Here is how a disclosed COI engagement works:

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1. Create a disclosed account

We set up a Wikipedia account that publicly identifies who it represents, for example “Bluedolphin is working on behalf of ACME and will be making suggestions to improve the current page.” Wikipedia policy requires anyone paid to contribute to disclose their employer, client, and affiliation. That disclosure is not optional; it is the rule that makes the rest of the work legitimate.

2. Research well-sourced changes

Our team identifies accurate, well-sourced information that supports a more complete article. Wikipedia is built from reliable, independent secondary sources, so every proposed change is tied to citations that meet the platform’s standards rather than to the client’s own materials.

3. Submit through the proper channel

Rather than editing the article ourselves, we propose changes on the article’s Talk page, or, for a new article, through Articles for Creation. Wikipedia directs paid parties to do exactly this: editors with a conflict of interest are strongly discouraged from editing affected articles directly and are asked to propose changes on talk pages so they can be peer-reviewed.

4. Independent editors decide

Independent Wikipedia community editors review the proposal and implement it at their discretion. We make sure each request is well-sourced and aligned with Wikipedia’s guidelines, but the decision to publish rests with the community, not with us and not with the client.

The article improves through Wikipedia’s own editorial process, which is why the changes hold rather than get reverted.

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How the partnership operates day to day

Bidirectional data sharing

IMPACT™ and AIQ™ outputs go to the PR firm’s account leads so they have a live read on search and AI representation. In return, the firm’s media plans and proactive narrative work come to Five Blocks so source-layer interventions line up with what is being placed and pitched.

Coordinated timing

Announcements, owned content publication, and proactive Wikipedia work are timed so the earned, owned, and entity layers move together. The engines then get a consistent picture instead of a fragmented one.

Joint calls

Five Blocks attends the joint calls and treats the PR firm’s account leads as peers rather than gatekeepers. Senior advisors are present when strategy is set, not only during execution.

Clean division of work

Media relations and storytelling sit with the PR firm. Entity optimization, Wikipedia strategy, search management, and AI source-layer work sit with Five Blocks. The lines are explicit at the start of every engagement and revisited when scope expands.

Why the structure matters

When these disciplines run in silos, each team executing without visibility into the other, the engines get conflicting signals and both workstreams lose leverage. The shared data flow and clean division of labor are what let the partnership do more than either side working alone.

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How AI forms its understanding of a brand

Major AI engines answer questions about an entity in two ways that work together:

• Training data, the corpus a model was built on (web pages, news, books, Wikipedia, and structured datasets), fixed at the model’s training cutoff.
• Real-time retrieval (RAG), live web content the engine fetches at query time instead of relying only on its training set. Perplexity, ChatGPT Search, and Google AI Overviews all rely on retrieval.

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The sources we strengthen

When an AI engine answers a question about a company, it pulls from sources across that company’s digital footprint. Five Blocks focuses on making each of these present and reliable:

• Wikipedia, which major engines use as a reference for entity questions, from both training and retrieval.
• Owned web properties, the brand’s own sites, ideally with schema markup and clear authorship.
• Authoritative third-party coverage, earned media that engines cite as sources.
• Structured data, schema markup and entity links that help engines identify and connect the entity correctly.
• Wikidata, which links entities across languages and feeds knowledge-graph systems.

Why this changes what AI says

Well-anchored content can enter the source pools AI engines draw on, and that changes their answers. By improving the quality and consistency of these sources, Five Blocks works to change how AI platforms represent clients over time, both the accuracy of the facts and the sentiment of the framing.

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What proactive reputation infrastructure looks like

A well-prepared entity has four layers in place before a crisis occurs:

• Wikipedia article, where notability supports one: a well-sourced, accurate, NPOV-compliant article that ranks in branded searches and feeds the Knowledge Graph and AI training corpora.
• Optimized Knowledge Panel: a complete, accurate panel populated through Wikidata, schema markup, and third-party citations, so Google and AI engines have a canonical structured reference for the entity.
• Authoritative owned content: corporate site pages, executive bio pages, and thought leadership content structured for machine extraction, ranking for priority queries and serving as citation targets for AI engines.
• Consistent entity signals: sameAs links, NAP consistency, and disambiguating identifiers across the open web, so the entity is legible to every engine that matters.

