Think inside your AI world.
An AI that knows what I promised my investors
What’s actually useful here isn’t an AI holding a transcript of your fundraising pitch meeting. It’s one that inherits the commitment you made — the specific number, and why you set it there — so that when you ask how you’re doing against your investors, it answers with a verdict instead of asking you to restate the promise first.
Every investor question you put to a general model starts with a tax: restate the commitment, restate the reasoning, then get an answer. Unl removes the tax by holding the commitment itself. The read arrives measured against the number you promised, so the question gets a verdict, not a fresh briefing.
What should the AI actually be inheriting?
Not a transcript of the pitch, not your investors’ names or preferences — the specific figure you committed to and the reasoning that made it the right number. That’s a narrow, concrete thing: a target, a floor, a percentage, and the why behind it.
This is the authority axis, distinct from memory. You’re not asking a model to recall a conversation with you personally; you’re asking it to reason from a decision you already made, so its answer starts from your settled commitment rather than a generic assumption about what founders typically promise.
What does inheriting the commitment change in practice?
Say you're an API company founder whose commitment is specific: expansion revenue at or above £40k a quarter. Asked generally how expansion is going, a model with no access to that figure can only describe the trend — up, down, flat — without ever saying whether it clears the number you actually promised.
With the commitment inherited, the same question gets a real answer: “Expansion £28k against the £40k you promised.” That’s not a trend description; it’s a verdict against the specific thing investors are checking.
Why does re-briefing never actually end otherwise?
A stateless model starts every conversation with no access to your £40k commitment, so each time you ask about expansion you either restate the number or get an answer measured against nothing in particular. The restating isn’t a minor annoyance; it’s the direct cost of the commitment living nowhere the model can reach on its own.
Once the commitment is held where a read can reach it, the question stops needing a preamble — not because the model remembers you, but because it inherited what you already promised, and applies it every time without being asked to relearn it.
You don’t need an AI holding a transcript of your pitch meeting; you need one that inherits the specific number you promised investors, so a question about expansion or growth is answered against that commitment instead of a trend description.
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 investor commitments to AI every time?
By holding the commitment — the specific number and the reason you set it — where the AI can reach it, rather than restating it in every conversation. That’s the authority axis: the AI inherits what you promised and answers against it, so a question about your progress no longer needs a preamble.
Isn’t this just AI remembering my pitch meeting?
No. Memory recalls a conversation; this inherits a decision. You’re not asking the model to remember sitting across from your investors — you’re asking it to reason from the specific figure you committed to, so the answer is a verdict against that figure rather than a generic trend description.
What does an AI that inherits my investor commitments actually return?
A verdict, not a description — “expansion £28k against the £40k you promised” rather than “expansion is trending upward.” Because the commitment is held and applied automatically, the question gets measured against what investors are actually checking. 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:
Alpha is invite-only · free at launch.