Opportunities are organised into 3 groups of channels, visible in the sidebar navigation:

• Influence covers platforms where content shapes what AI models say -YouTube, Reddit, and PR (news media, specialist blogs, comparison sites). Actions here build the brand’s footprint in the sources AI engines cite most.
• Paid Media covers channels where paid activation fills or amplifies AI visibility gaps (like ChatGPT Ads, DV360…). Actions here are most useful when organic presence alone isn’t enough to drive discovery.
• Organic covers the brand’s own website. Actions here fix the structural and content barriers that prevent AI models from reading, extracting, and citing brand pages accurately.

Each channel within a group can generate one or more recommendations per month, depending on what the data reveals for the brand × category × country combination being analysed.

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Channels : Influence

Requires: at least one active Search project on the workspace. Influence recommendations identify where your client’s brand is underrepresented on the platforms that AI models cite most frequently on top influential sources such as YouTube, Reddit, and media/blog/comparison sources (PR).

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YouTube : 4 types of opportunities

YouTube videos are one of the most frequently cited source types in AI answers. These recommendations target gaps in citation coverage and content quality.

Launch a video to capture missing AI citations

What it detects: Themes where competitor content is cited by AI models but the brand has no video presence.Trigger: The platform identifies the top two themes where competitors outperform the brand in AI citation rates. For each theme, it finds the highest-intent query to guide the video topic.Data used: Brand Thematic Overview (citation rates by theme), Keyword Details (search interest scores)What the brief covers: Video content creation aligned to the missing theme; YouTube metadata optimisation (title, description, chapters, transcript) to maximise AI discoverability.

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Reddit : 1 type of opportunities

Reddit threads are frequently cited sources in AI-generated answers. These recommendations target situations where competitors dominate discussions that influence what AI models say.

Create a Reddit-driven FAQ for your website

What it detects: Recurring question patterns across AI-cited Reddit threads that could be answered by brand-owned content on the website.Trigger: At least 3 threads with recurring FAQ signals, or 5+ unique FAQ-style queries identified across analysed threads.\Data used: Brand Sources Details, Reddit Listening, LLM analysis of thread content\What the brief covers: FAQ content brief (top 10 questions and answers for the website); FAQ page publication brief with schema markup guidance.

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Digital PR : 3 types of opportunities

PR recommendations cover 2 main source types (news media, specialist blogs) :

Deepen expertise via specialist content ecosystems (Reinforce)

Same logic as above, applied to specialist blogs rather than news media.Data used: Brand Sources Details (filtered to SPECIALIST_BLOG type)What the brief covers: Specialist content brief; publisher outreach brief for niche publications.\

Build initial authority via news media (Gap-fill)

What it detects: Themes where competitors outperform the brand AND news media domains are generating AI citations on those themes without the brand being present.\Trigger: Competitor Presence Rate exceeds brand Presence Rate AND brand is absent on the top news media domains for those themes.\What the brief covers: Educational content and market insight creation; personalised publisher outreach.

Establish expertise via specialist blog ecosystems (Gap-fill)

Same logic as above, applied to specialist blogs.\What the brief covers: Guest post and expert contribution briefs; outreach to specialist blog editors.\

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Channels : Organic

Requires: at least one completed GEO Audit for the same domain. Search project required for content strategy recommendations. On-site recommendations identify structural and content barriers preventing AI models from reading, extracting, and citing the brand’s web pages accurately. These are SEO and content actions on pages the brand own.

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Geo Audit Based : 7 types of opportunities

These recommendations are triggered by data from the GEO Audit project which crawls the brand’s pages and analyses them for AI readability signals.

Fix missing image alt texts

What it detects: Images on brand pages that are missing descriptive alt text — which means AI models can’t interpret the visual content or use it as a citation signal.\Trigger: At least 1 image without alt text AND image accessibility score below 100. Priority escalates based on page type (Home Pages and PDPs are most critical).
What the brief covers: Alt text audit; copywriting brief following the format [subject depicted] + [contextual relevance] + [product/category]; CMS deployment and validation.

Bulk remediate image alt texts

What it detects: Pages with 10+ missing alt texts, indicating a template-level gap rather than an isolated issue.\Trigger: 10+ images missing alt text OR image accessibility score below 80. If the missing alt ratio exceeds 20% of total images, a style guide is included.\What the brief covers: Full image inventory export; bulk alt text writing in priority order; CMS batch deployment; alt text style guide (if systemic gap confirmed).\

Add a summary block for AI quick-parse

What it detects: Pages that lack a structured summary block near the top — the key element that lets AI models extract the page’s primary value without reading everything.Trigger: No summary block detected on the page. Escalates to critical if the page also lacks Q&A and table elements (no extractability anchors at all).Content varies by page type:

• Home Page: brand overview
• PLP: category scope and range benefits
• PDP: product name + active ingredient + core claim + format/size
• Content Page: core topic claim + key benefit + target audience

What the brief covers: Summary block writing brief with content requirements per page type; schema implementation if applicable.

Add a Q&A section

What it detects: Pages that lack a FAQ or Q&A block — a dedicated extraction layer that makes it much easier for AI models to surface the page in answer to direct questions.
Trigger: No Q&A section detected. Priority escalates on Content Pages with 3,000+ words (highest citation opportunity loss). Not triggered on Home Pages.
What the brief covers: Writing 5–8 high-frequency Q&A pairs aligned to actual user queries; FAQ schema implementation (application/ld+json).

