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GEO
Entity & Schema Optimization
Make your brand a machine-readable entity engines trust.
Overview
What we do & why it matters
AI and search engines reason about entities, not just keywords. We build your entity — consistent structured data, knowledge-graph signals and disambiguation — so engines understand exactly what you are and cite you confidently.
What’s included
- Entity audit & knowledge-graph work
- Structured-data (schema) rollout
- Organization & sameAs signals
- Author & E-E-A-T markup
- DefinedTerm / dataset schema
- Validation & monitoring
Part of our GEO engagement · from transparent pricing.
Why entities
Search engines resolve things, not strings.
Modern retrieval breaks a query into intent, entity types and attributes, then matches candidates on subject–predicate–object relationships. A page that never states its facts explicitly forces the engine to guess what it is about.
One entity, many mentions
Your company, products, people and locations appear across your site and the wider web. Markup is what tells the engine these are the same things.
Attributes decide relevance
Once meaning is matched, completeness wins: price, availability, area served, credentials, review data. Missing attributes lose to complete ones.
Markup must match the page
Structured data that claims what the page does not show is a risk, not an advantage — and it is checked.
Relationships beat keywords
Linking a service to the problems it solves, the people who deliver it and the places it covers builds the graph that retrieval walks.
The result is mundane and valuable: fewer mistaken descriptions of your business in AI answers, richer search results, and pages that rank for the way people actually phrase things.
Deliverables
What you get.
The markup
The schema types that actually earn their keep
Schema has dozens of types and most of them do nothing for a B2B company. The useful subset is small, and the value comes from getting it correct and consistent rather than from covering as much of the vocabulary as possible.
Organization
The anchor for everything else: who you are, what you are called, where you are, and which external profiles are also you. Usually the single highest-value piece.
sameAs links
Explicit connections to your profiles elsewhere. This is how a crawler confirms that several scattered mentions describe one organisation.
Service and Product
What you actually sell, described in terms that match the page. Vague service markup is worse than none, because it invites a wrong classification.
FAQPage
Pairs questions with answers in a form that is trivial to extract. The cheapest structured-data win available on a page that already has an FAQ.
BreadcrumbList
Makes the site's hierarchy explicit, which helps systems understand how a page relates to the section above it.
Person and author
Attaching real, identifiable people to content. Material on credibility questions, and routinely missing on sites that have genuine experts.
Markup must describe what is visibly on the page. Schema claiming things the reader cannot see is a quality problem, not a clever shortcut, and we will not implement it.
Beyond markup
Being understood as a distinct organisation
Schema is how you describe yourself on your own pages. Entity recognition is whether the wider web agrees. The second is harder, slower, and ultimately what decides whether a system can talk about you confidently.
One name, used consistently
Legal name, trading name and the name people search — reconciled, with the variants explicitly connected rather than left to be inferred.
Consistent facts everywhere
Founding, location, size, what you do. Contradictions across sources make a system hedge, and hedged answers do not mention you.
Public reference data
Where your organisation legitimately belongs in open datasets and registries, correctly and completely filled in.
Identifiable people
Named experts with real profiles and real history. Organisations recognised through their people are described more confidently.
Topical association
Being connected to the subjects you want to be known for, built through sustained coverage rather than through a page that asserts it.
Disambiguation
If your name collides with another company, a product or a common word, the work is making the distinction explicit everywhere.
Maintenance
Validation, and keeping it true
Structured data decays. A redesign drops half of it, a CMS migration mangles the rest, and a stale claim stays in place for years. The engagement includes keeping it right, not just shipping it once.
Most of the broken markup we find was correct when it shipped. The failure is almost never the original implementation; it is that nothing watched it afterwards.
Diagnosis
How we find out whether you are currently understood
Before implementing anything, the useful question is whether systems already know who you are and get it right. The answer is usually mixed in a specific, fixable way, and it decides where the effort goes.
Ask the systems directly
Put your company name to each assistant and see what comes back. Wrong facts, a confused answer or a competitor's description is the diagnosis.
Trace each wrong claim
Incorrect statements have sources. Finding which stale listing or outdated article is feeding one is most of the work and makes the fix trivial.
Audit your own contradictions
Homepage, About, LinkedIn, directories and press releases frequently describe the company four different ways. Systems hedge when sources disagree.
