Think inside your AI world.
A guidance change, against a valuation you set
Earnings season is a wall of sound: hundreds of calls, thousands of guidance lines, every management team talking at once. What you actually need is the one revision that moves an assumption your valuation is built on — the input you decided the price hangs on — and that is a line the feed cannot hold, because the model is yours.
Read company guidance through Unl and the whole earnings stream arrives measured against the valuation you set — so instead of a season of calls to sit through, you get the single revision that moves the assumption your price rests on, with the reason it crosses. The one, not the thousand.
What company guidance and earnings calls publishes
Guidance is public and material, and there is a lot of it:
- Hundreds of earnings calls and guidance updates a quarter, clustered into a few weeks
- Every metric revised at once — revenue, margin, capex, the outlook language
- Thousands of issuers, almost none of which you hold or model
- No idea which revised line bears on a valuation you set — that context is not on the call
The line the feed can't hold
Say you built a valuation and named the assumption it hangs on: my price depends on the operating margin they guided to; if a revision cuts that guidance below the level I underwrote, the valuation no longer holds. That dependency is a decision you made; it is not a field the transcript models.
The reason the line is yours is the whole point. An earnings feed can push every guidance change; it cannot know which single input your price rests on, or the level below which your case breaks, or that a revenue beat is noise to you when the margin line you underwrote was quietly guided down.
The frame judges the data it is given; it does not verify the source’s accuracy.
The one that crosses
So when a company revises the exact assumption your valuation depends on, past the level you set, it crosses — and the other hundred calls that week do not. Through Unl the answer arrives as a verdict: this quarter’s guidance moves the input your price rests on below the line you underwrote, so the valuation you set no longer holds as written — here is the revision, read against your model. Same public call; a decision instead of a digest.
You didn't ask — it was already there
You did not ask for it. The assumption was already ratified, so the next time you open Claude on that name the crossing is already in the window — read against the input your price hangs on, with the reason it cleared it — rather than waiting for you to work back through the transcript. You author the model once; the read applies it every time guidance moves.
The answer comes back measured against what you already decided, and why.
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.
Questions people ask
How is this different from an earnings alert or a transcript feed?
An alert fires on the source’s rule — a call happened, guidance changed, a keyword appeared. This fires on YOUR rule: the single assumption your valuation depends on, the level that breaks it, with the reasoning attached. The feed shows you every revision; the measured read shows you the one that moves a number your price actually rests on, and why.
Do I get pinged the moment guidance changes?
The capability is that the crossing arrives measured against your model the next time you are working — it is already in the window, read against the assumption you set, rather than a stream you monitor. A scheduled push is a separate delivery; what the read guarantees is that when a revision moves the input your valuation hangs on, it surfaces as a verdict against your model, not as one more earnings item.
Does Unl change anything on the call or in my model?
Unl reads through the public guidance and transcript, and can write back on your explicit gesture — it never acts as a side effect of a read. The call is a public disclosure; your valuation lives in Unl; the read joins them and returns a verdict, altering neither.
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.