Glean vs Talk to Books

Glean connects your company's apps, documents, and permissions into one enterprise search and Work AI platform. Employees ask questions in natural language, get answers grounded in Slack, Google Drive, Jira, Confluence, Salesforce, and hundreds of other sources, then automate follow-up work with assistants and agents. The system respects existing access controls so people only see what they are already allowed to view.

Generic AI chat tools send broad prompts to models without your org chart, ticket history, or doc permissions baked in. Glean indexes enterprise context once, maps relationships in an Enterprise Graph, and claims a 30% reduction in token usage compared with off-the-shelf MCP tools because less irrelevant text reaches the model. That matters when every department is spinning up agents on the same data.

IT, engineering, sales, support, and HR teams at companies like Booking.com, Zillow, and Samsung use Glean to find answers faster, build no-code agents, and cut internal support tickets. Deployment is demo-led: you request access through Glean's sales flow rather than self-serve signup.

Talk to Books was a Google Research experiment that let you ask natural-language questions and get answers pulled from passages across a large collection of books. You typed a question or statement, and the system returned relevant book excerpts instead of standard web search results. Google has shut the service down, but the site still hosts an archive of sample queries.

The project launched in 2018 as part of Google's Semantic Experiences initiative. It used neural language models for semantic matching and retrieval, showing how conversational queries could surface ideas buried in published books rather than blog posts or forums.

Researchers, students, and curious readers used Talk to Books to explore topics through book-length context. The experiment informed later semantic search features inside Google products such as Shopping, Maps, Gmail, and Hangouts.