Check what markup exists
Validated against the vocabulary and against the stricter requirements of each consuming system, page type by page type.
Name collisions
Whether your name is shared with another company, a product or an ordinary word, which changes the entire disambiguation strategy.
Coverage inventory
Which external sources mention you at all, and whether they are current. Thin coverage is a slower problem than wrong coverage, and a different one.
The diagnosis is part of the free audit, and it frequently finds that the biggest win is correcting two stale external listings rather than anything on your own site.
Honest limits
What schema will not do for you
Structured data is plumbing. It makes good content legible and does nothing for content that is not worth reading.
Improve rankings directly
It is not a ranking factor in itself. It helps systems understand and display a page, which can affect outcomes — that is a different claim and we keep them apart.
Guarantee rich results
Valid markup makes a page eligible. Whether anything is displayed is the platform's decision, and it changes without notice.
Rescue thin content
A page with nothing to say, marked up perfectly, is still a page with nothing to say.
Work when it contradicts the page
Markup describing things the reader cannot see is treated as a quality problem and can cost you more than omitting it entirely.
Stay correct by itself
Without monitoring, it degrades with every redesign and migration. The implementation is the small part; keeping it true is the engagement.
Create an entity from nothing
Recognition is built from consistent presence across the web over time. Markup declares the claim; the rest of the web decides whether to accept it.
Questions
Entity & Schema Optimization, answered.
Why do entities matter for AI?
Engines cite sources they can identify and trust. A clear, corroborated entity makes your brand a confident citation choice.
Is schema enough on its own?
Schema helps machines parse you, but it works best alongside entity authority and consistent signals across the web.
Is schema markup a ranking factor?
Not in itself, and anyone telling you otherwise is overselling. What it does is help systems correctly interpret what a page is about, which page it relates to, and which organisation stands behind it — and correct interpretation does affect how and where a page gets used, including whether an assistant is confident enough to cite it. We keep those two claims separate in reporting rather than blurring them into a single promise.
We already have schema. Does it need redoing?
Usually it needs auditing rather than replacing. The common findings are markup that was correct at launch and has since drifted from the visible content, types missing on the pages that would benefit most, an Organization block that contradicts what the About page says, and validation errors introduced by a redesign that nobody noticed. The audit takes little time and tells you whether the work is a tidy-up or a rebuild.
How long does entity recognition take?
Longer than markup, and it is worth being clear about that upfront. On-page structured data takes effect as soon as pages are re-crawled, typically weeks. Being recognised as a distinct organisation across the wider web is a function of consistent presence accumulating over quarters, and it depends partly on coverage you do not control. We report the two separately so the fast part is not used to imply progress on the slow part.
Do we need a Wikipedia page?
No, and chasing one is usually a poor use of effort — notability requirements are genuine, and attempts to create a page for a company that does not meet them tend to end badly and publicly. There are other legitimate public reference sources where an organisation can be correctly represented, and consistent, accurate presence across ordinary sources does most of the work. We will tell you honestly whether your organisation plausibly meets the bar before anyone spends time on it.
Who implements the markup, you or our developers?
Either, and we are comfortable both ways. We can deliver validated markup with implementation notes for your team, or implement directly where we have access. What we insist on is that it ends up in your templates rather than injected by a tag manager as an afterthought — injected markup breaks quietly, loads late, and is the first thing lost in a migration.
How will we know it is still working in a year?
Because it is monitored rather than assumed. Validation runs on a schedule and after deploys, template changes that drop markup get flagged within days, and external profiles get reviewed against a single source of truth for your organisation facts. Almost all the broken structured data we encounter was correct when it shipped; the failure is that nothing watched it afterwards.
Does this help with anything other than AI search?
Yes, and that is part of why it is worth doing even if you are sceptical about assistants. The same markup governs eligibility for rich results in conventional search, helps your pages be represented correctly when shared, and gives internal systems a consistent description of what each page is. It is foundational work whose value does not depend on any single platform continuing to exist in its current form.
What does this cost to maintain once it is built?
Much less than building it, but not nothing, and we would rather be straight about that than imply it is one-and-done. The ongoing work is validation after deploys, a periodic check that external profiles still match your source of truth, and re-validation when consuming systems change their requirements without announcing it. It is a small recurring line, and skipping it is why most of the broken markup we audit was perfectly correct on the day it shipped.
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