Search engine results page (SERP) composition

The first three pages of Google search results for your name, brand, and key executives. Most firms track this. Few track it at the right geographic precision or device type. Arctura monitors SERP composition weekly with location-specific sampling across your top 5 target markets.

Review platform sentiment and velocity

Google Business Profile, Glassdoor, Trustpilot, G2, industry-specific platforms. Track both rating and velocity — a sudden spike in negative reviews is often the first signal of a coordinated reputation attack or an internal crisis that hasn't surfaced publicly yet.

News and press mention sentiment

Media monitoring via Google Alerts, Mention, or Meltwater. Key metric: ratio of positive to negative coverage, and which publications are amplifying each. One negative piece in a high-authority publication requires a different response than 50 pieces in low-authority blogs.

Social platform conversation sentiment

LinkedIn, X, Reddit, and industry forums. Reddit and Quora deserve specific attention because they have disproportionate LLM training data representation — a negative Reddit thread from 2023 may still be influencing what AI engines say about you today. See our guide on citation amplification playbooks for how to counterbalance this.

Executive digital footprint

Your reputation is your leadership team's reputation. A CEO with a thin or contradictory digital presence creates entity ambiguity that propagates into AI responses about the company. Monitor and build executive entity graphs as actively as brand entity graphs.

Competitor share of voice in earned media

Not just your coverage — the ratio of your coverage to competitor coverage in publications that matter to your buyers. Losing share of voice in trade press is often an early warning sign of reputation erosion before it shows up in direct brand searches.

Share of Model across AI platforms

How frequently your brand is cited in AI-generated responses to relevant queries across ChatGPT, Perplexity, Gemini, and Claude. This is the most important emerging reputation metric and the one nearly all traditional ORM firms do not yet track. Arctura's Signal Score™ incorporates SoM as a primary dimension. See the full AI visibility guide for measurement methodology.

Entity graph consistency score

The degree to which your brand's name, description, founding date, leadership, and canonical URL are consistent across all web properties. Inconsistency creates entity disambiguation failures — AI systems receive contradictory signals and either cite you with low confidence or default to a competitor whose graph is cleaner. See entity optimization for the fix.

Crawler infrastructure health

Whether your schema is rendered in initial HTML (not JavaScript), whether your robots.txt allows GPTBot, ClaudeBot, and PerplexityBot, whether your llms.txt is deployed and current. These technical signals determine whether AI systems can access your content at all — before any question of sentiment arises.

Maximize owned domain authority

Your primary domain should rank positions 1–3 for your brand name. If it doesn't, you have a technical SEO and content problem that needs to be fixed before any other suppression strategy will work. Ensure your homepage, About page, and leadership pages are all individually indexable with proper canonical tagging.

Occupy high-DA profile pages

LinkedIn company page, LinkedIn executive profiles, Crunchbase, AngelList, Glassdoor employer page, Wikipedia (where eligible), Bloomberg company profile, Google Knowledge Panel. Each one is a high-DA page that will rank for branded queries and push negative content down. Arctura manages all profile creation, optimization, and ongoing maintenance as part of the SignalStack™ deployment.

Secure authoritative press placements

One article in Forbes, Fast Company, or a relevant trade publication outranks 50 blog posts for suppression purposes. Arctura's earned media team targets placements that are both topically relevant and high enough authority to displace negative results on page one. One press placement = 50 owned posts in suppression power.

Build a content moat around branded queries

The same skyscraper content strategy that builds topical authority for non-branded queries also suppresses negative results for branded ones. A deep resource hub indexed under your domain creates multiple page-one entries for brand searches. Arctura produces this content with dual optimization — GEO-structured for AI citation and SEO-structured for traditional suppression.

Make your entity structurally unambiguous

When Google's and AI systems' entity graphs are clean — consistent sameAs nodes, verified Wikidata entry, bidirectional LinkedIn rel="me" links — the algorithm has no reason to elevate ambiguous or negative content to clarify who you are. Entity clarity is the long-term suppression foundation. See the complete entity optimization guide.

"What is [brand]?"

The identity query. Tests whether AI systems can accurately describe your organization, its founding, its core offering, and its leadership. Low scores here indicate an entity graph problem — the AI doesn't have a clear picture of who you are. The fix is entity declaration and @graph construction.

"Who founded [brand]?" / "Who runs [brand]?"

