.

How to get started

Getting started generally involves creating an account, opening a new AI project, and uploading your documents or adding web links. After the material is ingested, you can add or edit notes to refine coveRAGe, then test the assistant with sample questions. Once the answers look reliable, the project can be shared with the intended audience. Because performance depends heavily on source quality, it helps to start with a focused set of documents and expand from there.

Pricing and access caveat

Whismer is accessible through a web browser and APPears to offer a way to try the service, with paid options likely for broader use. Specific plan details, usage limits, and feature availability may change over time, so check the official site for current pricing before committing. The interface is primarily in Chinese, and availability outside its target region may vary.