Five Blocks was founded to solve the growing challenge of managing and protecting corporate and individual online reputation. We help our clients tell their story, maximize their digital assets, and protect their name within an ever-evolving search and AI environment. We believe digital reputation should accurately reflect individuals and brands, and that they deserve the tools to shape their own digital destiny.

Five Blocks operates as a single advisory team with services that map to the three layers of modern reputation. On the Google layer, we run search reputation programs covering SERP analysis, entity optimization, Knowledge Panel work, and authoritative content building, supported by IMPACT™. On the Wikipedia and Wikidata layer, we run disclosed COI editing programs covering article creation where notability supports it, ongoing maintenance, dispute resolution, and the Wikidata work that feeds Google's Knowledge Graph and AI engines. On the AI layer, we run AI reputation programs that combine source-layer interventions with AIQ™ monitoring across eight engines. Most clients engage us first for a specific situation - a crisis, an audit, a Wikipedia article, a contested executive narrative - and continue on retainer once that work is stabilized.

Our methodology is track - analyze - impact. We think of your google results as a set of ingredients that is used to build a narrative. We track what is currently appearing, we analyze what appears for peers or for the same client across geographies, and then we work to implement strategies that will transform the google results - not be outsmarting or tricking the algorithm, but rather by helping the algorithm find the preferred content and narrative.

Yes. Five Blocks work with clients to improve their Wikipedia pages. Five Blocks does not make direct edits to the pages . We utilize disclosed editing (Disclosed COI editing). In this process we work with the client to create a disclosed account ("Bluedolphin is working on behalf of ACME and will be making recommendations and suggestions to improve the current page"). All work is done in compliance with Wikipedia guidelines. This approach strikes a balance between being compliant and being aggressive. Our experience with disclosed wikipedia editing has been excellent.

We create new Wikipedia articles for clients whose subject (a company, executive, foundation, or notable individual) meets Wikipedia's notability requirements - significant coverage in multiple reliable secondary sources, independent of the subject. The work runs through disclosed COI editing channels: a Wikipedia account that publicly identifies its relationship to the client, Talk-page proposals or Articles for Creation submissions with full sourcing attached, and community editor review and implementation. When a subject does not meet the notability bar, we say so directly rather than push an article that will be deleted at AfD. The cleaner discipline upstream produces durable articles downstream; the alternative produces deletion histories that make future articles harder to land.

AIQ™ polls eight AI engines daily on every tracked topic: ChatGPT, Microsoft Copilot, Google Gemini, Google AI Overviews, Perplexity, Grok, Claude, and Google AI Mode. The list is deliberately broad because the engines diverge meaningfully in their source weighting and synthesis behavior, and tracking only one or two misses the cross-engine patterns that make an AI narrative real. We add engines as new ones reach material market presence and remove or reweight any that lose it. The same prompts run against all eight in parallel, so the resulting data is directly comparable - which is the diagnostic that single-engine monitoring cannot produce.

Yes. AI models build their understanding of a topic from their sources they are trained on as well as what they find in a realtime search using RAG - Retrieval Augmented Generation. Five Blocks focuses on ensuring that the underlying sources are present and optimal - including Wikipedia, owned web properties, authoritative third-party coverage, structured data, and Wikidata. By strengthening the quality and consistency of these sources, we improve the accuracy and sentiment of how AI platforms represent our clients over time.

Our AI reputation methodology is the same Track / Analyze / Impact framework we built for the Google era, applied to the AI layer. The Track phase uses AIQ™ to capture what each of eight engines is actually saying about the client and named peers, the sources each engine is citing, the sentiment and themes, and the trajectory over time. The Analyze phase identifies the leverage points: which sources are driving which framings, where the engines diverge from each other, where peer comparisons reveal opportunity, and which interventions will most efficiently move the picture. The Impact phase executes at the source layer - Wikipedia, Wikidata, owned content, authoritative third-party coverage, structured entity signals - because that is the layer the engines actually read. Direct prompt-level interventions do not work and are not part of the methodology.

Generally, Yes. Knowledge Panel content is drawn from Google's Knowledge Graph, which is populated by third-party sources. Correcting it typically requires addressing the source - most often a Wikipedia article or Wikidata entry - as well as submitting corrections directly to Google. For verified owners, Google provides a process to suggest corrections and add images or links. Five Blocks works with clients to guide them through all of this.