The layers reinforce each other. Wikipedia feeds the Knowledge Panel. The Knowledge Panel signals legitimacy to AI engines. Owned content fills SERP positions and provides AI citation targets. Entity signals connect the picture. When a crisis hits, this infrastructure is already in place, and engines have been reading it for months or years.

What reactive crisis response looks like, and where it falls short

Reactive work during a crisis is necessary, and Five Blocks does it. But it starts from a weaker position:

• Negative content may already be entrenched. Search engines and AI models weight content that has ranked or been cited for a sustained period over content that appeared yesterday. Building a counter-narrative from scratch during a crisis means competing against that established position.
• The work takes longer to show results. Durable Google work, earned rank, citation patterns, and entity signal reinforcement typically takes months, not weeks. Crisis pressure compresses that timeline but does not remove it.
• The cost is higher. Doing in weeks what should have taken months requires more resource, more parallel execution, and more senior attention. Infrastructure that could have been built methodically over six months now has to be stood up at higher intensity.
• AI narratives lag corrections. AI engines update on their own crawl and training schedules, not on the client’s timeline. A correction to Wikipedia or a new authoritative placement may take weeks to reach the engines’ outputs, and that lag hurts more when a crisis is live.

Proactive vs reactive: the main differences

The practical implication

For most organizations, the best crisis management investment is building the proactive foundation before a crisis occurs. Organizations that arrive at Five Blocks mid-crisis almost always wish they had started earlier. Those that have invested in the proactive layer, even partially, come through crises faster, at lower cost, and with less lasting damage to their search and AI representation.

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The three partnership formats

White-label

Five Blocks works entirely in the background under the referring firm’s brand. The client relationship and all communications stay with the PR firm; Five Blocks delivers the specialist work without a named presence on the engagement.

Named specialist

Five Blocks appears as the technical partner alongside the firm’s account team. The PR firm owns the client relationship and narrative strategy; Five Blocks owns Wikipedia, AI reputation, entity, and search execution.

Co-pitch

Five Blocks joins the PR firm on a new-business pitch as the AI or Wikipedia capability. The arrangement is shaped to the firm’s existing relationship with the prospect and their confidentiality preferences.

Why the partnerships work

The disciplines complement each other rather than overlap. Media relations and storytelling sit with the PR firm; entity optimization, Wikipedia, search, and AI source-layer work sit with Five Blocks. That division of labor, plus a shared data flow through IMPACT™ and AIQ™, means the two firms together deliver more than either would alone.

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The underlying risk is documented: large language models mishandle broad classes of human names even in short text because the linguistic cues are ambiguous. Weak entity infrastructure, no Wikidata entry, no schema markup, no clean disambiguation, makes that conflation far more likely.

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The disambiguation infrastructure

1. Wikidata entity record

   Five Blocks creates or completes a distinct Wikidata item for the client with full disambiguating attributes: date of birth, current employer, prior employers, and notable affiliations. Each item carries a unique Q-identifier that machines use to tell this person apart from any namesake. Where a Wikipedia article exists, it is linked as a sitelink on the same item.

2. Owned bio page with schema.org Person markup

   The client’s bio page on the company site is marked up with schema.org/Person and carries sameAs links pointing to the Wikidata Q-item, LinkedIn profile, Wikipedia article (where applicable), and any other authoritative identifiers. Schema.org defines sameAs as “URL of a reference Web page that unambiguously indicates the item’s identity”, which is the disambiguation signal engines rely on. This page becomes an anchor in the client’s identity graph.

3. Third-party citations with disambiguating context

   Third-party coverage, press mentions, bylined articles, industry profiles, is steered toward sources that use the client’s disambiguating context (title, employer, credential) alongside the name. Each citation that does so raises the engine’s routing confidence.

4. Wikipedia (where notability supports it)

   Where the client meets Wikipedia’s notability threshold, a Wikipedia article is the strongest single disambiguation signal available. Wikipedia’s own disambiguation system uses dedicated disambiguation pages and unique identifiers to resolve conflicts between subjects who share a name. An article linked to the Wikidata item ties all the layers together.

How the signals compound

Once the infrastructure is in place, search engines and AI engines route the client’s queries to the client rather than to a namesake. Person schema with verified sameAs links helps engines build an identity graph and gives them biographical disambiguation anchors. The disambiguation gets stronger as the entity signals deepen and more citations adopt the consistent context.

The post How does Five Blocks handle reputation management for individuals with common names? appeared first on Five Blocks.

