Protect · 03
Presence Architecture
The structural work: making you an entity that search and AI systems can resolve without guessing. Unglamorous, largely invisible, and the foundation everything else in this practice depends on.
Search and AI systems do not read pages the way people do. They attempt to resolve entities, meaning this person, this company, this role, and then attach information to them. When resolution succeeds, everything published about you accumulates to a single coherent record. When it fails, your work is scattered across several partial identities, or merged with a stranger's.
Most reputational problems we see are, underneath, resolution failures. The same person described with three different job titles across four sources. A company whose legal name, trading name, and brand are never stated together anywhere. Two people with one name and no structural signal distinguishing them. None of that is a content problem, and no amount of publishing fixes it.
The work is deliberately boring. Structured data that states plainly who you are and how you relate to your organization. Consistent representation of names, titles, and dates across every property you control. Explicit connections between your entity and your published work, so attribution actually lands. And explicit disambiguation from the entities you are not.
The reason we treat this as the foundation is leverage. Publishing into an unresolved entity wastes much of its value; publishing into a well-defined one compounds. It is also the least visible work we do, which is why it is so often skipped by firms selling visible activity.
When it applies
The situations this is the right tool for.
Your details differ across sources
Different titles, spellings, or dates in different places. Each inconsistency is a reason for a system to hesitate or split you in two.
Someone shares your name
Structural disambiguation is the only durable fix. Content alone does not separate two entities.
Your published work is not attributed to you
Articles exist but are not connected to your entity, so they accumulate no authority on your behalf.
A company has multiple names
Legal name, trading name, and brand, never stated together, producing three partial entities instead of one.
AI answers about you are hedged or thin
Frequently a resolution problem rather than a content problem, and diagnosing which is the first step.
You are about to publish substantially
Do this first. Publishing into an unresolved entity discards much of the benefit.
Process
How the work runs.
Entity audit
How systems currently resolve you: which identities exist, what is attached to each, and where they have split or merged.
Canonical definition
One authoritative statement of identity: name, role, organization, history, and the entities you are explicitly not.
Structured data implementation
Schema markup on the properties you control, stating relationships in a form machines read directly rather than infer.
Cross-source reconciliation
Aligning representation everywhere it appears. The tedious middle of the work, and the part that determines the outcome.
Attribution linkage
Connecting published work to the entity so authority accrues where it should.
Resolution verification
Confirming systems now resolve you as one coherent entity, tested rather than assumed.
Before you engage
Where the limits are.
Structured data is a statement, not a guarantee. Search systems treat it as one input among many and discard it when it conflicts with what they observe elsewhere. It cannot be used to assert something the rest of the record contradicts.
It also cannot create authority. This work makes existing material legible and attributable; it does not substitute for having material. A perfectly structured entity with nothing attached to it is still thin.
And it requires cooperation across properties you may not fully control, such as a company site, a professional profile, or a co-authored publication. Where we cannot reach something, we will tell you what remains inconsistent.
Questions we are asked
- Is this SEO?
- It overlaps with the technical end of it. The difference in objective matters: SEO aims to rank a page, and this aims for an entity to be understood. Ranking tends to follow from resolution, but chasing ranking without resolution is why so much SEO spend produces nothing durable.
- Can I not just add schema markup myself?
- The markup is the easy part and is widely documented. The substance is the audit, meaning determining how systems currently resolve you and what specifically is causing them to fail, plus the reconciliation work across sources, which is where the time goes.
- How long until this has an effect?
- Systems typically reflect structural changes within weeks to a few months. The effect is rarely dramatic on its own; it shows up as everything else working better, which is an unsatisfying but accurate description.
- Does this help with AI systems specifically?
- Substantially, and it is the highest-leverage intervention available there. Structured, consistent, unambiguous source material is precisely what reduces a model's need to infer, and inference is where invented details come from.
- What does this cost?
- The fee is stated in a written proposal before any work begins, scoped to what the assessment finds. How that is arrived at is set out in full on the How We Work page.
Related
Mechanisms that often run alongside this one.
Is this your situation?
One conversation, in confidence, with an honest reading of whether this mechanism is the right one, including when it is not.