Har någon lyckats lista ut hur AI faktiskt bestämmer sig för att citera ditt varumärke? Attributionsmysteriet driver mig till vansinne

You’re right that traditional SEO thinking doesn’t fully apply here. Let me break down how attribution actually works.

The Attribution Hierarchy:

1. Linked citations - Most valuable. Perplexity does this well with numbered footnotes. This is what drives actual traffic.

2. Brand mentions - AI says “According to [Your Brand]…” but no link. Builds awareness but no clicks.

3. Implicit citations - AI synthesizes your information without naming you. Worst case scenario.

What triggers attribution:

The key difference from SEO: AI systems use Retrieval-Augmented Generation (RAG) to pull current content. They’re making real-time decisions about which sources to cite based on:

• Content recency and freshness
• Clarity of your expertise signals
• How well your content matches the query intent
• Whether your information can be easily extracted

How I measure this:

I use Am I Cited to track attribution across platforms. The tool differentiates between linked vs unlinked mentions and shows position data. That’s crucial because a first-position citation is worth 5x a fifth-position mention.

Your 30% Perplexity citation rate is actually decent. But if you’re always position 4-5, you’re getting visibility without clicks.

I’ve been deep in attribution data for 8 months. Here are the patterns I’ve found:

Platform-specific attribution behaviors:

Perplexity:

• Most transparent about sources
• Heavy recency bias - fresh content gets cited more
• Loves structured data and clear headings
• Citation rate correlates strongly with content comprehensiveness

ChatGPT with browsing:

• Less consistent about attribution
• Tends to synthesize from multiple sources
• Brand authority seems to matter more than content freshness
• Getting mentioned by name (even without links) builds long-term recognition

Google AI Overviews:

• Draws heavily from content already ranking in top 10
• Strong preference for .edu, .gov, and established publishers
• Featured snippet content often gets pulled into AI Overviews
• Schema markup matters here

The attribution gap:

I tracked 200 prompts over 3 months. Brands with strong third-party coverage (press, industry mentions, Wikipedia) got 3x more attributions than brands with only their own content.

External validation is the key signal AI systems use to decide trust.

15 years in SEO here. The attribution game is fundamentally different.

Old model: Optimize page → Rank higher → Get clicks

New model: Build authority → Get cited → Build more authority (flywheel)

The biggest mindset shift: you’re not optimizing TO BE the answer anymore. You’re optimizing to be CITED as part of the answer.

What actually moves attribution:

1. Entity clarity - AI has to know who you are. Schema markup, consistent naming, Wikipedia presence all help.

2. Content extractability - Short paragraphs, bullet points, tables, FAQ structures. If AI can easily pull a quote, it will.

3. Source triangulation - AI cross-references sources. If multiple authoritative sites mention your brand positively, you’re more likely to get attributed.

4. Recency signals - Visible publication dates, regular updates, “Last updated” timestamps.

The clients I work with who improved attribution fastest did it by focusing on #3 - getting mentioned on other authoritative sites, not just publishing more content.

Small brand perspective here - we’re competing against companies 100x our size for attribution.

What’s actually working for us:

1. Niche down hard - We stopped trying to get cited for broad queries. Focused on very specific use cases where we have genuine expertise.

2. Expert content that AI can’t replicate - Our CEO does original research and shares proprietary data. AI cites this because it can’t generate it.

3. Reddit and Quora presence - Authentic participation (not spam) in communities. These platforms feed AI training data.

4. Speed to publish on trends - When something new happens in our industry, we’re first to publish thoughtful analysis. Recency wins.

Our attribution metrics after 6 months:

• Went from 5% citation rate to 23%
• Average position improved from 4.1 to 2.3
• 40% of our demo requests now mention “saw you in ChatGPT/Perplexity”

We use Am I Cited to track this. The competitive comparison feature showed us exactly which queries we should target.

Enterprise scale here - we track attribution across 500+ prompts in 12 markets.

