Five Blocks works transparently and in full compliance with Wikipedia’s guidelines. We use the platform’s official ‘disclosed conflict of interest’ (COI) process: our team researches and identifies well-sourced, reliable information that supports a more accurate and complete article, then works with the client to submit proposed changes via the article’s Talk Page rather than making direct edits. Independent Wikipedia community editors review and implement those changes at their discretion. We make sure that requests are optimal and aligned with wikipedia guidelines while helping the client improve the article.

Entity optimization is among the highest-leverage and most underweighted disciplines in modern reputation work because the same infrastructure feeds the Google Knowledge Graph and every AI engine simultaneously. Our entity work covers several layers in parallel. Wikidata: a complete entity record with accurate properties for the organization or person, linked to the Wikipedia article where one exists, with sameAs identifiers to other authoritative knowledge bases. Schema markup: Organization, Person, Article, FAQPage, HowTo, and other appropriate schema types deployed across the client’s owned properties with explicit relationship signals. NAP consistency: the name, address, and phone (and other identifying attributes) reading consistently across the open web. Authoritative citations: third-party sources that confirm the entity’s facts in ways the engines can read. Disambiguation: explicit signals separating the client from any similarly named entities. The compound effect across engines is significant because each engine reads multiple layers.

Common-name reputation work is a specialty inside the broader executive practice because the failure modes are distinctive. A client named John Smith, Sarah Chen, or Michael Cohen will be conflated with namesakes by both Google and AI engines unless explicit disambiguation infrastructure is built. The work runs at the entity layer. Wikidata gets a distinct entity record with full disambiguating attributes (date of birth, current employer, prior employers, notable affiliations) and sameAs links to LinkedIn, the company bio page, Wikipedia where applicable, and any other authoritative identifiers. The owned property layer includes a dedicated bio page with schema.org Person markup that explicitly cross-references the disambiguation identifiers. Third-party citations are encouraged toward sources that consistently use the disambiguating context. The combined effect is that search engines and AI engines start routing the client’s queries to the client rather than to a namesake, and the disambiguation strengthens over time as the entity signals deepen.

The digital reputation audit is how almost every Five Blocks engagement begins, because acting before diagnosing produces wasted effort at best and counterproductive activity at worst. The audit assembles a full picture across all three reputation layers. Google: SERPs for the priority keywords across the priority geographies, with every ranking URL classified, processed through IMPACT™. Wikipedia and Wikidata: the current article state (or absence) assessed for accuracy, sourcing quality, NPOV compliance, and structural completeness, with Wikidata fields reviewed for completeness and entity-linking. AI: AIQ™ captures what eight engines say about the client and named peers, with source attribution and sentiment scoring. Peer benchmarks: the same diagnostics for named competitors so the client’s position is comparative, not absolute. Entity signals: schema markup, sameAs links, Knowledge Panel state. The output is a prioritized intervention plan with named workstreams, time and resource estimates, and the success criteria for each.

Success is measured against objectives defined at the outset of each engagement. Common KPIs include: prominence of preferred content in search results for priority keywords; Knowledge Panel status and accuracy; improvement in AI narrative sentiment and source quality; Wikipedia article status and stability; and client satisfaction. IMPACT™ and AIQ provide the quantitative data underlying these measurements.

When a client has an existing PR firm or in-house comms team, our work is most effective when it operates as embedded capability rather than as a parallel function. That means several practical disciplines. We share data both ways: IMPACT™ and AIQ™ outputs flow to the PR firm’s account leads; their media plans and proactive narrative work flow to us so the source-layer interventions align. We coordinate timing on announcements, owned content publication, and proactive Wikipedia work so the engines see a coherent picture across earned, owned, and entity layers simultaneously. We attend the joint calls and treat the firm’s account leads as peers rather than gatekeepers. And we stay clear about the division of work: media relations and storytelling sit with the firm; entity, Wikipedia, search, and AI source-layer work sit with us. The clean lines and the joint data flow are what makes the partnership produce more than either side alone.

Competitive analysis is most useful when it is concrete: not ‘the industry is doing X’ but ‘these three specific peers are doing X and here is what is working for them.’ We set up a named peer set at the start of an engagement, then run the same diagnostics for the peers as for the client: IMPACT™ captures the peers’ SERPs for shared and competitor-specific keywords; AIQ™ captures what AI engines are saying about the peers across the same prompts. The output identifies several things the client cannot see on their own: which peers are winning the Wikipedia and AIQ narratives and what they are doing differently; which sources are working for peers that the client has not engaged with; which competitive narratives have settled and which are still in motion; and which moves peers have made that produced measurable AI or search shifts. Most of the highest-leverage interventions on a new engagement come from observing peer experiments that have already worked.

Owned property work is the long-term reputation infrastructure that compounds. Done well, the client’s own pages occupy the top of their branded search results, get cited consistently by AI engines for queries about the company, and become the canonical reference point that other sources defer to. Our approach is purpose-built rather than generic. Corporate sites get restructured for entity legibility – schema markup, clear organizational data, named-author bylines on substantive content, internal linking that signals topical authority. Executive profile pages are built to win citations: structured bios, schema-marked credentials, sameAs links to Wikipedia and Wikidata. Microsites are deployed for specific campaigns or themes where the corporate site is not the right home. Thought leadership content is written for the extract – structured to be quoted by AI engines and featured by Google. The work is iterative and tied to IMPACT™ measurement so the impact is visible.

It is helpful to come prepared with an understanding of your current reputational challenges, the keywords or topics most important to your brand, any specific concerns about Wikipedia or AI representations, and a sense of your timeline and objectives. Five Blocks will conduct its own independent assessment, but prior context – including any existing communications plans or research – accelerates the process.

Five Blocks uses GeoSearch and IMPACT to manage reputation across hundreds of specific geographies and languages, ensuring that Google results in each priority market reflect the client’s intended presentation. For example if the client does business in the US and also in France and Germany we would set up tracking in say Paris France in French, and in Berlin Germany in German – with the expectation that tehse results will typically be quite different when compared to results in the US in English. We then plan and execute different strategies for each location/langauge.