K2 · Stage 3 — Anchor: the entity
Entity Anchor· KEOone consistent, unambiguous machine identity
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 definitionDoes the model confuse you with someone else, or attach wrong data to you? The Entity Anchor builds a consistent, unambiguous, verifiable picture of you in the data sources machines read.
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.
Machines don't store a mere company name — they store an unambiguously identifiable actor: an entity. If your data differs from place to place, your Wikidata item is incomplete, or the profiles about you aren't linked to each other, the model can become uncertain — and may even confuse you with another organization.
The Entity Anchor intervenes at the source of the errors. It uncovers what's missing for unambiguous machine identification, then carries the fixes through a controlled workflow. Part of this is the sameAs reference web linking your profiles: these references tell machines that the various records and profiles belong to the same company.
If an item fails verification, it returns to drafts. Every step and change remains traceable.
We assess the completeness of the Wikidata item against pre-defined, verifiable requirements. We state precisely when the item counts as done: are the core facts there — founding year, registered seat, official website — and are the claims backed by proper sources? Not generic advice: an explicit target.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
A controlled remediation pipeline: Draft → Review → Publish → Verify → Proven, every step logged — humans edit, the system proves.
- A consolidation workbook: shows what's missing, what the target state is, and the order worth working in.
- A fix log: the status, history and supporting reference of every change, traceable in one place.
- A verification report: on a shareable online surface you see which items are proven and which are still in progress.
- Optional before–after comparison: we show how often your brand name and your category name appear together on the web. This is a raw count and a directional signal — not a statistically precise metric.
Reports are delivered in Hungarian and English.
Verified implementation instead of a simple to-do list
Every fix moves through defined steps and can only be closed with evidence. We don't say it's done — we show the proof.
You can see exactly when it counts as done
The targets are itemized and verifiable, so you too can see unambiguously when the work is complete.
Humans edit, the system verifies
The system never edits Wikidata automatically: every edit is made by a person. This is a deliberate ethical and quality decision — knowledge bases may treat automated mass editing as harmful interference.
The Entity Anchor performs, documents and verifies the necessary fixes; by itself it does not measure impact. The co-occurrence of your brand and category name is a raw count, suitable for direction only. We do not promise that a brand will enter AI models' training data. Whether the AI's knowledge of you has changed is re-measured by the Release Audit on the new model generation. The work requires the results of the existing audits and the company's approved facts. ---
- How much does entity anchoring cost?
- The canonicalisation workbook and fix plan starts from €440 (net). The done-for-you variant — where we create the records and follow them through to verification — starts from a further €525, because record work is manual, human-intensive work.
- What do I receive?
- Submission-ready entries and execution instructions: a Wikidata record with sourced claims, a sameAs network, and a status machine that tracks the process through to verification. The goal: your name resolves unambiguously and machine-verifiably to your company.
- How is this different from "knowledge panel" services?
- Elsewhere this is typically an element of a premium monthly retainer, stretched over months. Here it is a one-off, closable project: the records get created, submitted, and the status machine follows them to verification. A project, not a subscription hook.
Curious what AI says about you?
Start with the free entry check — no commitment.
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