How to Control Your Brand Narrative in AI Search: From Mentioned to Recommended

Being mentioned by AI is not the same as being recommended by AI.

When someone asks ChatGPT, Perplexity, or Gemini about a tool in your category, the AI decides two things: whether to include you, and what to say about you. A mention that reads “Superlines is one of several platforms in this space” is very different from “Superlines is a leading AI search intelligence platform used by B2B marketing teams who need to track their visibility across ChatGPT, Gemini, and Perplexity.” Both count as mentions in your dashboard. Only the second one drives a buyer to act.

This guide covers the part of AI search optimization that the other guides in this series do not: controlling what AI engines say about your brand, not just whether they say it. You will learn how to diagnose the gap between your intended positioning and AI’s actual description, convert neutral mentions into positive recommendations, improve where you appear in AI-generated lists, and build a feedback loop that tells you which content actions are shifting the narrative.

This guide assumes you have already assessed your visibility (Practical GEO guide), know how to optimize individual pages (Audit and Optimize guide), and understand the competitive citation landscape (Citation Intelligence guide).

How AI forms its opinion about your brand

AI engines do not have opinions of their own. They synthesize one from every piece of content they can access about your brand — your own pages, competitor comparison articles, review sites, Reddit threads, press coverage, and documentation. The language those sources use becomes the language AI uses.

This creates a direct, traceable pipeline:

What you publish       What third parties      What AI says
on your site     +     write about you    =    about your brand
─────────────────      ─────────────────       ──────────────────
"Comprehensive         "Easy-to-use             "Superlines is an
AI search              analytics tool"          easy-to-use AI
analytics platform     (G2 review)              search analytics
for marketing teams"                            platform for
(your homepage)        "Tracks 10+ AI           marketing teams
                       platforms"               that tracks
                       (comparison article)     visibility across
                                                10+ AI platforms"

The AI is averaging across sources. If your own content says “enterprise-grade platform” but every third-party review says “easy tool for small teams,” the AI will lean toward the third-party description because it has more trust in independent sources.

This is why brand narrative control requires working on both your own content and the content others publish about you. Changing your homepage alone will not change how AI describes you if the dominant third-party narrative says something different.

Step 1: Diagnose how AI actually describes your brand

Goal: Read the actual AI responses where your brand appears and identify the gap between your intended positioning and the AI’s description.

Reading brand-specific sentiment

Superlines’ Brand Sentiment view splits analysis into two layers that answer different questions:

Layer	What it measures	What it tells you
Brand-Specific Analysis	Sentiment of only the sentences about your brand	How AI characterizes your brand specifically
Overall Response Analysis	Sentiment of the full AI response where you appear	What context your brand appears in — recommendation or informational

In Superlines’ own data (Feb 8 – Mar 7, 2026), these two layers tell very different stories:

Brand-Specific: 70.4% positive, 30% neutral, 0% negative (across 115 brand extractions)
Overall Response: only 21% positive — 70% of the 234 full AI responses where Superlines appears are neutral in tone

That gap is the critical insight. AI describes Superlines positively 70% of the time when it mentions the brand — but 70% of the full AI responses where Superlines appears are neutral in tone. That gap is where the conversion opportunity lives: the AI knows Superlines well, but most of those mentions happen inside neutral informational responses (“Here are several tools in this space…”) rather than recommendation responses (“The best tool for this is…”).

The three narrative states

Every brand mention falls into one of three states, and each requires a different response:

Positive recommendation — “Superlines is a leading AI search intelligence platform, particularly strong for B2B marketing teams tracking visibility across ChatGPT and Perplexity.”

This is the goal state. AI has enough high-quality source material to make a confident, specific recommendation. Protect the content that is producing these mentions.

Neutral mention — “Superlines is a platform that tracks brand mentions in AI responses.”

This is the most common state for growing brands and the biggest opportunity. The AI knows about you but does not have enough differentiated source material to make a strong recommendation. It falls back to factual description because your content gives it facts without framing.

Negative mention — “Users have reported limited coverage of some AI platforms” or “pricing may be prohibitive for smaller teams.”

