Think inside your AI world.
BigQuery + Amplitude through Unl
When the warehouse and the product tool answer “how are we doing?” differently, the problem isn’t the data — it’s that no one target sits over both.
BigQuery + Amplitude through Unl reads the warehouse query and the product metric against the one target definition you ratified, so both answer the same question the same way — one figure, judged against your line. The definition is held in Unl.
The criterion that binds them
Say you’ve ratified a single target definition — the metric, exactly how it’s computed, and the line it must clear — because a warehouse figure and a product figure that mean subtly different things can’t both be measured against one goal.
The two naked reads
BigQuery returns the query result and table metadata. Amplitude returns the product metric. Both are correct in their own terms and quietly incomparable, because neither is computed to your single definition.
The one measured answer
Against your target: computed one agreed way across both, the metric is 18% — two points under your line; the warehouse and product versions differed only because they were counted differently, not because either was wrong. One figure, measured against the target you set.
And back again
If you move the target line after a strategy shift, that new line is ratified in Unl — and the next read holds both the warehouse and product figures to it, still counted the one agreed way.
The answer comes back measured against what you already decided, and why.
A router can return a BigQuery number and an Amplitude number. It cannot make them one comparable figure against a goal, because the target definition — the criterion — sits in neither the warehouse nor the product tool.
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.
Read further
Questions people ask
Can AI make my warehouse and product metrics comparable?
Yes, through Unl. You ratify one target definition — the metric, how it’s computed, and the line it must clear — and BigQuery and Amplitude are read against it, so both answer the same question the same way, judged against your line.
Why do my warehouse and product numbers disagree?
Because they’re counted differently, not because one is wrong. The single definition that makes them comparable lives in Unl, so the read computes both the one agreed way and measures the result against your target.
Does Unl retain my BigQuery or Amplitude data?
Unl reads through BigQuery and Amplitude, and can write back on your explicit gesture — it never acts as a side effect of a read.
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.