Crisis preparation is among the highest-leverage retainer work we do because the value is asymmetric: small investments in the steady state pay back enormously when a situation hits. The components are well-established. A current, well-sourced Wikipedia article (so the journalist’s background-research stop is accurate from minute one). A maintained Knowledge Panel and Wikidata entity (so the engines have clean facts to anchor to). A robust set of owned properties – corporate site, executive bios, foundational explanatory content – ready to rank when search demand spikes. Continuous monitoring through IMPACT™ and AIQ™ so the situation is detected at source rather than at amplification. And a documented playbook with named decision-makers, drafted holding statements, and pre-aligned legal and PR coordination. None of this is exotic; what differentiates clients who handle crises well is that the infrastructure was in place before the call came in.

Legal coordination is built into how we operate on sensitive engagements, and the closer the coordination, the better the outcomes for both workstreams. We routinely sit on joint calls with general counsel and outside firms, align narrative pacing with litigation timing, share factual research that informs both legal and reputation strategy, and support legitimate legal escalation paths (defamation analysis, takedown requests under specific statutory or platform-policy grounds, regulatory communications). The lines we do not cross are the obvious ones: we do not edit Wikipedia in ways that would compromise an active legal matter, we do not push content that misrepresents factual positions counsel is asserting, and we do not undertake removal work that requires legal authority we do not have. Most engagements that involve counsel are stronger for the coordination than they would be running parallel.

Link building in the modern sense – generating placements in outlets that strengthen entity authority and feed both Google and AI engines – is a different discipline than the bulk-link tactics that defined SEO in the 2010s. The Google updates that punished those tactics have only become stricter, and AI engines explicitly weight source authority in a way that makes low-quality links irrelevant or counterproductive. Our approach focuses on placements in outlets that the engines actually weight: trade press in the client’s vertical, mainstream business and financial press, academic and research publications where relevant, industry analyst content. Each placement strengthens the entity layer when it links to authoritative entity infrastructure (the company’s Wikidata, Wikipedia article, structured-data-enriched pages) rather than just the homepage. Bulk link campaigns, link exchanges, and low-quality directory submissions are not part of the playbook.

Google analysis starts with breadth and ends with leverage. IMPACT™ runs queries across the client’s defined keyword set (executive names, brand names, key products, key controversies, key industry terms) across the geographies and languages the audience actually searches in. Every result that appears is classified along several dimensions: ownership (client-owned, earned, third-party, hostile), SERP feature (organic, AI Overview, People Also Ask, knowledge panel, image, video, news), topical relevance to the query intent. The output is a map of where the SERP is solid, where it is exposed, and where the leverage is highest. From there the remediation plan addresses three layers in parallel: content (filling gaps with authoritative pages), entity (Wikidata, schema, sameAs links, Knowledge Panel), and authority (third-party coverage that wins citation slots). The plan ties to monthly reporting tracking specific keyword-level outcomes.

A material share of our work flows through PR, public affairs, and investor relations partnerships, and we have operated this way for two decades. The major global PR firms, the boutique strategic communications shops, and the law-firm-affiliated communications groups refer clients to us for the work that sits outside their core practice: Wikipedia work, AI reputation programs, source-layer Google work, and entity optimization. The partnership format varies. White-label engagements run under the firm’s brand with us in the background; named-specialist engagements run with us as the visible technical partner alongside the firm’s account team; co-pitch engagements involve us on the prospect’s pitch as the AI or Wikipedia capability. The arrangement is shaped to the firm’s client relationship and confidentiality preferences. The relationships are durable because the disciplines are genuinely complementary rather than overlapping.

Suppression as a primary strategy is brittle and increasingly ineffective. The 2010s playbook of pushing positive content into the top ten to push negative content out of the visible range worked when SERPs had ten organic blue links and ranking shifts were the only variable. Today’s SERP has AI Overviews at the top, People Also Ask boxes, knowledge panels, video carousels, image packs, and shopping results – and the ranking algorithms are far harder to game with light-weight content. The durable strategy is content promotion: build authoritative content the engines genuinely prefer, host it on properties with real authority, support it with structured data and entity signals, and let it earn rank rather than try to inflate it. The same content also serves AI engines and Wikipedia citation, so the work compounds across layers rather than just shifting positions on a single SERP. Suppression-only programs we have inherited from prior firms almost always need to be replaced with promotion-led work to hold over time.

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.