The insight that changed everything:

Attribution isn’t just about individual pieces of content. It’s about AI’s overall perception of your brand entity.

We mapped out how AI describes our brand vs competitors. Discovered:

• AI categorized us as “legacy” when we’re actually innovative
• Competitors with weaker products were described as “cutting-edge”
• This perception drove attribution decisions

How we fixed it:

1. Launched a PR campaign specifically targeting AI training sources
2. Updated all content to emphasize innovation and modern approach
3. Got analyst coverage that explicitly positioned us as market leader
4. Monitored AI responses weekly to track perception shift

Took 4 months, but attribution rates doubled and our brand is now described accurately.

What we track:

• Citation frequency by platform
• Position distribution trending
• Brand sentiment in AI responses
• Share of voice vs top 5 competitors

Am I Cited handles all of this in one dashboard. The executive reports are what sold leadership on the investment.

Documentation perspective here - I write technical docs for a dev tools company.

What I’ve learned about documentation and AI attribution:

Technical docs get cited A LOT by AI, especially for “how to” queries. But only if structured correctly.

Format that works:

• Clear H2/H3 hierarchy matching question patterns
• Code blocks with complete, working examples
• Definition lists for terminology
• Step-by-step numbered instructions

Format that fails:

• Long prose explanations
• Buried answers requiring context
• Outdated examples or deprecated code
• Inconsistent terminology

We restructured our docs to be more “AI-extractable” and saw a 40% increase in Perplexity citations within 6 weeks.

The key insight: write like you’re answering a Stack Overflow question, not writing a textbook chapter.

I run an agency specializing in AI attribution. Here’s my framework:

The Attribution Triangle:

1. Authority - Do AI systems recognize you as an expert? (Entity signals, backlinks, third-party mentions)

2. Accessibility - Can AI easily extract and cite your content? (Structure, freshness, clarity)

3. Relevance - Does your content match query intent? (Comprehensive coverage, question-answer format)

You need all three. Missing any one kills your attribution rate.

Most common mistakes I see:

1. Publishing thin content - AI needs depth to cite confidently
2. Ignoring entity optimization - AI doesn’t know who you are
3. Poor content structure - Information is buried
4. No third-party validation - Self-published content only
5. Not tracking attribution - Can’t improve what you don’t measure

The measurement stack I recommend:

Am I Cited for automated attribution tracking + manual spot checks for qualitative insights + GA4 for referral traffic from AI platforms.

Attribution optimization is a marathon, not a sprint. Expect 3-6 months for significant improvement.

The competitive angle is what opened my eyes.

We were so focused on our own attribution that we missed what competitors were doing. Started monitoring them with Am I Cited and discovered:

• Main competitor was getting cited 3x more often
• They had invested heavily in comparison content
• Their content structure was more AI-friendly
• They were getting press coverage we weren’t

What we changed:

1. Created comprehensive comparison guides for every competitor
2. Restructured content with clearer headings and summaries
3. Launched a press campaign targeting industry publications
4. Added more proprietary data and original insights

Results after 4 months:

• Our attribution rate caught up to competitor
• Now appearing first in 35% of relevant queries
• Brand mentions in AI responses doubled

The competitive intelligence was the missing piece. You can’t optimize in a vacuum.

This thread has been incredibly helpful. Summarizing my takeaways:

Key insights:

1. Attribution ≠ mentions - Linked citations are what matter most
2. Position matters as much as frequency - First position is worth 5x later positions
3. Third-party validation is critical - AI trusts brands that others trust
4. Content structure affects extractability - Make it easy for AI to cite you
5. This requires ongoing monitoring - Can’t improve what you don’t measure

My action plan:

1. Set up proper attribution tracking with Am I Cited
2. Audit our content for AI-extractability
3. Prioritize getting more third-party coverage
4. Focus on queries where we should be winning but aren’t
5. Monitor competitor attribution strategies

The shift from “rank for keywords” to “get cited by AI” is real. Thanks everyone for the insights.