Think inside your AI world.
Snowflake + Amplitude through Unl
The warehouse has a retention number and the product tool has another, and they disagree — because “retained” was never defined in one place they both answer to.
Snowflake + Amplitude through Unl reads the warehouse cohort and the product retention against the single definition of “retained” you ratified, so the two resolve to one number that means what you set. The definition is held in Unl.
The criterion that binds them
Say you’ve ratified one definition of retention — the action, the window, the cohort — because two tools each computing ‘retention’ their own way produces two numbers and endless meetings about which is real.
The two naked reads
Snowflake returns the cohort via Cortex querying. Amplitude returns its own retention chart. Both are internally correct and mutually inconsistent, because each carries its own idea of “retained” — and neither holds yours.
The one measured answer
Against your single definition: read the same way — same action, same window, same cohort — retention is 26%, and the warehouse-versus-product gap you kept arguing about was two different definitions, not two different truths. One number, meaning what you decided.
And back again
If you refine the window from thirty days to twenty-eight, that one change is ratified in Unl — and both the warehouse and the product read move to it together, staying consistent.
The answer comes back measured against what you already decided, and why.
A router can fetch a Snowflake cohort and an Amplitude chart. It cannot make them agree, because the single definition of “retained” — the criterion — lives in neither warehouse nor 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 reconcile retention numbers from my warehouse and product tool?
Yes, through Unl. You settle a single definition of retention — the action, the window, the cohort — and Snowflake and Amplitude are read against it, so both resolve to one number that means what you decided.
Why do my two tools report different retention?
Because each carries its own definition of “retained.” The single definition that makes them agree lives in Unl, so the read applies one meaning across both and the gap between them disappears.
Does Unl store my warehouse or product data?
Unl reads through Snowflake 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
Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.
It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
Alpha is invite-only · free at launch.