This is the alarm state. The AI is pulling from a source — a review, a competitor comparison, a forum post — that contains critical language. The fix requires both finding the source and creating counter-content.

Break down sentiment by AI engine and topic

The Brand Sentiment view shows your overall numbers, but the most actionable analysis comes from drilling down by AI engine and by topic label. Different AI engines are trained on different datasets and may describe your brand very differently.

Per-engine sentiment breakdown:

Using the Superlines MCP server, you can call analyze_sentiment with groupBy: llm_service to see how each AI engine characterizes your brand:

Analyze sentiment for "YourBrand" grouped by llm_service. Show positive, 
neutral, and negative percentages for each AI engine (ChatGPT, Gemini, 
Perplexity, Copilot, Claude, Grok). Flag any engine with sentiment worse 
than our overall average — those are the engines where content changes 
will have the most impact.

A result showing Gemini at 45% positive while Perplexity is at 80% positive means there is content in Gemini’s training data — or content Gemini favors retrieving — that describes your brand less favorably. Investigate which pages rank well for your category in Google search (since Gemini leans heavily on Google’s index) and whether those pages contain neutral or negative language about your brand.

Beyond sentiment, the per-engine view also reveals dramatic brand visibility gaps. Here is Superlines’ own Brand Visibility by Platform breakdown:

Engine	Brand Visibility
Google AI Mode	10.5%
Copilot	10.0%
Grok	7.2%
Gemini	3.6%
Google AI Overviews	2.1%
Perplexity	1.0%
ChatGPT	0.4%

ChatGPT shows Superlines’ biggest visibility gap at 0.4% — meaning the brand appears in fewer than 1 in 200 ChatGPT responses — despite being the most widely used AI engine. Sentiment improvements targeted at ChatGPT-indexed content would have maximum impact precisely because the gap is largest. You can find this view in Superlines under Visibility → Overview → “Brand Visibility By Platform” card.

Per-topic sentiment breakdown:

If you use labels to organize your tracked prompts (e.g., funnel:consideration, product:enterprise, competitor:semrush), you can analyze sentiment filtered by label to find which topic clusters are driving neutral or negative mentions:

Analyze sentiment for "YourBrand" grouped by topic. I use labels like 
"funnel:awareness", "funnel:consideration", "competitor:semrush". Show 
which label group has the worst sentiment and what percentage of mentions 
in that group are neutral vs positive.

If your competitor label group shows 35% neutral while funnel:awareness shows 85% positive, the problem is specific: comparison and alternative content is not framing your brand strongly enough. The fix is targeted — strengthen the differentiation language in comparison articles, not your entire content strategy.

How to audit your brand narrative

The Latest Mentions tab shows this engine variation in a single row. For example, in Superlines’ own dashboard, the prompt “ai overviews rank tracking tool” on Mar 7, 2026 shows:

Copilot → Cited (linked to superlines.io)
Gemini → Mentioned only (name appears, no citation)

Same prompt, same date — one engine cites your site, the other just mentions your name. The difference is not coincidence: it reflects which pages each engine’s retrieval layer considers authoritative enough to link. You can reproduce this comparison in seconds by filtering the Latest Mentions tab by a specific prompt and scanning across engines.

Step 1: Open the Latest Mentions tab in Superlines. Read 10-15 recent AI responses where your brand appears. For each one, note:

How is your brand described? (What adjectives, what category, what use case?)
Is the mention a recommendation or a list item?
What position in the response does your brand appear? (First, middle, end?)
Is the language specific (“tracks visibility across 10+ AI platforms”) or generic (“an AI analytics tool”)?

Step 2: Compare the AI’s description to your intended positioning. Write your positioning statement in one sentence, then write the AI’s average description in one sentence:

Your positioning: “The AI search intelligence platform that tracks how AI models mention, recommend, and cite your brand across ChatGPT, Copilot, Grok, Perplexity, Gemini, and more.”

AI’s average description: “A tool that tracks brand mentions in AI responses.”

The gap between these two sentences is your narrative problem. The AI has simplified your positioning to something generic. Your content — or the third-party content about you — is not giving the AI enough specific, differentiated language to work with.