Convert prose data to semantic HTML tables

What it detects: Sections where comparison data, ingredient properties, clinical results, or multi-variable information is written as prose instead of structured tables — making it harder for AI to extract and use.Trigger: GEO Audit flags “Tabular data could be better structured for extraction.”\What the brief covers: Identifying the highest-value prose sections; converting to properly marked-up HTML tables with semantic structure (thead, th scope, tbody).\

Reduce on-page reading grade level

What it detects: Pages where copy complexity exceeds the AI readability threshold for that page type — making it harder for AI models to parse and cite the content.\Trigger thresholds by page type:\

• Home Page: Flesch-Kincaid Grade Level > 9
• PDP: Grade Level > 9 OR Flesch Reading Ease < 60
• PLP: Grade Level > 10 AND readability score < 70
• Content Page: Grade Level > 10 AND readability score < 70

\What the brief covers: Rewriting sentences over 25 words; replacing specialist vocabulary with plain-language equivalents; targeting a Flesch Reading Ease score ≥ 60.

Audit and resolve the JavaScript rendering gap

What it detects: Pages where the JavaScript-rendered version has significantly more (or less) content than the static HTML version — meaning AI crawlers that don’t execute JavaScript are missing content.Trigger: Word count ratio between static HTML and JS-rendered version is outside the 90–110% range (more than 10% difference).\What the brief covers: Crawling the page with both JS-enabled and static tools; identifying the sections responsible for the gap; fixing so all primary content is visible in the static HTML response.

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Search-based content strategy : 2 types of opportunities

These recommendations require a Search project and use AI visibility metrics (Brand Mention Rate and Average Position by topic) to identify content gaps and opportunities.

Fill Content Gaps to Capture Missing AI Citations

When it applies: No page from your main domain ranks on the Thematic. AI models never cite you for this topic. Each keyword with zero ranking coverage becomes a Create row.**What the brief covers: **One content brief per missing topic, telling a copywriter (or an AI agent) exactly what to publish without further research - the anchor keyword to answer, the uncovered fan-out sub-questions to address declaratively, and the content format dictated by the dominant search intent (explainer, comparison, buying guide, or FAQ/review page).

Create a foundational topic page on Weak Spot

When it applies: The brand has both low mention rate (< 30%) AND poor average position (> 8) on a topic — meaning AI models barely mention the brand and when they do it’s ranked poorly.What the brief covers: Creating a new 2,000+ word authoritative page from scratch, with definition-first structure, FAQ schema, structured sections, and E-E-A-T signals (author bio, external citations, datePublished).

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Channels : Paid Media

Requires: at least one active Search project. ChatGPT Ads and Rufus Ads additionally require an active Brand Perception analysis. Paid Media recommendations identify where paid activation can fill or amplify AI visibility gaps — either because organic presence is too low to drive discovery, or because a paid placement gives immediate access to high-value surfaces.

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DV360 : 1 type of opportunities

Build a DV360 allowlist from authority sources

What it detects: High-authority domains and URLs that AI models frequently cite as sources — which can be used as programmatic targeting inventory in DV360.\Trigger: At least 3 targetable domains identified from the brand’s AI citation data.Data used: Brand Sources Details (authority sources, URLs, domains, visibility scores, citation counts)What the brief covers: A curated list of the top 30 domains and top 30 URLs ranked by visibility score and citation count; guidance on building a DV360 allowlist or curated deal; campaign launch brief including measurement setup.The idea: if a site is already influencing what AI says about a category, placing display or video ads there reinforces the brand in the same environment.

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ChatGPT Ads : 3 types of opportunities

Retailers only. ChatGPT Ads opportunities are only generated for workspaces with an active Brand Perception analysis. Country note: If the workspace country is outside [US, CA, AU, NZ, UK, JP, KR, BR, MX], the opportunity title will include ”— Prepare now for ChatGPT Ads beta launch” to signal upcoming availability.\

Offset low organic visibility with paid ads

What it detects: Themes where the brand’s citation rate in ChatGPT is too low for organic discovery to work on its own, while competitors are being cited organically\Trigger: Brand citation rate below 20% on target themes AND competitors are cited organically on those same themes.Data used: Brand Perception analysis (citation rate by theme, competitor presence\What the brief covers: Campaign structure for compensatory ChatGPT Ads — keyword targeting (positive and negative), ad group structure, headline and description frameworks.\

Fix brand misperceptions with corrective ad copy

What it detects: Negative attributes that AI models consistently associate with the brand — surfaced often enough to affect how the brand is recommendedTrigger: A negative attribute appears in 15%+ of brand mentions in ChatGPT responses AND no corrective content currently exists in the AI citation pool for those attributesData used: Brand Perception analysis (attribute-level perception, negative attribute frequency, sentiment scoresWhat the brief covers: Corrective ad copy brief using the specific negative attributes identified by SOM to reframe the brand narrative.

Target high-potential personas via paid campaign

What it detects: Persona segments where the brand’s share of voice in ChatGPT is very low while competitors dominate.\Trigger: Brand Share of Voice below 15% on a persona AND competitor SOV above 40% on that same persona, with a gap of 25+ percentage points
Data used: Brand Perception analysis (Share of Voice by persona)\What the brief covers: Persona-targeted campaign brief with audience-specific language and intent signals drawn from the SOM SOV analysis.