The leadership query. Tests executive entity graphs. A CEO with a well-formed JSON-LD Person declaration, verified sameAs nodes, and strong LinkedIn presence will produce accurate, consistent AI responses. A CEO with a thin or contradictory digital presence will produce vague, hedged, or incorrect responses — which affects the brand's perceived credibility in the AI response.

"What does [brand] do?" / "What services does [brand] offer?"

The offering query. Tests whether your knowsAbout and service schema accurately reflects your current positioning. If AI responses describe your services incorrectly or in outdated terms, it's a schema maintenance problem — your entity graph hasn't been updated to reflect your current positioning.

"Is [brand] legitimate?" / "Is [brand] trustworthy?"

The trust query. Tests E-E-A-T signal completeness. AI engines answer this by looking for verification signals: third-party citations, press coverage, professional associations, Wikidata entries. A brand with weak E-E-A-T infrastructure will receive hedged or negative trust responses. See the E-E-A-T guide for the complete signal build.

"[Brand] vs. [Competitor]"

The competitive query. Tests your relative signal strength against your primary competitors. If your competitor is consistently cited first or with higher confidence in comparison queries, they have a stronger entity graph or more authoritative citation network. Arctura's competitive displacement strategy targets this cluster specifically.

"Who is better for [use case]: [brand] or [competitor]?"

The use-case comparison. Tests whether AI systems associate your brand with the specific problems your buyers need solved. If you're not mentioned in these responses, you have a knowsAbout and topical authority gap — your entity graph doesn't signal expertise in the relevant domain.

"What are [brand]'s weaknesses?" / "[Brand] reviews"

The negative comparison. Essential for reputation monitoring. Tracks what AI systems say when users are looking for reasons not to buy. Arctura monitors this cluster weekly for all clients — it's often the first place reputation issues surface in AI responses, ahead of traditional review platforms.

"[Brand] alternatives"

The displacement query. Tests whether your competitors are being recommended as alternatives to you — which means you're already being considered but may be losing the final recommendation. This cluster requires both entity strength and citation amplification in the publications AI systems use as sources.

"Best [service category] companies" / "Top [industry] firms"

The discovery query. High-stakes for brand acquisition. When a prospect doesn't know your brand yet and asks AI for recommendations, this is the query type that determines whether you exist for them. Appearing in these responses requires topical authority and citation network strength — not just entity clarity.

"Best [service category] for [specific use case]"

The qualified recommendation. More valuable than the generic category query because the intent is specific and the user is further along in their decision. Appearing here requires your content to have explicitly addressed the use case with structured, citation-ready content — FAQPage schema, standalone citable paragraphs, entity-led openings.

"Who should I hire for [problem]?"

The action query. The highest-intent cluster — the user is ready to make a decision. SoM in this cluster directly drives lead generation from AI-first research behavior. Arctura prioritizes this cluster in all client optimization programs because citation here produces the highest downstream conversion rate.

"What company handles [specific problem] best in [location/industry]?"

The localized or vertically specific recommendation. For Arctura's local market clients, this cluster is often where the ARM ecosystem's local AI presence strategy produces the highest SoM gains — because local entity signals (Google Business Profile, local citations, geographic schema) are often absent from competitors' entity graphs.

The @id must be stable, canonical, and resolvable

Your @id is your entity's permanent address in the knowledge web. It must be a URL you control, it must not change, and it must return a 200 response. A dead @id is worse than no @id — it's a negative signal. Arctura uses the pattern https://domain.com/#org as the canonical @id for all organization entities.

Name strings must be identical across all pages and properties

"Arctura Reputation Management" and "Arctura Reputation Mgmt." and "Arctura" are three different entities to an LLM. Pick one canonical name string and use it everywhere — schema, social profiles, press releases, business registries. Inconsistency creates disambiguation failures. Disambiguation failures lower citation probability.

Schema must be in initial HTML — never JS-rendered

Test with curl, not your browser. curl -s https://yourdomain.com | grep "application/ld+json". If the schema isn't in the curl output, LLM crawlers that don't execute JavaScript will not see it. This is the single most common technical GEO failure Arctura finds in initial client audits.

sameAs links must be live and verified

Dead sameAs links are negative signals. Every link in your sameAs array must return a 200 and must display your canonical name on the destination page. Before adding a node, verify it manually. After adding it, monitor it monthly. A Crunchbase profile that gets unclaimed and corrupted is worse than no Crunchbase profile.

