Learn - Workspace

Count is built around a workspace, a single environment where your team connects to data, works together, and builds a shared understanding of the business. Every workspace brings together three things, the data you trust, the people who need it, and the context that makes analysis meaningful. Say you're a data analyst and you want a product manager to explore insights without accidentally breaking anything. You control who's in and what they can do. Analysts can build, query, and run the AI agent, while teammates, like that PM, can collaborate and consume insights without touching the underlying work. And getting them in is easy. Whitelist your company domain and anyone in your organization can join straight away. No manual invites needed. Of course, you can send individual invites to collaborators as you deem necessary. Once your team is in, they need data. Connect your warehouse like BigQuery, Snowflake, and so many more. And everyone works from a single trusted source, rather than scattered exports and some stale spreadsheets. But raw data isn't enough. When finance defines revenue one way and marketing defines it another, decisions fall apart. Define your key metrics once in count semantic layer or pull them from GitHub. And everyone works from the same metrics and the same schema every time. As your team grows, so does the work. Projects keep everything organized by initiative, team, or objective. So a growth analysis and a finance review don't end up tangled together in the same project or the same canvas. And just like workspace access, you control who sees each project and which data sources are available with it. So a marketing team only sees what's relevant to them, and sensitive finance data stays where it belongs. Inside every project, your team works on campuses, where a PM can explore a funnel, a data analyst can run queries, and an executive can review the outcome, all in the same place and at the same time.