Honest Methodology
What can AI visibility tools actually measure - and what can't they?
The short version: tools can measure what AI engines answer today. They can't see inside the model. Anyone who says otherwise is selling you something.
28 Labs · August 2026
AI visibility tools work by asking engines real questions and reading the answers - what's called retrieval measurement. That's real and repeatable. What no tool can measure is what a model "knows" from its training. The difference decides what you can fix, how fast, and what any vendor can honestly promise you.
How an AI answer actually gets made
2
The engine reads a shortlist of live pages
3
It blends them with what it already "knows"
4
The answer names a few brands
Two ingredients: live retrieval (measurable, fixable in weeks) and training memory (not directly measurable, moves over months).
Step 2 is where measurement lives. When an engine searches the web to build an answer, the pages it reads and the brands it names are observable facts - ask the same questions on a schedule and you get a real instrument: who's named, who's recommended, which pages the answers came from.
Step 3 is where the overclaiming lives. Models also carry what they absorbed in training - and there's no API for that. You can see its fingerprints when an engine names a brand without reading any page. But no tool can audit it, and nobody can promise to change it on a deadline.
The honest split
Measurable today
- Whether you're named when buyers ask, question by question
- Which brands get recommended instead of you
- Which pages the answers were built from
- How that changes when you change your pages
Not measurable, by anyone
- What a model "knows" about you from training
- Why one engine trusts a source another ignores
- Guaranteed movement on a training-data timeline
If a vendor's pitch lives in the right-hand column, ask them how they measure it. Watch what happens.
Why the difference matters commercially
Retrieval is fixable on a timeline you control: publish the page that answers the question, get into the sources engines read, and the counts can move inside weeks. Training memory moves slowly and indirectly - it follows what the public web says about you, over months. So an honest engagement measures retrieval, fixes retrieval, and treats the slow layer as a tailwind you earn - not a dial anyone can turn.
The test to run on any vendor - including usAsk: "Which questions, which engines, and can I re-check every number myself?" A real measurement re-derives - specific questions, specific answers, counts you can verify. A score out of 100 that can't be traced to answers isn't measurement. It's theatre.
What we do about it
We measure the retrieval layer against a frozen set of real buyer questions - the same questions every wave, so movement means something. We name this limit in every report we ship, because clients who understand the instrument make better decisions with it. That's the whole trick: measure what's measurable, be honest about the rest, and let the counts speak.