The Kapa team is very responsive and engaged as we continue to define and expand our AI strategy. The product is very simple to implement and manage.

Our main customer-facing assistant pulls from our docs and our public roadmap through a web crawl, so users can easily get answers about current and upcoming capabilities. We've also used Kapa's retrieval to build a copilot inside our own product, so customers get answers grounded in that same content while they're actually using the tool, not just when they're on the docs site. On top of that, we run an internal version of Kapa for our own team, and it's quietly become the thing people reach for over our own internal search. Notion and Jira search inside our company are bad enough that people have said flat out they'd rather just ask Kapa, and it's especially handy when something is documented in more than one place since it finds the version with the right context instead of leaving us to guess.

We built our in-product AI agent on top of Kapa's MCP server, so it can search our knowledge base directly instead of us having to build that retrieval layer ourselves. It's become a core piece of how the agent answers technical questions, and when we've hit issues, the team has been quick to dig in and explain exactly what was happening. Docs indexing is fast enough that we can push a fix to our content, wait a bit, and test again, which matters a lot when we're iterating on how well the agent handles a specific use case. We've also been able to get fairly granular about what the agent does and doesn't reference, including steering it away from recommending features that are still in beta.