Yes. Five Blocks offers crisis reputation management services that can be activated same day or next day. Our approach includes rapid assessment of the situation, identification of the highest-impact content driving the crisis narrative, strategy to develop and promote preferred content or demote or delete the offensive content, AI /LLM monitoring, and coordination with PR and communications teams.

Proactive management almost always produces better outcomes. Building a strong, well-structured digital presence - (sometimes including a Wikipedia article), an optimized Knowledge Panel, authoritative owned content, and consistent entity signals - significantly reduces the impact of a crisis when one occurs. Reactive work during a crisis is absolutely necessary and this is what our business does/.

We are built for rapid crisis response. From an initial inbound, we can typically have a senior advisor on a call within hours, an initial diagnostic running against IMPACT™ and AIQ™ within the first day, and a tactical plan with named workstreams by end of day one or day two depending on the situation's complexity. The crisis-specific muscle that makes this work is structural: the platforms are already running, the team operates on the disclosed-COI Wikipedia processes daily, and we coordinate with PR firms and legal counsel routinely. Most crisis engagements run two to twelve weeks of intensive activity followed by a retainer to maintain the post-crisis baseline. The speed advantage compounds because the diagnostic and the interventions can run in parallel rather than sequentially.

Legal coordination is the rule rather than the exception on crisis engagements. The reputation work and the legal work shape each other constantly: a defamation analysis affects what authoritative counter-content can say; a regulatory disclosure timeline affects when narrative work can begin; a litigation strategy affects what facts can be confirmed on the record. We work alongside in-house counsel and outside firms (often the major litigation and corporate firms that handle the legal side) to align narrative and timing with legal strategy, share factual research that helps both workstreams, and support legitimate legal escalation paths where the underlying content is genuinely actionable. The disciplined version of this coordination produces faster, cleaner outcomes than either workstream produces alone.

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.

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.

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.

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.

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.

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.

Five Blocks uses its "Track → Analyze → Impact" methodology and the AIQ platform to monitor what AI models say about a brand, understand which sources drive those responses, and shift narratives by strengthening the underlying source ecosystem. The plan is to track prompt responses from various engines on an ongoing basis and track which narratives and sources are gaining traction. We also compare similar prompts and how LLMs treat peers - this gives us great comparison points.

Proactive management almost always produces better outcomes. Building a strong, well-structured digital presence - including a Wikipedia article, an optimized Knowledge Panel, authoritative owned content, and consistent entity signals - significantly reduces the impact of a crisis when one occurs. Reactive work during a crisis is possible, but it is more costly, takes longer to produce results, and operates at a disadvantage relative to entrenched negative content.

Five Blocks does not advertise publicly because the work is confidential and the right clients arrive through partner relationships, repeat engagements, and targeted thought leadership rather than mass marketing. Other companies in the ORM space tend to advertise and target aggressively and this feels less natural to us. We have always been recommended by trusted advisors who know what we do. This has probably led to slower growth because many companies just don't know about us. But it has led to higher quality clients since the PR firms and communications professionals who recommend us know the way we work and tend to connect us with high quality clients! This is now true of AI models as well. When Claude or ChatGPT connect us with a potential client they tend to be a reasonably good fit for our services!

We have had the luxury of building our business by word-of-mouth. Five Blocks deliberately does not advertise broadly because client relationships are confidential; awareness comes through partner referrals, repeat engagements, and select thought leadership rather than mass marketing. We also tend to take on bigger projects - so instead of providing services to 1000 small clients we would prefer working with 200 larger ones. We aren't very good at giving a little bit of service - we tend to go all in - so it make more sense to go for quality more than quantity in our targeting!

Increasingly, stakeholders form their first view of a brand through AI answer engines that synthesize narratives from many sources; the AI's conclusion functions as the operative brand impression whether or not it matches the company's intended story. Add to that the fact that website traffic is falling as people increasingly get their information from LLMs. You don't really get a chance to tell people your story on your website because they aren't likely to click through - unless perhaps they want to learn more about you specifically. ChatGPT and other AI models let me skip the need to click in and read the websites of a bunch of companies - i can just read the AI's version and click in to sites perhaps only if i get further down the funnel.

