Your engineers waste 5 hours a week looking for information across tools. That's $50K/year per engineer.

The answer exists. It's in a Slack thread from three months ago. Or a Google Doc someone shared in a channel. Or an email attachment. Or a meeting recording nobody transcribed. Or a Notion page that's been moved twice. The answer exists, but finding it takes longer than re-creating it, so people re-create it, re-ask it, or make decisions without it.

Research consistently finds knowledge workers spend 20-30% of their time searching for information. For a team of ten, that's two to three people's worth of productive time lost to retrieval. The problem isn't that the information doesn't exist. It's that no tool searches across all the tools where the information lives.

One search bar for your company's full knowledge base

AI search connects to Slack, Google Drive, Dropbox, Notion, Gmail, and meeting tools. It reads inside every document, email, transcript, and thread and searches by meaning, not keyword. Ask "what was decided about the pricing change" and get a cited answer drawn from the Slack discussion, the meeting transcript, and the strategy document simultaneously.

The search reads inside PDFs, slide decks, spreadsheets, images, and every other format. A decision buried in a meeting transcript is as findable as one in a titled document. The AI assistant synthesises across sources, so "summarise our approach to enterprise pricing" draws from everything the team has said, written, and decided.

The difference between keyword search and meaning search

Traditional search finds documents containing specific words. You need to guess the right keywords, remember who might have written the document, and hope the title is descriptive. AI search finds answers by meaning. "How do we handle refunds for annual customers" finds the relevant policy even if nobody titled a document with those words. The search understands what you're asking, not just the words you're asking with.

Agents that surface answers proactively

Agents don't just find information when asked. They can monitor discussions and surface relevant context before anyone has to search. A question in Slack about a client's history prompts the agent to pull the relevant context. A new project discussion triggers the agent to find past work on the same topic.

Who this is for

Engineering teams where context is spread across code, docs, Slack, and meetings. Product teams searching across roadmaps, specs, and customer feedback. Sales teams looking for competitive intel, case studies, and pricing precedents. Marketing teams finding campaign assets and brand materials. Startups where everything is everywhere. Any team that's spent fifteen minutes looking for something that should have taken fifteen seconds.

For the broader team knowledge approach, see team wiki.

Get started

Give your team one search bar for everything. Try Fabric free. See pricing for teams.

FAQs

What tools does Fabric search across?

Fabric connects to Slack, Google Drive, Dropbox, Notion, Gmail, and meeting tools. See connections for the full list.

Does it search inside documents, not just titles?

Yes. AI search reads inside every PDF, slide deck, email, transcript, and document and searches by meaning.

Can the AI synthesise across multiple sources?

Yes. The AI assistant draws from every connected source and produces cited answers that combine information from Slack, documents, meetings, and email.

How is this different from searching each tool separately?

Each tool's search only covers that tool. Fabric searches across all of them simultaneously and produces a single cited answer. The answer to "what did we decide about pricing" might draw from a Slack thread, a meeting transcript, and a strategy doc at the same time.

Does the search understand questions, not just keywords?

Yes. AI search works by meaning. "How do we handle enterprise security questionnaires" finds the relevant policy even if nobody titled a document with those words.

Can agents surface answers proactively?

Yes. Agents can monitor discussions and surface relevant context before anyone has to search. A question in Slack about a client can prompt the agent to pull the relevant history.

How long does setup take?

Connect your tools in minutes. There's no IT deployment or configuration project. The search works across connected sources immediately.

Is our data secure?

Yes. Fabric uses AES-256 encryption and is CASA Tier 2 compliant. Your data is never used to train AI models.