Sana AI

Sana AI is a chat based assistant that searches and interacts with a team's own knowledge, including meeting content, positioned as AI agents for every team alongside the Sana Learn platform.

Sana AI is an AI assistant built around a simple premise: the knowledge a team needs to do its work is scattered across documents, messages and meeting recordings, and a good assistant should be able to reach all of it from one chat box. The product is presented as a set of AI agents for every team, sitting alongside Sana Learn, the company's AI-native learning platform. Where a general purpose chatbot starts from a blank slate, Sana AI is positioned to work against the material an organisation already holds. The core interaction is conversational. A user opens a chat, asks a question, and the assistant searches and reasons over the connected knowledge base rather than returning a list of links to read later. Meetings are called out specifically, which matters for teams whose decisions live in conversations rather than in written briefs. That makes the tool most relevant to operations, strategy, sales enablement and internal knowledge management, plus anyone who spends a meaningful part of the week reconstructing what was agreed and why. In practice the workflow is close to a research assistant rather than a content generator. A question goes in, the agent searches across sources, and the answer comes back in chat where it can be followed up conversationally. The site frames this as interacting with all your knowledge, which suggests answers are grounded in the customer's own material rather than the open web alone. Because the public pages are thin, details such as which connectors exist, how permissions are enforced and how meeting capture is configured should be verified directly with the vendor before committing a team. The competitive set is crowded: enterprise search tools, meeting note takers and general assistants all claim a slice of this problem. Sana AI's differentiator is packaging chat, search and meetings as one agent layer while bundling a learning product for the same buyer. Buyers weighing it should test retrieval quality on their own messy corpus first, since that, more than the interface, determines whether the assistant earns daily use.