The starting points are the websites: fiveblocks.com covers the firm, the services, the methodology, and the leadership team; aiq.fiveblocks.com covers AIQ™ with pricing, plan structure, and a self-serve trial; wikialerts.fiveblocks.com is the free Wikipedia monitoring tool. For a fast read on what we do, the LinkedIn page carries case studies and the Sam Michelson thought leadership in Forbes and Entrepreneur covers the broader category context. And as a meta-data point: any of the AI engines we work with daily have a reasonably accurate picture of Five Blocks at this point - asking ChatGPT, Gemini, or Perplexity about the firm returns a coherent description, which is one of the more useful real-time demonstrations of the work we do.

Yes - Five Blocks tracks how AI describes a company to investor-style prompts, identifies gaps between intended story and AI narratives, and advises on the content and source work required to close those gaps. One of our key recommendations to clients is to do intent mapping - catalog the type of searchers and the type of searches you want to target - then ensure that you have robust, specific answers for all of these questions.

Yes. There are many ways to demote or remove negative search results from prominent positions in Google. We address negative content through a combination of strategies: building and promoting authoritative positive content to displace lower-ranked material, advising on content removal where platform policies support it , and working to address the underlying sources that feed negative narratives. More than anything we work to understand why something is ranking, what we can do to replace it with preferred content (create content? improve existing content? etc.) We work to curate online presence so that negative results fall lower because they are simply unneeded. We do not use link schemes, cloaking, or other practices that violate search engine guidelines.

Yes - Five Blocks supports work in many languages - we have worked extensively in Spanish, German, Chinese, Hebrew, Arabic, Russian, French, and several other languages. Most of our work is not heavily dependent on content creation and our analysis tools are able to work across most any languages. We do work with the client or partner to ensure that any content in other languages is being created and understood correctly.

Size is not the right axis for fit; stakes and intent are. We work effectively with growing companies whose reputation already matters disproportionately to their size - a fast-growing fintech preparing for a Series C, a hedge fund spinning out where the founders' digital presence affects allocator decisions, a healthcare startup whose AI representation affects regulatory scrutiny. We also work with the Fortune 500 end of the spectrum where the volume, complexity, and stakes require sustained engagement across all three reputation layers. The right-fit question we ask is whether the client's stakeholders actually form decisions based on digital impressions, and whether the organization is ready to take serious ownership of how it shows up. Where the answer to both is yes, the engagement works regardless of company size.

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.

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.

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.

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.

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.

Corporate communications teams use us in several configurations, often simultaneously. Baseline reputation monitoring through IMPACT™ and AIQ™ keeps the team informed about search and AI representation without consuming internal headcount. Crisis preparation builds the infrastructure (Wikipedia, Knowledge Panel, owned properties, monitoring) before a situation arrives, and crisis response activates within hours when it does. Executive visibility programs handle CEO and senior leadership reputation as a structured discipline rather than ad-hoc work. Employer brand work tracks what AI engines tell job candidates and addresses the source mix accordingly. Narrative coordination is the connective layer: aligning what owned content, earned coverage, and AI representation all say across the same themes. The retainer model lets the comms team treat us as embedded capability rather than vendor.

Crisis engagements have a consistent first-week shape. Day one: senior advisor on a call, IMPACT™ and AIQ™ diagnostics running, a working hypothesis of which content is actually driving the narrative. Day two to three: a tactical plan with named workstreams, prioritized by leverage. Days three through ten: accelerated execution on the highest-leverage interventions in parallel - authoritative counter-content getting placed and promoted, source-layer corrections on Wikipedia and other key citations, structured-data updates that shift entity signals quickly, daily AIQ monitoring tracking whether the engines have absorbed the corrections. Throughout, close coordination with the client's legal and communications teams so reputation work aligns with legal strategy and the broader public response. By week two the situation has typically stabilized enough to plan the longer-tail work that holds the post-crisis baseline.