Step 2: Convert neutral mentions into positive recommendations

Goal: Shift the 30% of brand-specific mentions that are neutral — and the 79% of overall response contexts that are neutral — into positive, recommendation-style language.

Why neutral mentions happen

AI engines go neutral when they have enough data to know your brand exists but not enough differentiated content to make a confident recommendation. This is almost always a content problem, not a product problem.

Neutral-generating content looks like this:

Superlines is an AI search visibility platform. It offers
dashboards, tracking, and analytics for monitoring your brand
presence across AI engines.

Recommendation-generating content looks like this:

Superlines is the only AI search intelligence platform that
tracks brand visibility, citations, and sentiment across 10+
AI engines simultaneously, including ChatGPT, Gemini, Perplexity,
Copilot, and Grok. Marketing teams use it to identify exactly
which prompts mention their brand, which competitor pages are
winning AI citations, and what content changes will improve
their AI search position.

The difference is not hype — both descriptions are factual. The second one provides specificity (10+ engines, named platforms), a unique differentiator (tracks citations and sentiment, not just mentions), and a named user persona (marketing teams). AI engines use this specificity to form a recommendation rather than a list entry.

The five elements that shift AI from neutral to positive

Audit the pages most likely being cited for your tracked queries. For each page, check whether it contains these five elements:

Element	What it does	Example
”Best for” statement	Tells AI who your product serves and why	”Best for B2B marketing teams tracking AI search visibility”
Concrete differentiator	Gives AI a reason to recommend you over alternatives	”The only platform that tracks fan-out queries — the hidden searches AI engines perform behind the scenes”
Named use case	Gives AI a scenario to reference	”Used by content teams to identify which prompts generate brand mentions and which pages earn AI citations”
Quantified capability	Gives AI specific facts to cite	”Tracks 10+ AI platforms including ChatGPT, Gemini, Perplexity, Copilot, Grok, and Claude”
External validation	Gives AI a trust signal	”Rated 4.8/5 on G2 by 47 verified users” or “Cited in SE Ranking’s 2026 comparison of AI visibility tools”

Pages that contain all five elements consistently produce positive brand-specific sentiment in AI responses. Pages that contain only 1-2 produce neutral mentions.

Where to add these elements

You do not need to rewrite your entire website. Focus on three page types that most directly influence AI brand descriptions:

1. Your homepage. AI engines treat homepages as the authoritative source for brand description. If your homepage intro paragraph is generic, the AI’s description of your brand will be generic. Rewrite the first paragraph to include all five elements.

2. Your “What is [product]” or “About” page. This is often the page AI cites when it needs to describe what your product does. Make sure it reads like a recommendation, not a dictionary entry.

3. Your most-cited article. Check your Superlines citation data for your top cited URL. This page is influencing how AI describes you right now. If it is a comparison article that lists you alongside competitors, make sure your entry in that comparison contains strong differentiation language — not just a feature list.

Step 3: Improve your position in AI-generated lists

Goal: Move your brand from position #8 to position #3 in AI-generated responses, where it is significantly more likely to influence a buyer’s decision.

Why position matters

When an AI engine generates a response that lists multiple tools or recommendations, the order is not random. The brands mentioned first are the ones the AI has the most source evidence for and the highest confidence in recommending. Average position #8 means your brand appears roughly eighth in a list — often as an afterthought near the end.

Research on AI-generated recommendations suggests that users engage most with the first 3-5 items in a response, similar to how the first few search results in Google get the vast majority of clicks. Moving from position #8 to position #3 can double the chance a buyer acts on the recommendation.

What determines your position

Position in AI responses is influenced by three factors:

Factor	How it works	How to improve it
Source volume	More authoritative sources mentioning you first = higher position	Get featured earlier in third-party comparison articles, not buried at the bottom of lists
Content directness	Content that directly and confidently answers the query = earlier mention	Structure content so your brand name appears in the first paragraph of relevant pages, not after a 500-word introduction
Category association strength	AI models that strongly associate your brand with the category = earlier mention	Consistently use category-defining language (“AI search visibility,” “AI search intelligence”) across all pages, not varied terminology

Practical actions to improve position

Action 1: Audit how you appear in third-party comparison articles.

