Last week one of our web pods restarted 11 times in 24 hours, and another restarted 9 times. The app worked, users were happy, and latency was fine. Our application logs had nothing to say about it, just gaps where a process used to be.
We spent a couple of days chasing the wrong thing. Then we found a Kubernetes default we d never thought to override, and the crash loop stopped.
The Basedash semantic layer lets teams create reusable SQL metrics and models that AI can reference across chat, charts, dashboards, insights, and automations.
Side note: We're live on Product Hunt today with our Slack Data Agent!
Export to Excel
You can now export to Excel (.xlsx) anywhere you could already export data. Charts, tables, markdown tables, and in-browser record views all get a native Excel option right alongside CSV, so the data you pull out of Basedash lands in a real spreadsheet formatted columns and all instead of a raw text file you have to clean up first.
Someone on the team finally looked at our production client bundle, which we hadn't checked in a while, and it was 25.67 MB. We got it down to 18.34 MB without removing a single feature. Most of the wins were stuff we were shipping to customers that had no business being there.
Bundle size is the metric that drifts up one dependency at a time until somebody notices. We noticed, turned on rollup-plugin-visualizer, and read the treemap. That treemap is the most useful 30 seconds of the whole project. You stare at the rectangles and immediately have opinions about every one of them.
Basedash Embedding puts the full power of Basedash inside your own product. Drop a dashboard in with one iframe, or embed the whole app so your customers can chat with the AI agent, build their own dashboards, and get automatic insights — without ever leaving you. Row-level security scopes every customer to their own data, and customization controls decide exactly which features they see. Setup is one iframe and a JWT. Analytics, inside your product. Scoped to every customer.
Skills are reusable bundles of instructions that every Basedash AI surface can read on demand.
Define your metrics once and any AI agent in your workspace will pick up the skill when it's relevant. No more pasting the same caveats into every prompt.
Each skill is a short, plain-language playbook for one concept. Admins manage them; everyone else's AI gets the benefit. Add them as you go — the more you teach Basedash, the more it acts like an analyst who already knows your business.
Basedash already reads from your databases, warehouses, and SaaS tools. Now it can act on them too. Connect any MCP server — Linear, HubSpot, Slack, Resend, Notion, GitHub — and the Basedash agent gets new tools it can use right inside chat. Ask it to email your latest signups, file a Linear bug from a support ticket, or update a HubSpot lead based on what a user did in your product. Pair with Automations to run the whole flow on a schedule. Connect any app. Take action anywhere.
Basedash is now an MCP server. Connect Claude, Cursor, ChatGPT, or any MCP-compatible client and your AI agent can ask Basedash anything about your data — across every database, warehouse, and SaaS tool you've already connected to your workspace. It can pull live numbers, compare cohorts, generate charts, and dig into trends, all governed by the same access controls your team already uses. Your data analyst, inside every tool you ship in.