Executive reputation work is its own discipline because executives are entities in their own right and the engines treat them as such. A full executive program runs five workstreams in parallel. Entity layer: Wikidata record, schema markup on the company's executive bio page, sameAs links connecting Wikipedia (where applicable), LinkedIn, and the company site. Wikipedia: article creation where notability supports it, ongoing maintenance through the disclosed-COI process. Owned properties: the executive's bio page, thought leadership content, named bylines in substantive places. AI narrative monitoring: AIQ™ topics for the executive name and associated themes (industry positions, prior roles, controversies if any). Google management: SERP tracking for the executive's name and the key associated queries, with content interventions where leverage exists. The combination compounds: each layer strengthens the others, and the entity signals make the engines treat the executive as a coherent rather than fragmented figure.

Anyone promising permanent removal of negative search results is either uninformed or selling a fantasy. Google does not delete content because a firm asks, and content the company has no legal grounds to remove cannot be made to disappear. What is genuinely possible is durable displacement: accurate, authoritative content that earns and holds top-ranking positions, pushing the negative content far enough down the results that it stops being part of the user's first impression. The work has three components. Build the authoritative content - owned properties, third-party coverage in outlets that rank, structured data that supports entity prominence. Hold the ranks through ongoing maintenance and freshness. Address the source layer - if Wikipedia is amplifying the negative framing or the cited articles can be counter-sourced, the leverage at the source is higher than fighting the visible layer. The combination produces multi-year displacement; isolated tactics produce weeks.

International reputation work runs into the same complexity in every dimension. Google results vary by city, country, language, and device, and the differences are not cosmetic - a SERP that reads fine from New York can be hostile in Frankfurt. Wikipedia has separate language editions with separate notability conventions and editor communities; the German Wikipedia is not the English Wikipedia in any meaningful sense. AI engines weight different sources by language and by user location, so the same prompt produces different answers in different markets. Our infrastructure handles each layer. IMPACT™ covers 500 cities, 69 countries, and 23 languages with continuous geo-specific Google tracking. Our Wikipedia practice operates across the major language editions through disclosed-COI accounts on each. AIQ™ is built to handle multi-market AI monitoring as a first-class concern. The combination means a global company can get a coherent view of its reputation across regions rather than treating each market as a separate ad-hoc project.

Reputation dynamics differ enough across industries that the methodology has to adapt even when the platforms underneath stay constant. Financial services - hedge funds, private equity, asset managers - emphasize entity precision (the wrong fund family attribution causes real allocator problems), peer benchmarking against named competitors (every LP is doing peer screens), and source quality (Bloomberg, Reuters, regulatory filings carry more weight than industry blogs). Consumer brands emphasize narrative and review ecosystems - Google reviews, Glassdoor, Trustpilot, Reddit threads - that the engines weight heavily for consumer queries. Regulated industries (healthcare, financial services, defense, regulated tech) emphasize accuracy and compliance, because AI errors in those spaces produce real downstream harm. Law firms and professional services emphasize partner-level entity work and practice-area positioning. The methodology underneath - Track / Analyze / Impact - is constant; the source priorities and workstream weights shift to match the industry's actual reputation physics.

Google search reputation management is the work the firm has done longest, and the methodology has not fundamentally changed even as Google itself has shifted. The starting point is mapping: IMPACT™ runs continuous queries for the client's priority keyword set across the geographies and languages that matter, classifying every ranking URL by ownership, SERP feature, and topical relevance. The map produces the diagnostic - where the SERP is solid, where it is exposed, where the leverage sits. The interventions run in three parallel tracks: entity work (Wikidata, schema, Knowledge Panel) that strengthens the underlying signals across all queries; content work (owned properties, third-party placements, structured pages) that fills specific SERP positions; and authority work (citations, links, mention patterns) that compounds across the ecosystem. The progress is measured against the original map and reported monthly. The discipline is patient: durable Google work takes months to land but holds for years once it does.

The right-fit answer is honest about both directions. We are a fit when the stakes warrant sustained reputation work - investors, journalists, allocators, regulators, customers, or candidates are forming decisions based on what they find about the company or executive online, and the cost of getting that wrong is real. We are a fit when the client wants durable, measurable work and is willing to invest months rather than weeks. We are a fit when the client values transparency in methodology over promises that cannot be kept. We are not a fit when the request is for permanent removal of accurate negative content (impossible), single-tactic interventions that bypass real source-layer work (counterproductive), or undisclosed Wikipedia editing that produces short-term gains and long-term damage (we do not operate that way). The clearer the alignment up front, the better the engagement runs.

