AVE Studio · AI Visibility Engineering
K4 · Stage 4 — Prove: the delta at every model generation — the flagship of the product family
Release Audit · KEO re-measurement timed to model releases
The verified, canonical, readable, citable record AI models learn from — so each new model is trained to know and recall your business correctly.
Knowledge Engine Optimization: the definitionHow do you know the work done on the AI's memory actually paid off? At every significant model release we re-measure Model Mindshare, and if a gap opens, we name the next fix we ship.
The machine-equity loop — Release Audit
- 1 · MEASURE — “Where do I stand today?” Per engine, with a range (Citation Tracker, Selector)
- 2 · FIX — “Why do they skip me?” Page by page, then fixed in code (Gap Analysis, Gap Closure)
- 3 · ANCHOR — “Where does the machine learn about me?” An unambiguous entity, present where models learn (Entity Anchor, Corpus Campaign)
- 4 · PROVE ↺ — “Did it move, and what did it bring?” 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.
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.
Machine equity: the definitionThe results of the Entity Anchor and the Corpus Campaign can only be judged credibly if we can show: did anything change? That can be meaningfully examined when a new model version ships — a new GPT, Claude or Gemini generation — because a model's own trained knowledge only refreshes with a new generation. The Release Audit is built for exactly that moment.
Watching for new model versions
The system automatically and free of charge signals when a significant new model appears, and offers a re-measurement. A paid measurement never starts automatically.
Measurement only after approval
After your approval, we run the web-off measurement on the new model — with exactly the same fixed question set as the K1 baseline.
Change, and comparison against competitors
We compare the result with the previous model generation. Optionally we measure competitors too: their movement helps show what would have happened without any intervention.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
A release watcher and re-measurement system: automatic alerts on new model generations, same-question delta measurement with a competitor panel.
- A shareable Release report: shows the changes per model and per language.
- An itemized change list of false information: what the model newly learned, what it forgot, and what new errors appeared.
- Optional competitor comparison: helps separate the market's general drift from your own result.
- A long-term trend on the dashboard: generation by generation, you can follow how the AI's knowledge of you evolves.
Reports are delivered in Hungarian and English.
By our market analysis, a unique method
Re-measurement structured uniformly and tied to model releases is, by our market analysis, unique. The outcome of work on AI memory can only be judged from repeated measurement like this.
Comparison on identical footing
The baseline and the re-measurement use the same question set, so the results are genuinely comparable.
The system claims no more than the data shows
If the measurement ranges overlap, the result reads: "no demonstrable change." If competitor data is insufficient, we say: "not enough data for a reliable claim."
You pay only when there's something to measure
Paid measurement is tied to the release of a new model version. We charge no ongoing monthly fee in periods with no new, meaningfully measurable model generation.
How much does the release audit cost?
From HUF 90,000 net (approx. €225) per model release. The annual pass starts from HUF 280,000 net (approx. €700) with at least three release audits and a trend report — cheaper from three major releases up, and it makes an unpredictable release calendar plannable.
What do I receive?
A before–after comparison on the new model generation: what the old model knew about you from memory versus the new one — with a competitor control group, so your own movement separates from the market's. Delivered within days of the release.
Why re-measure at a model release?
Because a new model generation's memory is built from the text corpus that existed before the cutoff: what was embedded by then, it knows — what was not stays out for months or years. The recall delta with a control group is the only honest proof that corpus work actually worked.