K1 · Stage 4 — Prove: the closed-book baseline
Model Memory Audit· KEOmemory measurement with the web off
The verified, canonical, readable, citable record AI models learn from — so each new model is trained to know and recall your business correctly.
Read the definitionTier 1 measures what the AI finds about you on the internet. This audit shows what it remembers without outside help. The two results are not the same.
The Machine equityMachine equity is the business asset that arises from AI systems being able to recognize, understand, recall, recommend — and, on a user's behalf, transact with — a brand. Composed of three layers: rented reach (retrieval visibility), owned memory (trained model recall), and machine buyability (agent-side transactability). Measured honestly only as a portfolio of ranged, per-layer metrics — never as a single blended score.
Read the definition loop
Machine equity is the business asset that arises from AI systems being able to recognize, understand, recall, recommend — and, on a user's behalf, transact with — a brand. Composed of three layers: rented reach (retrieval visibility), owned memory (trained model recall), and machine buyability (agent-side transactability). Measured honestly only as a portfolio of ranged, per-layer metrics — never as a single blended score.
Read the definition- 1 · MEASURE — where you stand and which questions matter (Citation Tracker, Selector)
- 2 · FIX — why the AI skips you, fixed in code (Gap Analysis, Gap Closure)
- 3 · ANCHOR — an unambiguous entity, present where models learn (Entity Anchor, Corpus Campaign)
- 4 · PROVE ↺ — closed-book baseline and the delta at every new model generation (Model Memory Audit, Release Audit)
KG Doctor and the Claim Coherence Engine — the studio's own instruments — serve the entire loop.
AVE Studio doesn't run short-term campaigns: we build our partners' machine equity, across model generations.
What does the AI find on the internet?
The AI searches live: it visits the web, reads sources, then answers from them. If your content is available and adequate, it can find you today.
What does the model know on its own?
Measurement with the web off: the model can rely only on the knowledge it acquired during training. This is the more durable layer: it shows whether the AI names you without external sources — and what it knows about you.
Unaided recall
Does it name you in your own category when we don't reveal your brand name in advance?
Recognition by name
Then we provide your name and check whether the model recognizes you, and whether its knowledge is accurate. The order is fixed so the first measurement isn't contaminated.
Repeated measurement across models
We repeat the audit multiple times on OpenAI, Anthropic and Google models, because no reliable conclusion can be drawn from a single answer.
The list of false information
We itemize what the model knows about you incorrectly. The fix is planned from this list.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
Our closed-book battery: web-off, fixed-question measurement per model and per language, across six dimensions, with ranges.
- Six separate measurement dimensions: recall, primacy — how early you're mentioned —, accuracy, detail, consistency, and the context the model presents you in.
- Results per model and per language: the model may know different things about you in Hungarian and in English; you see both separately.
- A confidence range on every result: shows how firm each value can be considered.
- A background picture of your online footprint: we show how much trace of you exists in the sources models can learn from. This is context — not a score.
- Trends over time on the dashboard, plus a detailed report: you get a cost estimate before every measurement, and the audit starts only after your approval.
| Model A | Model B | Model C | |
|---|---|---|---|
| Hungarian | 32–51% | 18–39% | 4–19% |
| English | 41–63% | 9–27% | 22–44% |
Illustrative example: unaided-recall results always appear as a range — never as a single combined score.
Reports are delivered in Hungarian and English.
◇ marks illustrative data — example readouts, not measured client results.
It measures what the model knows on its own — not what it finds
Most measurement examines live search results. We measure the deeper layer: the picture of you living in the model's own, previously acquired knowledge.
Brand-research method, applied to AI models
We ask first without the name, then with it, always in the same order. The same logic has long been standard in human brand-recall research.
No misleading, merged "memory score"
A single number would blur distinct phenomena, so we show all six dimensions separately, each with a confidence range.
Results stay precisely comparable later
The Release Audit uses the same fixed question set, so a new model generation's result is genuinely comparable to the baseline.
Surfaces that use live web search — like Perplexity and Google AI Overview — are deliberately not measured here, because they don't reflect the model's own trained knowledge. The Model Memory Audit delivers the diagnosis; the fix is the work of the Entity Anchor and the Corpus Campaign, and the outcome is re-measured by the Release Audit. ---
- How much does the model memory audit cost?
- From €365 (net). There is no market price for this measurement because there is no market product: to our knowledge we are the only ones measuring trained model memory in productised form. The base price covers one language and three models; more languages or models are a fixed add-on.
- What do I receive?
- A recall matrix broken down by model and language: what the AI knows about you with no web access, from memory alone. Alongside it, the false-claim list — what models repeatedly get wrong about you — and your corpus footprint: where your sources are present.
- What does closed-book measurement mean?
- We query the model with web access disabled: it can only rely on what it memorised during training. This matters because that knowledge works even when the AI does not search — and once embedded, it persists across model generations.
Curious what AI says about you?
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