Think inside your AI world.
Which SKU should I drop?
Deciding what to cut from a range usually comes down to instinct — the line that feels tired, the one nobody’s excited about any more. That’s the wrong test. The right one is whether a SKU still clears the profit you actually need it to make, against a rule you set once and stopped revisiting.
Every SKU takes up space that something else could use better, and that trade only makes sense against a number: the profit it needs to clear to earn its spot. Unl holds the keep rule the owner ratified, so the drop list is generated from the rule, not from which product feels stalest this month.
Why instinct picks the wrong SKUs to cut
Instinct notices what’s visible — the line that hasn’t moved this week, the one that got a complaint. It doesn’t notice the quietly profitable-looking SKU that’s actually earning less than the space costs, because that SKU never triggers the feeling that something’s wrong.
A keep rule fixes that blind spot by replacing feeling with a number: not “does this feel worth keeping” but “does this clear the profit I set as the bar,” applied the same way to every line whether it feels tired or not.
What the rule actually says
Say you sell textiles and keep it simple: a SKU stays only if it clears more than £300 in monthly profit. Nothing about feel, nothing about how long it’s been in the range — just the number.
Measured against your own rule, the drop list writes itself: “Drop these two — both under £300 monthly profit against your keep rule.” Neither line looked obviously wrong on the shop floor; both were quietly under the bar you had already set.
Why the rule has to be applied, not remembered
A general-purpose model can list every SKU’s monthly profit if it’s handed the figures, but it can’t say which ones to drop without your £300 line, because that figure is a decision about rail space and priorities, not something visible in the sales report.
Measured context holds the £300 keep rule and checks it against every SKU automatically, so the drop list comes back as a verdict against your own bar — not a guess based on which product happened to catch your eye this week.
Which SKU to drop is a verdict against the profit floor the owner ratified, not a feeling about which line looks tired; measured context checks every SKU against that floor and returns the ones genuinely under it.
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 decide which products to cut from my range?
Against a keep rule you set once, not by which line feels tired. For one seller that’s a flat £300 monthly profit bar, applied the same way to every SKU regardless of how it feels on the shop floor. Instinct notices what’s visible; a keep rule catches the quietly underperforming line instinct misses.
Why does a product that seems fine still end up on the drop list?
Because looking fine and clearing your keep rule are different tests. A SKU can sell steadily and still sit under the monthly profit bar you set once space and effort are accounted for — and only that bar, not how it feels, tells you it’s not earning its place.
Can AI tell me which products to drop from my range?
It can total up profit per SKU if you hand it the numbers, but it can’t say which to drop without your keep rule, because the profit bar is a decision about your own rail space, not a figure in a sales report. Measured context supplies the rule so the read returns the SKUs genuinely under it. 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
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.
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