Person and Organization are separate entities with separate @ids

A common error: declaring the founder's sameAs nodes on the Organization entity. The founder's LinkedIn should be in the Person entity's sameAs. The company's LinkedIn should be in the Organization entity's sameAs. Conflating them creates entity ambiguity that reduces citation accuracy for both.

knowsAbout must reflect current positioning — not legacy messaging

The knowsAbout array tells AI systems what your entity is an authority on. If your positioning has evolved — you've niched down, pivoted, or expanded — but your knowsAbout hasn't been updated, AI responses will describe your expertise in outdated terms. Arctura reviews and updates knowsAbout as part of every quarterly GEO infrastructure audit.

Declare parentOrganization for subsidiary brands

Arctura Reputation Management operates within the ARM ecosystem. That relationship should be declared in Arctura's @graph with a parentOrganization reference to ARM's @id. This bidirectional relationship — also declared in ARM's @graph — creates a knowledge graph edge that strengthens both entities' citation probability and helps AI systems accurately represent the organizational structure.

FAQPage schema on all high-value content pages

FAQPage is one of the highest-citation schema types. Each question-answer pair is a standalone citable unit — an AI system can extract it, answer a user query with it, and attribute it to your domain. Every Arctura resource guide (including this one) includes FAQPage schema with questions matched to exact LLM query phrasing for the 12-cluster probe protocol.

Activate the crisis command structure

Arctura's crisis team is on-call 24/7. Within four hours of engagement, the command structure is live: crisis lead, content lead, technical GEO lead, and client communications lead. No public statement is issued during this window — assessment only. Every hour of assessment is worth three hours of correction if the wrong statement goes out.

Signal audit and entity graph assessment

Before any content is published, Arctura runs a complete signal audit: what is currently ranking for brand searches, what are AI engines saying, what content is spreading and on which platforms, what is the current Share of Model reading for key query clusters. This baseline determines the counter-signal strategy.

Deploy the official statement and entity-anchored response content

The official statement is published on your owned domain with full structured data: Article schema, FAQPage schema addressing the core questions the crisis has raised, and an updated entity declaration that references the response. This is the canonical source. All other communications reference back to it. Schema is deployed in initial HTML — tested with curl before publication.

Activate high-trust platform synchronization

The narrative established in your owned statement is synchronized across high-trust platforms in order of their AI citation weight: LinkedIn (highest training data inclusion probability), Medium, industry press. Each publication is GEO-structured — entity-led opening, standalone citable paragraphs, direct links back to the canonical response. The goal is to establish a citation cluster around the accurate narrative before the crisis content can compound.

Monitor SoM shift and activate suppression

By hour 48, Arctura is running real-time SoM probes across all four AI platforms to track whether crisis content or counter-narrative content is gaining citation momentum. Simultaneously, the content suppression layer is activated — Layer 3 earned media outreach for sympathetic coverage, and Layer 4 content architecture deployment for branded query occupation.

Assess, adapt, and enter sustained recovery mode

At 72 hours, the acute phase ends and sustained recovery begins. Arctura provides a full signal report: what is ranking, what AI engines are saying, what the SoM reading is across query clusters, and what the 30/60/90-day recovery trajectory looks like based on the counter-signal deployment. The SignalStack™ continuous loop takes over from the acute crisis protocol.

Crisis content trains into LLM responses

High-engagement negative content — particularly from high-DA sources like major publications or viral Reddit threads — has a disproportionate probability of incorporation into LLM training data. Arctura deploys counter-citation campaigns on Reddit and Quora specifically to seed accurate narratives in the same communities where crisis content originated, giving LLM training pipelines a balanced signal to draw from.

Entity graph contamination

During a crisis, incorrect or negative information sometimes propagates into third-party data sources that feed knowledge graphs — Wikipedia talk pages, Wikidata edits, Crunchbase updates. Arctura monitors all Tier 1 and Tier 2 sameAs nodes daily during a crisis for unauthorized modifications that could corrupt the entity graph.

Schema deployed incorrectly under pressure

Crisis urgency creates schema deployment errors — a JSON-LD mistake that creates entity ambiguity can persist for months. Arctura follows the ARM Framework's Byzantine Fault Tolerance protocol: no schema update during a crisis is deployed without 3-agent consensus (Schema Architect + GEO Audit + OPS lead sign-off). This prevents haste from compounding the crisis with a technical GEO failure.