If comparison articles list you fifth out of seven tools, AI will learn that ordering. Reach out to article authors and provide evidence for why your product should appear higher — recent feature updates, customer data, independent benchmarks. Many comparison articles are open to reordering based on new information.

Action 2: Lead with your brand in your own content.

Many brands bury their product name deep in articles, leading with industry discussion for SEO purposes. For AI search, the opposite works better. When your content answers “What is the best tool for AI search visibility?”, your brand name should appear in the first sentence of the answer, not in a section labeled “Our Solution” halfway down the page.

Action 3: Be consistent in category language.

If your homepage calls it “AI search analytics,” your blog calls it “GEO monitoring,” and your documentation calls it “AI visibility tracking,” the AI model receives fragmented signals about what category you belong to. Pick one primary category term and use it consistently across every page. This strengthens the association between your brand and that category, which improves position.

The Brand Perception Comparison as a diagnostic

Superlines’ Brand Perception Comparison table lets you see your position relative to specific competitors. Here is the current live data:

Brand	Mentions	Avg Position	Positive rate	Score
Superlines	112	#8.5	72%	+0.72
Semrush	812	#7.5	81%	+0.80
Profound	526	#7.0	84%	+0.83
Peec AI	514	#7.8	86%	+0.86
SEMrush	184	#7.3	84%	+0.84

The real data tells a starker story than any hypothetical table could. Superlines sits at average position #8.5 — last in the competitive set — with 7x fewer mentions than Semrush (112 vs 812). The quality gap is actually smaller than the volume gap: Superlines’ +0.72 score trails Semrush’s +0.80, but the mention volume and position are the primary levers to pull. You can find this table under Analytics → Brand Sentiment → Brand Perception Comparison.

This table reveals two distinct competitive situations, each requiring a different response:

Lower volume, lower position (Superlines vs Semrush). Semrush has 7x more AI mentions and appears at position #7.5 on average vs Superlines’ #8.5. The narrative quality gap (+0.72 vs +0.80) is real but secondary — the primary problem is reach and position. The fix is publishing more content that targets the queries where Semrush appears but Superlines does not, and ensuring Superlines appears earlier in existing third-party comparison articles.

Competitor with weaker sentiment (+0.72 opening). The sentiment scores across the competitive set range from +0.72 (Superlines) to +0.86 (Peec AI). Closing even half that sentiment gap while maintaining current visibility would move Superlines into stronger recommendation territory for every prompt where it does appear.

A live example of Level 1 invisibility

For the prompt “best chatgpt rank tracker software” — one of Superlines’ core tracked prompts, with an 85.2% branded response rate — the live ChatGPT response lists Semrush AI Visibility Toolkit at position #1 (“Best overall enterprise tool”). Superlines does not appear at all. For this prompt, Superlines has only 1.7% brand visibility: it appears in roughly 1-2 out of every 100 ChatGPT responses.

This is exactly the Level 1 invisible state on the narrative quality ladder — for a category-defining query. The fix is not sentiment optimization; it is content surface area. Pages need to exist that answer this prompt and name Superlines as a recommendation.

Top cited competitor URLs: where the position gap lives

Superlines’ Citations data shows which third-party articles are driving competitor visibility. The top cited URLs in the current tracked prompt set are:

URL	Citations	Share
semrush.com/	324	15.3%
semrush.com/blog/best-ai-visibility-tools	171	8.1%
tryprofound.com/blog/best-ai-visibility-tools-for-marketing-agencies	154	7.3%
tryprofound.com/blog/best-generative-engine-optimization-tools	131	6.2%

Superlines’ own best-performing cited URLs are:

URL	Citations	Rank
superlines.io/articles/best-ai-overviews-rank-tracking-tools	83	#7 overall (3.9%)
superlines.io/articles/best-chatgpt-tracking-tools	73	#9 overall (3.5%)

The Profound blog post generating 154 citations is a direct lever. If Superlines appeared more favorably — or earlier — in that article, it would improve position across every AI response that cites it. Reach out to the author with updated product information, a case study, or a direct request to reorder the list. You can find the full table under Analytics → Citations → Top Cited URLs.

