Overview
AI Analyst and the Daasity MCP Server now answer from your dashboards the way you see them in Daasity: with the dashboard's filters, the default scope your data model sets, and any filter you name in your question. Every answer tells you which filters it used, so you can check it against the dashboard at a glance. Refreshes are also much faster on large accounts, more of your measures come through, and personal customer information stays out of answers.
What's new
- Dashboard filters carry through.When an answer comes from one of your dashboard tiles, it now runs with that dashboard's filters, so the number matches what you see on the dashboard. For example, if a dashboard is filtered to your online store, asking "What were my top products last quarter?" answers for the online store instead of adding every channel together. Answers also list the filters that were applied, keep the dashboard's totals row out of ranked results, and tell you when a tile's row limit cut the results short.
- Your filter wins.Ask for a different value than the dashboard's default, and the answer uses yours. For example, "top 10 brands, latest 52 weeks" from a tile that defaults to 12 weeks now runs for 52 weeks and says so. If you ask for something the tile can't filter on, like a region on a tile with no region filter, the AI tells you the tile can't answer that way rather than returning the tile's default numbers under your label.
- Default scope, applied every time and stated.Your data model can say how a view should be filtered unless someone asks otherwise, like ranking retail brands against your competitive set instead of the whole category. Answers now apply that default consistently and say which scope was used. For example, "top 10 brands, last 52 weeks" returns your competitive set with a note that it's scoped that way, and adding "across the whole category" returns every brand. The AI also won't add up markets or time periods that are meant to be picked one at a time. This works now, with no refresh needed.
- More of your measures come through.Averages, medians and measures calculated from other measures in your data model are now part of your views, so the AI uses your definition instead of working out its own version.
- Column names you recognize.Tables in AI Analyst show your field labels, like "Net Sales," instead of the underlying field name.
- Faster refreshes.Refresh Views on accounts with very wide reports used to take hours and now finishes in minutes.
- Personal customer information stays out of answers.A customer's name, email, phone number, street address and IP address are never returned. City, state, zip code and country are, so regional reporting keeps working. Business locations and products aren't treated as personal, so store, warehouse and distribution center addresses and full product names come through. For example, "net sales by shipping zip code last quarter" returns zip codes, while a request for your top customers' emails comes back with that field withheld and a note saying so.
- Know when a measure isn't available.If a measure can't be built on a view, your AI can see which one and why, and tells you instead of substituting a similar measure. For example, ask for lifetime net sales on an order-level view and the AI explains that it lives on your customer views, rather than adding up a different number under that name.
What to do
- Run Refresh Semantic Layer, thenRefresh Views, from the account menu in AI Analyst. Refresh Semantic Layer picks up your dashboard filters, and Refresh Views rebuilds your views with everything above. Refresh Views was previously called Refresh MCP Views.
- Ask a question you already know the answer to from a dashboard, like your top 10 products by net sales last month, and compare the two.
This is the same refresh that brings back the fields in today's fix post, so you only need to run it once.