Slow AI response decay after media cycle closes

The media cycle may close in days. AI responses incorporating crisis content can persist for months. Arctura continues SoM monitoring for 90 days post-crisis, with weekly probe runs across all four platforms. When AI responses are still reflecting crisis-era content at the 30-day mark, Arctura activates a secondary counter-citation push to accelerate the decay.

Establish the canonical source — the entity's permanent home

Before any signal can be amplified, the source must be authoritative. Arctura begins every engagement with a complete @graph deployment on your primary domain: Organization entity, Person entities for key executives, FAQPage schema on all high-value content pages, and a canonical URL structure that creates a stable identity URI for every entity in your graph.

The canonical source is also where Arctura deploys the llms.txt file — the crawler guidance layer that tells AI retrieval agents which content should be prioritized for citation on which query types. Outcome: become the definitive source, eliminating ambiguity for AI models and search engines alike.

Distribute your signal where people and AI look first

Entity graph completeness on your owned domain is necessary but not sufficient. AI systems assign higher citation confidence to entities they recognize from multiple independent, high-authority sources. Arctura synchronizes your narrative across the sameAs node hierarchy: Wikidata, LinkedIn, Crunchbase, Wikipedia (where eligible), industry registries, and press placements.

The earned media component of this stage is the highest-leverage investment Arctura makes for clients. One article in a publication with high LLM training data inclusion probability — Forbes, TechCrunch, a respected industry journal — produces more citation signal than 50 owned blog posts. Outcome: distribute your signal where people and AI look first, building a network of trust that no single-source strategy can replicate.

Align your content with how AI systems extract and cite information

GEO content is structured differently from SEO content. The optimization target is not keyword density or readability score — it's citation probability. Arctura applies ARM GEO content principles to every piece of content produced for clients: standalone citable paragraphs that are complete without surrounding context, entity-led openings in the first 50 words, declarative statements rather than hedged claims, and explicit query-phrasing alignment with the 12-cluster SoM probe protocol.

This stage also covers topical authority architecture — the skyscraper content structure that builds a dense, interlinked knowledge graph around your core expertise areas, making it structurally difficult for AI systems to discuss your domain without citing you. Outcome: connect your brand to the exact language your buyers use when asking AI systems for help.

Keep the signal clean, current, and continuously compounding

The most common point of failure in reputation infrastructure programs is maintenance. The entity graph is deployed correctly at launch, then allowed to drift — a sameAs link dies, a Crunchbase profile gets unclaimed, a new product line isn't added to the knowsAbout array, executives change and the Person entities aren't updated. Drift creates entity ambiguity. Entity ambiguity erodes citation probability.

Arctura runs a continuous orchestration loop — modeled on the ARM AURE continuous GEO optimization protocol — that audits entity consistency across all nodes on a weekly cadence, updates content to reflect current positioning, monitors SoM across all four platforms, and adjusts the citation amplification strategy based on platform-by-platform probe data. Outcome: your Signal Score™ compounds over time rather than eroding.

Entity Declaration Score

@graph completeness, @id validity, sameAs verification rate, name string consistency across properties. An entity that scores 96+ here is structurally unambiguous — AI systems can accurately describe it without hedging.

E-E-A-T Signal Score

Credential coverage across all four dimensions. Most new clients score 60–75 here — strong on Experience and Trust, weaker on Expertise schema and Authority citations. The three external citations needed to close the E-E-A-T gap is the most common gap Arctura addresses in the first 60 days.

GEO Content Score

Citation structure of published content — proportion of content with standalone citable paragraphs, entity-led openings, FAQPage schema, and query-phrasing alignment. Most clients score 40–60 here before Arctura's content architecture is deployed.

Crawler Infrastructure Score

llms.txt deployment, robots.txt AI crawler permissions, schema render method (initial HTML vs. JS), XML sitemap priority configuration, page speed. The most technically specific dimension — and the one where Arctura most frequently finds the gap between a good reputation strategy and a working one.

Share of Model Score

Weighted average SoM across four AI platforms for the 12-cluster probe protocol. The composite metric that all other dimensions serve. Arctura targets 80% average SoM for all clients by month six — the threshold at which citation probability becomes self-reinforcing.