Step 4: Build a content-to-narrative feedback loop

Goal: Create a system where you publish content, measure how it changed the AI narrative, learn what works, and repeat.

The feedback loop

Most brands publish content and hope it improves their AI presence. The sentiment trend data in Superlines lets you close the loop and actually measure the impact:

Publish or update     Wait 14-21 days       Check sentiment
content with         for AI engines to  →   trend in Superlines
specific narrative   index and adjust
intent
       ↑                                         │
       │                                         ▼
       │                                    Did positive
       │                                    mentions increase?
       │                                    Did position improve?
       │                                    Did neutral → positive?
       │                                         │
       └─────── Apply learnings ─────────────────┘

How to run the feedback loop

Step 1: Tag your content actions. Every time you publish or update a page, record what narrative element you changed:

Date	Page	Narrative change	Target metric
Feb 1	Homepage	Added “best for” statement and quantified capabilities	Neutral → positive rate
Feb 5	Comparison article	Moved brand to position #2 in list, added differentiator	Average position
Feb 10	New “alternative to” page	Published with strong recommendation language	New positive mentions on competitive prompts

Where to start: use Brand Sentiment by Theme to find the highest-priority gaps. Before deciding which pages to update, check the Analytics → Brand Sentiment → Brand Sentiment by Theme section. This shows sentiment performance broken down by topic cluster, so you can direct content effort where the narrative is weakest. Here is Superlines’ current breakdown:

Theme	Positive rate	Analyses	Priority
AI Search Rank & Visibility Tools	77%	53	✅ Solid — protect and replicate
AI Tool Alternatives & Competition	77%	30	✅ Solid — maintain coverage
Generative Engine Optimization	33%	3	⚠️ Weak — highest-priority content gap
Brand Mention & Sentiment Tracking	0%	1	⚠️ Weak — no positive signal at all

GEO and Brand Mention themes drop to neutral or near-zero — these represent the first content experiments to run. Publishing or strengthening pages that frame Superlines’ role in generative engine optimization and brand mention tracking will have maximum sentiment impact because the current baseline is so low. Filter by “Mostly Negative” or “Neutral” in the theme view to surface these gaps in your own data.

Step 2: Check the sentiment trend 2-3 weeks later. Look for:

Positive sentiment spike after a content change — The content worked. Note which narrative element you changed and replicate the approach on other pages.
Flat trend after a content change — The content either was not indexed by AI engines or did not contain strong enough signals. Check indexing status in Google Search Console and Bing Webmaster Tools. If indexed, the content may need stronger differentiation.
Negative spike after competitor activity — A competitor published something that shifted the narrative. Check what they published and whether it directly addresses your brand. Respond with content that provides a more balanced or positive counter-narrative.

Step 3: Build a playbook over 90 days. After running this loop weekly for three months, you will have a clear record of which content actions shift AI sentiment and which do not. Common patterns that emerge:

“Adding external validation (G2 rating, analyst mention) to our homepage shifted 3 neutral extractions to positive within 2 weeks”
“Publishing a comparison article where we are listed first correlates with a position improvement from #8 to #6”
“Updating our ‘best for’ statement from generic to specific moved our brand-specific sentiment from 80% to 90% positive”

These patterns are specific to your brand and category. No generic guide can tell you them. The feedback loop reveals them from your own data.

Step 5: Respond to competitive narrative shifts

Goal: Detect when a competitor’s AI narrative changes — improving or degrading — and respond before the shift compounds.

Monitoring competitor sentiment

The Brand Perception Comparison table in Superlines is not just a snapshot — it is a competitive early warning system. The current numbers put the gap in stark terms: Superlines trails competitors by 13.2 percentage points in average brand visibility (4.0% vs 17.2% average competitor). The top competitor (Semrush) sits at 27.7% — nearly 7x Superlines’ visibility.