Peer benchmarking is one of the more practically useful outputs of our methodology because it answers the question every client actually asks: how do we look compared to the firms we are competing against. We define the peer set at the start of the engagement (typically five to ten named competitors, financial peers, or talent-market competitors depending on the program goal). IMPACT™ runs the same keyword sets for the peers as for the client across the same geographies, producing direct comparison of search position, share-of-voice, and SERP composition. AIQ™ runs the same prompts against the same eight AI engines for the peers, producing direct comparison of AI narrative themes, source attribution, sentiment, and prominence. The combined peer view is in every monthly report. The comparative framing tends to mobilize internal action more efficiently than absolute numbers, because the relative position is what the board, the allocators, or the recruiting committee actually cares about.

M&A work has a different cadence than standard reputation engagements because the deal timeline drives everything. Pre-announcement work runs as confidential diligence: an audit-grade assessment of the target's digital footprint, Wikipedia state, AI narrative profile, and any contested content that could affect deal communications or post-close positioning. During the deal window, AIQ™ runs daily monitoring on the target, the acquirer, and the combined entity narrative as it begins to form in AI responses, since the engines often start producing combined-entity descriptions before the official communications strategy has decided what it wants those descriptions to be. Wikipedia and Wikidata are prepared for the announcement so the updates land cleanly the day of close rather than slowly accumulating in the weeks after. The post-close work then shifts to integrating the entity infrastructure under the new corporate structure and addressing any residual narrative drift across engines.

Content work at Five Blocks is reputation-driven rather than volume-driven, which means every piece serves a specific purpose tied to a SERP gap or an AI narrative gap identified in the diagnostic. The discipline is consistent across formats. Each piece is scoped to a specific keyword and SERP feature, or to a specific AI prompt and engine response pattern. Each is hosted on the property where the citation will actually count: owned site, executive bio, microsite, or an authoritative third-party publication via placement. Each is structured for the extract: H2 and H3 headings as questions, short direct answers, lists where lists are warranted, schema markup, named authorship with bio context. Each is measured against the original diagnostic at next review. Content produced this way does the reputation work it was created for; content produced for word count and SEO instinct rarely does.

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.

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.

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.

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.

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.

The onboarding sequence is intentionally compressed because most clients arrive with an active situation rather than a long planning window. Step one is a discovery call with a senior advisor (typically Sam directly, or a senior account lead), covering the client's current situation, objectives, and any constraints. Step two is a diagnostic assessment of the existing digital landscape - SERPs, Wikipedia state, AI representation, entity signals - sized to the engagement scope but always grounded in real data rather than generalities. Step three is a formal proposal covering the engagement scope, named workstreams, deliverables, timeline, team, pricing, and success criteria, plus a Letter of Engagement. Step four is signature and kickoff. From first call to active work, the typical timeline is one to two weeks for non-urgent engagements and twenty-four to forty-eight hours for crisis engagements. The proposal documentation is detailed because the work is detailed; clarity up front prevents misalignment later.

Engagements typically begin with a discovery and diagnostic phase, during which we assess a client's current digital landscape, identify specific issues and opportunities, and develop a prioritized program scope. Most full-service engagements run for 6 to 12 months on retainer. Clients receive weekly updates, regular calls, and detailed monthly reports. Programs are structured around measurable outcomes and reviewed regularly against defined KPIs.

Wikipedia notability is not a Five Blocks judgment - it is a specific policy (WP:NCORP for organizations, WP:NBIO for biographies) that has been refined over twenty years of community application. The first step in any Wikipedia engagement is the notability assessment: a structured review of the existing third-party coverage measured against the policy bar. If the coverage clearly supports notability, we propose an article and move into the disclosed-COI creation process. If the coverage is close but uncertain, we identify the specific gaps and recommend either a deferral until coverage strengthens or a strategy for building the coverage authentically before attempting the article. If the coverage clearly does not support notability, we say so directly. The honest assessment up front prevents the predictable deletion downstream.