Think inside your AI world.
Databricks through Unl
Databricks can answer from across your lakehouse. Whether the data behind the answer meets the bar you set is your judgement — and a confident answer can rest on stale data.
Databricks through Unl reads your live lakehouse — Unity Catalog, AI Search, Genie spaces, custom functions — against the data-quality bar you have ratified, so an answer comes back trusted-or-flagged, with the quality condition it fails named.
What Databricks holds
Databricks’ managed MCP surface exposes governed data access:
- Unity Catalog data
- AI Search and Genie spaces
- Custom functions and AI Gateway tools
What the naked read gives you
A naked read returns the answer Genie computes. Powerful, and it won’t tell you the source table hasn’t refreshed in a week, because “data no older than a day” is a bar you set, not a catalogue property it applies for you.
The frame judges the data it is given; it does not verify the source’s accuracy.
What changes when Databricks is read measured
Say you’ve ratified a data-quality bar: any answer used for a decision must rest on tables refreshed within 24 hours and with no failed quality checks.
“Can I rely on this figure?” returns, measured: flag — the figure is computed correctly, but its source table last refreshed 40 hours ago, past the freshness bar you set so decisions aren’t made on stale data. Databricks supplied the answer; your ratified bar supplied the quality check and the reason.
And back again
When you accept a weekly-refresh table as fit for a particular report and record it, the next read holds that report to the exception you settled.
The answer comes back measured against what you already decided, and why.
The lane is live and open to this tool today: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
Questions people ask
Can AI check Databricks answers against a data-quality bar?
Yes, through Unl. You ratify the bar (freshness, passed quality checks, with reasons), and a Databricks read is measured against it — the answer returns trusted or flagged, with the condition it fails named.
How do I connect Databricks to Claude?
Databricks offers a managed MCP server over Unity Catalog, AI Search and Genie. Through Unl the same reads arrive measured against your ratified quality bar, so an answer reads as reliable-or-flagged.
Does Unl modify my lakehouse?
Unl reads through Databricks, and can write back on your explicit gesture — it never acts as a side effect of a read.
What if a table has no quality checks defined at all?
Then it is unchecked rather than passing, and the read keeps the two apart. Your bar asks for no failed checks, and a table with none configured returns no failures, which reads as clean while proving nothing. The read names the tables carrying no checks, so an answer used for a decision never rests on an empty result set.
What this is
Think inside your AI world — you stay in command
Save the thoughts, decisions and targets worth keeping, each with its reasoning, carried into every AI session the moment they matter. A new unit of exchange between you and your AI: the Settled Why with standing that travels. Unprompted.
Your whole AI world. What you decided at the epicentre. It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector — quick to connect, in a couple of steps.
MCP native·Human settled·Model agnostic·Your data
Measured Context
Connect Unl to bring the right information into the moment.
Your sources, read against the criteria you set.
The full product, open. Free at launch.