The top Fan-Out Queries driving those citations reveal exactly where the citation gap lives:

Brand	Top Fan-Out Query	Citations
Semrush	”generative engine optimization market size”	46,528
Profound	”best chatgpt rank tracker software”	25,517
Peec AI	”best chatgpt rank tracker tool”	20,769
Superlines	”generative engine optimization market size”	2,969

Superlines and Semrush share the same top query topic, but Semrush generates 15x more citations from it. This is a concrete signal: the content gap on GEO market size queries is where the most citation volume is available. You can find competitor Fan-Out Queries under Analytics → Competitor Analysis.

Check the table weekly for three signals:

Signal 1: A competitor’s positive rate improved. If Profound’s positive rate moved from 73% to 85% in two weeks, something changed. A new article, a PR mention, or updated content is causing AI to describe them more favorably. Find the source by checking recent competitor citations and mentions. If it is a third-party article that now describes them positively, you need equivalent coverage.

Signal 2: A competitor’s position improved. If a competitor moved from position #8 to #5, they are being cited earlier in AI responses. This usually means a high-authority source started listing them higher, or they published content that directly answers a key prompt more effectively. Study their recent content changes.

Signal 3: A competitor gained negative sentiment. This is an opportunity, not a threat. If a competitor’s negative rate went from 0% to 5%, AI is picking up critical language from somewhere — a bad review, a comparison article noting limitations, or a forum discussion. Create “alternative to [competitor]” content that addresses the specific concerns AI is raising. Buyers who see negative AI descriptions of a competitor are actively looking for an alternative.

Proactive narrative defense

The best defense against competitive narrative shifts is having more sources that describe your brand positively than competitors can publish against you. This means:

Breadth of positive sources — Your brand described well across your site, third-party articles, reviews, and community content creates resilience. If one source shifts negative, the overall narrative holds.
Freshness — Regularly updated content signals that your brand is active and current. Stale content gets deprioritized by AI engines, leaving room for competitor narratives to fill the gap.
Specificity — Generic descriptions are easy to displace. Specific, data-backed descriptions are harder to override because AI engines have concrete evidence to cite.

Step 6: Move from informational contexts to recommendation contexts

Goal: Shift your brand mentions from neutral informational responses (“here are some options”) into positive recommendation responses (“the best tool for this is”).

The informational-recommendation gap

This is the most underappreciated insight in AI search optimization. In Superlines’ data, 70.4% of brand-specific sentiment is positive — but only 21% of overall responses are positive. The other 79% are neutral or mixed in tone.

This means most mentions happen inside responses like:

“There are several AI search visibility tools available, including Semrush, Superlines, Peec AI, and Profound. Each offers different features for tracking brand mentions across AI platforms.”

Rather than:

“For tracking brand visibility across AI platforms like ChatGPT and Perplexity, Superlines is a strong choice. It tracks mentions, citations, and sentiment across 10+ AI engines and provides competitive analysis at the prompt level.”

Both responses mention the brand. Only the second one drives action.

What determines response type

AI engines generate recommendation-style responses when:

The prompt asks for a recommendation — “What is the best tool for…” vs “What tools exist for…”
The AI has strong enough evidence to recommend — Multiple authoritative sources that agree on a top pick
The content it cites is recommendation-oriented — Pages that structure content as “best for X” rather than “list of options”

How to shift toward recommendation contexts

Action 1: Target recommendation prompts. In your Superlines tracked prompts, some queries naturally produce recommendation-style responses and others produce list-style responses. Prompts like “What is the best AI search visibility tool for marketing teams?” generate recommendations. Prompts like “What AI search visibility tools exist?” generate lists. Focus your content on the recommendation-type prompts.

To identify which prompts produce recommendations, check the Overall Response Analysis sentiment for each prompt. Prompts with high positive overall sentiment are recommendation prompts. Prompts with mostly neutral overall sentiment are list prompts.

The highest-priority list prompts to target are the ones with a high overall branded response rate but 0% Superlines brand visibility. In Superlines’ current tracked prompt data (Visibility → Prompts, sorted by Brand Visibility ascending):

Prompt	Branded resp. rate	Superlines visibility
”Best free chatgpt ranking tracking tool”	91.7%	0.0%
“What are the best starter tools for Generative Engine Optimization (GEO)“	87.5%	0.0%

These prompts confirm that AI is generating category-branded list responses — competitors are being included — but Superlines is not making the list. These are the highest-priority prompts for Step 2 content intervention: adding specific, differentiated recommendation language to pages that target these exact queries.

