Scattered Knowledge
Most of the knowledge that matters to you is scattered across multiple places. Your emails. Your calendar. Your documents. Your storage systems. You probably switch between five different apps just to get a complete picture of what's happening in your work life.
AI assistants typically don't see any of this. They respond based on whatever information you paste into the chat window. They're isolated, which is part of why they often miss critical context or require you to manually compile information they should be able to access directly.
Weave now connects to all your data sources through MCP (Model Context Protocol) and integrated storage connections, letting you tap into Google (Drive/Gmail/Calendar), Jira, Slack, Monday, OneDrive, Outlook and more, all from a single interface. You now has access to the knowledge that actually matters to your work, all within Weave’s secure and sustainable desktop app!
Why Data Connectivity Matters
The difference between AI that knows your context and AI that doesn't is massive.
Think about a typical workflow right now: you want help drafting an email response. To do that effectively, your AI models should know what was said in previous messages, what deadlines are relevant to this conversation, what your company's tone is, what related projects are active. Instead, you manually copy and paste a few sentences, hope you haven't left out anything important, and get a response that misses the mark because the AI didn't have enough information.
Or you're trying to understand the full picture of customer feedback across dozens of support emails and messages. You either spend an hour manually reviewing threads and compiling notes, or you paste fragments into an AI and watch it guess at patterns. Neither is ideal.
Data connectivity solves this at a fundamental level. When you can instruct AI to gather and read the documents you need, it has the full context necessary to give you genuinely useful answers. It understands your priorities because it can see your calendar. It understands concerns because it can read your email threads. It understands your documentation and knowledge base because it's connected directly to where that information lives.
The result is faster, more accurate, more contextually aware assistance. Less manual compilation. Less context loss. Less back-and-forth to clarify what you actually meant
How Weave's Data Connectivity Works
Weave's data connections work through two mechanisms: MCP (Model Context Protocol) for standardized integrations, and direct storage connections for platforms like Google Drive and Microsoft 365's OneDrive.
MCP: The Open Standard for AI Context
MCP is a tried and true open protocol that lets AI systems safely request information from external data sources. Think of it as a standardized language that your AI speaks to your other tools. Rather than Weave having to build custom integrations with every possible service, MCP provides a universal interface. More platforms are adopting it, which means your data connectivity options keep expanding as Weave adds more options to the catalog.
When you enable an MCP integration in Weave, you're giving Weave permission to search that data source when it's relevant to your conversation. The AI can request specific information (pull the last 10 emails about a project, search your documents for a specific topic, check what's on your calendar next week) and incorporate that information into its reasoning in real time.
Storage Connections: Google Drive, OneDrive, and more
For the platforms where you store most of your files and communications, Weave has direct integrations. You can connect your Google Drive and/or OneDrive and Weave will be able to access your documents, spreadsheets, and files.
These connections are scoped and encrypted. You decide what data Weave can access, and you can revoke access at any time through settings. The data isn't uploaded to Weave's servers or used for any purpose beyond responding to your requests. It's processed locally on your device, with maximum privacy.
What You Can Actually Do With This
Practical examples are where this gets interesting.
Project Context Without Manual Compilation
You're working on a project with a specific team. Instead of opening five different apps to understand the current state (checking the project management tool, reading relevant emails, reviewing the latest documents, checking the timeline on your calendar) Weave can access all of that context for you instantly. Ask it "what's the current blockers on the web redesign project," and it knows because it can see your email threads about it, your documents, your calendar, the status discussions you've had.
Knowledge Base Search and Synthesis
You have unstructured sources of internal documentation, email threads about company policies, previous client conversations, and relevant industry articles all scattered across different systems. You want to understand your company's actual stance on a specific issue, not just find documents that mention it, but understand what you've actually committed to, what your team has learned, what the tradeoffs are. Weave can now search across all of that simultaneously and synthesize a coherent answer that's rooted in your actual documentation and history.
Smarter Email Drafting
You want to write a response to a client email, but you need context from previous conversations and relevant documents. Previously, you'd have to manually pull up those threads, read through the history, review any shared documents, and then draft your response, often in a different app. Now Weave can access your email history and documents directly. Tell it "draft a response to the latest email from the design team," and it pulls the full thread, checks any related documents you've shared, understands your previous commitments, and drafts something that's actually coherent and contextually appropriate.
Meeting Summaries and Follow-Up
You run a meeting and Weave transcribes it locally on your device. But it can do more than transcribe. If you connect your calendar and email, Weave knows what decisions were made in previous meetings on this topic, who was expected to deliver what, and what the actual context is. It generates a summary that's not just a transcript, but an actual summary that captures decisions, action items, who owns what, and how this connects to previous commitments.
Customer Understanding at Scale
If you're in customer service or sales, imagine being able to ask your AI to summarize all communications with a specific customer, across email, documents, and meeting transcripts. What have we promised them? What's their actual use case? What have been the pain points? What can we offer that actually solves their problem? Your AI now has the full context it needs to give you a genuinely useful answer.
Privacy and Security: Built In, Not Bolted On
If Weave can access your data, how is it still private?
The answer comes down to three things: how the data moves, how much control you have, and how the connections are secured.
Data stays on your device by default. When you use local models in Weave, your data never leaves your machine. The AI processes your information locally, generates responses locally, and nothing travels to cloud servers for analysis or logging. When cloud models are necessary, the same no/low data retention infrastructure applies: your data goes in, gets processed, and doesn't stick around.
You have granular control. Connect to Jira but not Confluence. Allow access to specific projects, not your entire account. Disconnect and revoke permissions anytime. Weave doesn't store your data, doesn't train models on it, and doesn't log your questions or track what information was accessed. Every layer of the architecture is designed around one principle: your data is yours.
The connections use industry-standard security. When you connect your Notion, Jotform or Canva account to Weave, you're using the same security standard every secure app uses. You authenticate directly with your account provider, not with Weave. You grant permissions through their official interface. You revoke access the same way you'd disconnect any other app. Your passwords never touch Weave's systems.
This is what separates Weave from most AI platforms. Your data isn't raw material to be mined or logged.The moment an AI platform starts treating it as training fuel, it stops being your tool and becomes a liability. Weave's architecture is built to make sure that never happens.
What This Means for How You Work
The practical effect is that you can now use Weave like it actually understands your work. Not in a magical sense (it still has the limitations of AI) but in the practical sense that it has access to the information it actually needs.
You can ask contextual questions without constant manual recompilation. You can get work done in fewer back-and-forths. You can rely on your AI to catch context that you would have previously forgotten to include. And you can do all of this while your data stays private, your conversations stay secure, and your energy footprint stays low.
That's the point of building connectivity into an AI system that's actually designed around privacy and sustainability. It's not about making Weave flashier or more complex. It's about making it actually useful in the way that real work requires. Your data is everywhere. Your AI should be able to reach it.
Try it yourself. Connect your data to Weave and start having conversations that actually understand your context. You can download Weave for free, and connectivity features are available at all subscription levels.
