Think inside your AI world.
An AI that knows my product priorities
What you actually want isn’t an AI that remembers chatting with you about the backlog. It’s one that inherits the weighting you already settled — churn beats activation, activation beats delight — so that asking “what’s next” returns an order, not a fresh negotiation.
Every prioritisation question you ask a generic model starts with a tax: restate the weighting, re-justify the order, then get an answer that forgets both by next session. Unl removes the tax by holding the weighting itself, so “what’s next” comes back already ranked against it.
What you actually want it to hold
Not your writing style or your calendar. The one thing that actually decides a prioritisation question: the order you’ve settled between the things that compete for your time — churn work first, then activation, then polish — and the reasoning behind ranking them that way.
This is the authority axis, not a memory of conversation. You’re not asking a model to recall discussing the backlog with you last week; you’re asking it to inherit a weighting you already decided, and reason from your settled order rather than a generic default.
Your actual weighting
Say you build a consumer utility app solo and settled your weighting months ago: churn work outranks activation work, which outranks delight polish, in that order, always. Three items are sitting in your backlog this week — one from each category — and normally you’d re-rank them from feel.
Measured against your own weighting, the order is simply applied, not re-derived: “By your weighting, the churn item leads, then activation, then the delight polish.” Nothing about the three items changed. What changed is that the ranking you’d already settled did the deciding.
Why re-explaining never ends without this
A stateless model starts every session at zero, so a prioritisation question gets a plausible-sounding answer built on whatever you happen to paste in that day — and if you forget to restate the churn-first weighting, the model will happily rank the delight polish above a churn fix.
With the weighting held permanently, you stop re-explaining it because the thing worth explaining — the order and why — is held once and applied every time you ask what’s next, this week and the next one.
You don’t need an AI with a memory of your backlog conversations; you need one that inherits the weighting you already settled, so “what’s next” returns your own order applied consistently rather than a fresh negotiation every session.
Reads through Unl arrive with measured context — in the presence of the decisions you’ve already settled. The reach lane is live: 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.
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Questions people ask
How do I stop re-explaining my product priorities to AI every time?
Hold the weighting itself — the order you’ve settled between what competes for your time, and why — where a read can reach it, rather than pasting it in each session. That’s the authority axis: the read inherits what you decided, so “what’s next” no longer needs a fresh explanation.
Isn’t an AI that knows my priorities just AI memory?
No. Memory recalls a conversation; this inherits a decision. You’re not asking a model to recall chatting with you about the backlog — you’re asking it to reason from the weighting you already settled, so a prioritisation question is answered against your order rather than a generic default.
What happens if I forget to restate my priorities to a normal AI?
It ranks from whatever it’s handed that session and nothing else, so forgetting to restate a churn-first weighting means it can rank a delight polish above a churn fix without knowing that’s backwards. Measured context holds the weighting permanently, so it’s applied whether or not you mention it that day. The reach lane is live: 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.
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.