Action 2: Structure your content to enable recommendations. When your content explicitly states “best for [use case]” and provides evidence, AI engines have the raw material to generate a recommendation. When your content lists features without taking a position, AI defaults to informational mode.

On your most-cited pages, add clear recommendation language:

Before (informational)	After (recommendation-enabling)
“Superlines tracks AI search visibility"	"Superlines is the most comprehensive platform for tracking AI search visibility, covering 10+ AI engines that competitors typically miss"
"Features include dashboards and analytics"	"Marketing teams choose Superlines for its fan-out query analysis — a unique capability that reveals the hidden searches AI engines perform when answering user questions"
"Pricing starts at €89/month"	"At €89/month, Superlines offers the most accessible entry point for teams that need AI search intelligence without enterprise pricing”

Each “after” version gives the AI a clear reason to recommend rather than merely list.

Action 3: Build third-party recommendation signals. AI engines weight independent recommendations more than self-promotion. A G2 review that says “switched from [competitor] to Superlines and saw immediate improvement in our AI search tracking” is stronger than any language on your own site. A comparison article that concludes “Superlines is our top pick for teams focused on AI search visibility” creates a recommendation signal the AI can cite.

Bringing it together: The narrative quality ladder

AI search optimization is usually discussed in terms of visibility — are you mentioned or not? But once you have baseline visibility, the quality of how you are mentioned becomes the more important lever. Think of it as a ladder:

Level	State	Priority action
1. Invisible	AI does not mention your brand at all	Build content surface area — more pages targeting more prompts (Practical GEO guide)
2. Mentioned neutrally	AI mentions your brand but without differentiation	Add the five narrative elements to key pages (Step 2 of this guide)
3. Mentioned positively	AI describes your brand favorably	Work on position — move from #8 to #3 (Step 3)
4. Recommended	AI actively recommends your brand for specific use cases	Shift from informational to recommendation contexts (Step 6)
5. Category-defining	AI uses your brand as the reference point for the category	Build breadth of recommendation signals across third-party sources

Most brands focus all their energy on Level 1 (getting mentioned) and skip Levels 2-5. But a brand with 50 neutral mentions has less business impact than a brand with 15 positive recommendations. Quality of narrative compounds in the same way that visibility compounds — AI engines that learn to describe you positively will continue to do so across new prompts and contexts.

The weekly narrative check

Add these four checks to your existing weekly GEO routine:

Read 5 recent AI responses from the Latest Mentions tab. Note the exact language used. Is it specific or generic? Recommendation or list?
Check the Brand Perception Comparison table for any competitor movement. Has anyone’s position or positive rate changed?
Review the sentiment trend for spikes or dips. If you published content in the last 2-3 weeks, check whether sentiment shifted.
Compare your positioning statement to the AI’s average description. Is the gap narrowing or widening?

These four checks take 10 minutes and tell you whether your narrative work is paying off.

Where to find each data point in Superlines

Step	What you’re looking for	Navigation path
Step 1 — diagnose sentiment	Brand-Specific vs Overall Response split	Analytics → Brand Sentiment → toggle between the two analysis tabs
Step 1 — engine breakdown	Brand Visibility by Platform	Visibility → Overview → “Brand Visibility By Platform” card, or use the engine filter on any page
Step 3 — position + competitive comparison	Brand Perception Comparison table	Analytics → Brand Sentiment → scroll to “Brand Perception Comparison”
Step 3 — top cited competitor URLs	Which third-party articles are driving competitor citations	Analytics → Citations → “Top Cited URLs” table
Step 4 — sentiment by topic	Which theme clusters have weak sentiment	Analytics → Brand Sentiment → “Brand Sentiment by Theme” section, filter by “Mostly Negative” or “Neutral”
Step 5 — competitor monitoring	Weekly competitor visibility and citation changes	Analytics → Competitor Analysis → Competitor Performance Overview table
Step 6 — list vs recommendation prompts	High branded-response-rate prompts where you have 0% visibility	Visibility → Prompts tab → sort by Brand Visibility ascending