AVE Studio · AI Visibility Engineering
K2 · Stage 3 — Anchor: the entity
Entity Anchor · KEO one 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.
Knowledge Engine Optimization: the definitionDoes the model confuse you with someone else? We build the entity architecture: a machine-readable record in your own codebase, corrected external registries, with verifiable identifiers.
The machine-equity loop — Entity Anchor
- 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 definitionMachines 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.
How much does entity anchoring cost?
The canonicalisation workbook and fix plan starts from HUF 175,000 net (approx. €440). The done-for-you variant — where we create the records and follow them through to verification — starts from a further HUF 210,000 net (approx. €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.
Why anchor now? While your data says different things in different places, the model either stays silent about you or says something else — and a wrong picture does not fix itself: the next model generation learns again from the same sources. The fix is one-off, closable work; the delay burns the same error into every new model generation.