Think inside your AI world.
The prioritisation meeting, through Unl
A prioritisation meeting is supposed to produce an order. Most produce whichever order the loudest voice in the room argued for, because the actual weighting formula lives in the facilitator’s head and gets applied inconsistently, if it gets applied at all.
A ranked list is only honest if the weighting behind it is fixed and applied evenly. Unl holds the formula you ratified — reach times confidence, retention items counted double — so the meeting opens with a list already ranked to it, not a blank page waiting for the loudest voice.
What decides the order, really
Ask a room to rank ten candidates without a shared formula and you get an order shaped by confidence of delivery, not quality of reasoning — the person who argues most fluently gets their item ranked highest, and the quieter, better-reasoned case sinks toward the bottom.
A formula fixes that, but only if it’s actually applied consistently, item by item, rather than referenced in spirit and then overridden by whoever pushes hardest once the meeting is underway.
Your actual formula
Say you run product for a creator-monetisation tool and have fixed your weighting precisely: rank by reach times confidence, with retention-touching items counted double, because retaining a creator is worth more to the business than reaching a new one who might churn in a week. This week’s loudest request is a discovery feature with high reach and modest confidence.
Measured against your own formula, the order isn’t decided by volume: “Re-ranked to your weighting — the retention fix tops it, not the loudest request.” The discovery feature wasn’t dismissed. It simply didn’t out-rank an item that your own formula counts twice.
What the meeting becomes
A generic scoring template can hold columns for reach and confidence, but it can’t apply your retention-doubling rule unless someone manually remembers to apply it to each item, which is exactly the inconsistency that let the loudest request win in previous meetings.
Measured context applies your formula to every candidate the same way, every time, so the meeting opens on a list already ranked to your weighting — and spends its remaining time on sequencing and scope, not on relitigating whose case sounded most confident.
A prioritisation meeting produces the loudest voice’s order unless a fixed weighting is applied to every candidate consistently; through Unl the PM’s own formula ranks the list automatically, so the room starts from a reasoned order rather than a fresh negotiation.
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
Why does the loudest person in the room usually win prioritisation?
Because without a shared, consistently applied formula, the order gets shaped by confidence of delivery rather than quality of reasoning. A well-reasoned but quietly argued case sinks to the bottom next to a confidently pitched one. A fixed weighting, applied to every item the same way, removes that advantage.
What’s a fair way to rank features in a prioritisation meeting?
A formula fixed before the meeting starts and applied to every candidate the same way — for one creator-tools PM, reach times confidence, with retention-touching items counted double because keeping a creator matters more than reaching a new one. The formula only works if nobody gets to override it live.
Can AI run my prioritisation meeting?
It can hold generic columns for reach and confidence, but it can’t apply a specific rule like doubling retention items unless it’s reminded every single time, which reintroduces the inconsistency the formula was meant to fix. Measured context applies your formula automatically to every candidate. 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.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
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