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Common questions about the context layer specifically. For Desktop questions see the Palette Desktop FAQ. For broader Palette questions see the general FAQ.
Connect your tools. The context layer's signal engine interprets events into meaningful context and builds a living map of your org. Your team reads it as plain English. Your AI tools read it via MCP. See How it works for the full picture.
Every claim in your context is confidence-scored and traced back to its source. The context layer tells you when it's not sure. Your team validates context through lightweight check-ins, and you can correct anything instantly. Your wiki says what you wish were true. The context layer reflects what's actually happening, and tells you when it's uncertain.
Dashboards show you charts and wait for you to look. They go stale between refreshes. The context layer is a living layer that updates itself, serves both humans and AI tools, and flags when something is uncertain or outdated. It works for you even when you're not looking at it.
Wikis capture intent. The context layer captures reality. A wiki goes stale because someone has to manually maintain it. The context layer stays current because it reads from your tools and confirms with your team. Both are useful, they just solve different problems.
Slack, Jira, Linear, GitHub, Notion, and Google Calendar today. More coming based on what design partners need. See the integration pages for the details on each.
Connecting your tools takes a few minutes. Building your initial context map can take up to 30 minutes depending on data volume. You'll see value the same day.
You can, but then your AI only sees what you see. Your Slack channels, your repos, your docs. When every team connects their tools to the context layer, your AI gets context from across the whole org, teams, channels, and projects you're not in, because those teams opted in. It's the difference between giving your AI your silo and giving it the company. See MCP for more.
The context layer doesn't replace good processes. It makes the context from those processes available everywhere, to every team member and every AI tool you use. Good processes generate valuable context. The context layer makes sure that context doesn't stay locked in one tool or one person's head.