AVE Studio · AI Visibility Engineering
K3 · Stage 3 — Anchor: the corpus
Corpus Campaign · KEO presence in the sources AI learns from
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 definitionGreat content is worthless if it never appears in the sources AI models learn from. We write and place the material on those channels — the practical side of KEO, carried through to publication.
The machine-equity loop — Corpus Campaign
- 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 definitionAI models learn their knowledge of the world from vast text collections called corpora. Every model has a knowledge cutoff: only information available before it can enter training; anything published later can be learned at the earliest by a following model generation. If your brand isn't present in the right sources, the next generation won't necessarily know about it either.
01Placements in media with documented AI licensing agreements
We plan targeted placements in outlets with documented AI licensing deals (AP, FT, Guardian, Le Monde, News Corp, Axel Springer, Schibsted, Reddit — every claim backed by a source link), and on Hungarian surfaces that AI crawlers visit with high frequency.
02Timing aligned to model refreshes
We time publication to the expected refresh rhythm of model generations. We calculate when content must appear to still have a chance of entering the sources processed for the next generation.
03Verification and evidence
We check whether the content appears in the Common Crawl open web archive, and whether — per your server logs — the models' data-collection crawlers, such as GPTBot or ClaudeBot, actually visited the page.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
A corpus-presence verifier: Common Crawl checks and server-log AI-crawler evidence, timed to model refresh rhythms.
- A quarterly campaign plan: with concrete, trackable tasks.
- A placement log: every item documented. We work only with real, earned press and professional presence; no fake profiles, no artificial manipulation.
- An AI-crawler visit report: presents your Common Crawl presence and the server-log evidence of AI crawler visits.
- A closing re-measurement: the work's actual impact is re-measured on the new model generation by the standalone Release Audit.
Here, the honest commitment isn't a footnote — it's the foundation of the service
- We don't promise what nobody can guarantee. Offers promising guaranteed inclusion in AI model training are misleading. Entry into training data cannot be directly controlled and cannot be proven in advance.
- We commit to what can actually be influenced. We build the brand's presence on the best-documented channels, then verify the outcome with measurement that can be checked after the fact. We cite AI licensing only with a source, and crawler visits only with measured evidence.
- The time constraints cannot be bypassed. New content can take effect at the earliest in a following model generation. The gap between a model's knowledge cutoff and its release can itself be months.
- An AI crawler visiting your page does not mean the model has learned the content. A crawler visit is an important early signal — not proof. Real proof comes from re-measurement on the new model generation.
Reports are delivered in Hungarian and English.
How much does the corpus campaign cost?
From HUF 190,000 net (approx. €475) per quarter. Media cost is on top, as a transparent pass-through: we neither absorb nor mark it up beyond a 15% management fee — the middle of the industry's 10–20% band.
What do I receive?
A quarterly placement plan aligned to the model release calendar, a licensed-outlet registry, and crawl evidence: Common Crawl and AI-crawler proof that your placements were actually reachable by the models' collectors.
Do you guarantee I get into the models' knowledge?
No — and anyone who guarantees that is overpromising: nobody controls model training. What we provide is cutoff-aware timing, the right outlets, and evidence that the content was reachable by the collectors. As a matter of principle we take no success fee on outcomes we cannot causally prove.
Why start now? Months pass between a model's knowledge cutoff and the release of the new version — what does not make it into today's corpus will not be there for the next generation either. The campaign is not urgent because we say so; it is urgent because the calendar cannot be waited out.