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How engagements typically start

Most clients engage us first for a specific situation: a crisis, an audit, a Wikipedia article, or a contested executive narrative. They then continue on retainer once that work is stabilized.

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How IMPACT™ benchmarks search

Same keyword sets, same geographies

IMPACT™ runs the client’s priority keyword set against each named peer across the same geographies and languages, so every comparison is side by side rather than an isolated snapshot.

What gets compared

Search position for shared queries, share-of-voice across the keyword set, and SERP composition, meaning which owned, earned, third-party, and hostile URLs appear for each brand.

How leverage is identified

The comparison shows which SERP positions peers are winning that the client is not, and which peer moves produced measurable search shifts.

How AIQ™ benchmarks AI narratives

Same prompts, same engines

AIQ™ runs the same prompt set against the eight AI engines AIQ currently tracks, ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode, for the client and for each named peer.

What gets compared

AI narrative themes, the sources each engine cites, sentiment, and prominence across engines. This shows which peers are winning the AI narrative and which sources drive that outcome.

Why the gaps matter

AI engines often describe peers differently even for closely related queries. Those gaps point to the source relationships and narrative themes the client should prioritize.

How benchmarks are reported

• The combined peer view appears in every monthly report alongside the client’s absolute metrics.
• Relative positioning usually moves internal action faster than absolute numbers, because the board, allocators, or recruiting committee cares about competitive position, not the raw score.
• Benchmarks also surface peer experiments that have already worked, source moves, content investments, Wikipedia changes, so the client gets a set of validated interventions rather than untested hypotheses.

The post How does Five Blocks benchmark my brand’s reputation against peers? appeared first on Five Blocks.

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The four disciplines applied to every piece

1. Scoped to the gap. Each piece targets a specific keyword and SERP feature, or a specific AI prompt and engine response pattern. Content produced without that anchor rarely moves either one.
2. Hosted where the citation counts. The publishing property is chosen for where it will earn authority: an owned site, an executive bio, a microsite, or an authoritative third-party outlet via placement. Search and AI engines weight sources by credibility, so a single citation from an authoritative independent outlet shapes AI answers more than several additional owned pages.
3. Structured for extraction. Every piece is formatted so engines can pull a clean answer from it: H2 and H3 headings framed as questions, short direct answers at the top of each section, lists where lists are warranted, schema markup (FAQPage, HowTo, Article, or Person/Organization as appropriate), and named authorship with bio context. AI engines extract answers more efficiently from dense, well-organized content with schema markup than from long pages where the answer is buried. They also weight content written by named experts with credible bios, because identifiable authorship signals expertise.
4. Checked against the diagnostic. At the next review cycle, each piece is judged against the gap it was created to fill. Content that does the reputation work it was built for gets that verification; content produced for word count rarely does.

What this means for AI engines

Retrieval-based AI engines such as Perplexity, ChatGPT Search, and Google AI Overviews assemble answers from underlying source content rather than editable model outputs. They pull most readily from content that states facts plainly, is organized into clean answerable units, and carries schema markup. This is the same discipline that wins featured snippets and People Also Ask boxes; the selection logic largely overlaps. Content built to this standard works for both.

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Baseline reputation monitoring

IMPACT™ and AIQ™ keep the team informed about search-result composition and AI-engine representation without consuming internal headcount. Threats and opportunities show up in continuous data rather than periodic audits.

Crisis preparation and response

Crisis preparation builds the infrastructure before a situation arrives: Wikipedia article, Knowledge Panel, owned properties, and monitoring coverage. Crisis response activates within hours when one does, with a diagnostic assessment, tactical plan, and accelerated execution running in parallel from day one.

Executive visibility

CEO and senior leadership reputation is managed as a structured discipline: entity layer, Wikipedia strategy, owned-property buildout, AI narrative monitoring through AIQ™, and Google SERP management for the executive’s name and associated topics.

Employer brand

AIQ™ tracks what AI engines tell job candidates about the company as an employer. Research shows 54% of candidates ask AI to evaluate whether a company is worth pursuing before applying. Five Blocks identifies the sources shaping that AI narrative and advises on the content and source work to improve it.

Narrative coordination

This is the connecting work across the program: aligning what owned content, earned coverage, and AI-engine representation say about the same themes. Five Blocks shares IMPACT™ and AIQ™ data with the PR firm and in-house team, coordinates timing on announcements, and keeps the entity layer in sync with the communications strategy.

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