Avouch

//.entity_foundations

First, the engine has to know you.

Before it can recommend you, the engine has to be sure who you are.

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FIG. 01What AI says

User prompt: Tell me about Harbourview Wealth.

ChatGPT
Harbourview Wealth is a Surrey-based wealth manager specialising in multi-generational planning for business-owning families, regulated by the FCA. …

★ ONE FIRM, ONE SET OF FACTS

Illustrative example — not a real client
//.the_problem_001

An engine can't recommend a firm it can't pin down.

If the engine isn't sure whether you're you — or confuses you with another office, another adviser, another firm of the same name — it plays safe and names someone it's certain about. Most firms have never been made legible to a machine. That's the first gap we close.

//.how_we_work_002

How we work

  1. Build the Entity Home

    One canonical page becomes the single source of truth about your firm — the page every other mention points back to.
  2. Work you into the graph

    We get your firm into the knowledge graph and the entity records the engines draw on, so the facts they hold about you are yours.
  3. Make the facts agree

    Your name, location, regulatory status and key people read the same everywhere an engine looks — your site, Companies House, LinkedIn, directories.
//.what_you_get_003

What you get

Your Entity Home

One canonical, machine-legible page that defines the firm — the anchor for every other signal.

Knowledge-graph presence

A clean entity record in the graph engines rely on; panels usually surface in two to three months.

Schema that confirms

Structured markup that only ever states what a human can already see on the page — never invented (the Kalicube rule).

//.the_work_004

The work, in order

  1. Entity check

    Where the engines are unsure, wrong, or silent about who you are — measured, per engine.
  2. Home + schema

    We build the canonical page and mark it up in the format machines read.
  3. Cross-platform reconciliation

    We line up the facts across every source the engines cross-check, so nothing contradicts.
//.the_framework_005

Entity Home → Knowledge Graph → Consistency

Identity is built in three moves: one home, a record in the graph, and the same facts everywhere — in that order.

Entity Home

One canonical page: one firm, one home, one set of facts.

Knowledge Graph

A clean record in the entity sources engines draw on before they answer.

Consistency

The same name, status and people, everywhere the engine cross-checks.

//.how_we_differ_006

How we’re different

DimensionAvouchTypical agency or tool
SchemaOnly ever confirms what's on the pageStuffed with claims the page doesn't back
Source of truthOne canonical Entity HomeFacts scattered across pages and profiles
ConsistencyReconciled across every source engines checkLeft to drift, profile by profile
//.the_tooling_007

Tracked on the Citation Intelligence Platform.

The platform measures whether the engines can identify you cleanly — and flags, per engine, where they're still unsure or stating something untrue.

See the platform →

Usually the first layer of the retainer — the foundation everything else is built on.

//.questions

Foundations questions

What is an “Entity Home”?
One canonical page that is the definitive source of truth about your firm — who you are, what you do, who you serve, your regulatory status. Every other mention points back to it, so the engines have one place they trust.
Isn't this just schema markup?
Schema is part of it, but it's the smallest part. The work is making your identity consistent and verifiable everywhere an engine looks — schema only ever confirms what's already visible on the page.
How long until a knowledge panel appears?
Often two to three months for a panel to surface; full maturity takes longer. We measure it so progress is visible rather than promised.

AI can't recommend what it doesn't know.

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