How to Make AI Actually Recommend Your Web3 Project (Not Just Google Rankings)

Originally published on Belkin Marketing Blog

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TL;DR: Google rankings don't matter if ChatGPT never mentions your protocol. Here's how to fix that.

The Problem Nobody's Talking About

Your DeFi protocol ranks #1 on Google for "best yield farming platform."

Congrats. But when 40%+ of users ask ChatGPT or Perplexity "what's the safest yield farming protocol in 2026," your project doesn't exist.

Why? Because AI search doesn't work like Google search.

I've spent 17+ years in crypto marketing, and 2025-2026 is when I watched traditional SEO become... not dead, but insufficient. Let me show you what's actually happening and how to win at this new game.

How I Discovered This (The Hard Way)

We were working with a solid DeFi lending protocol. Great product. Real TVL. Actual users. Ranked well on Google.

Then a founder DMed me: "Why does ChatGPT recommend three competitors but never mentions us?"

I tested it. Asked ChatGPT variations of their target queries. Perplexity. Claude. Gemini.

Result: Zero mentions. Across hundreds of queries.

But here's the kicker—when I searched Google for the same queries, they ranked top 5. The disconnect was obvious:

• Google sees: Their SEO-optimized landing pages
• AI sees: Scattered mentions across the web, none authoritative enough to cite

That's when I realized we'd been playing the wrong game.

The Data That Changed My Strategy

Let me hit you with some numbers from Q4 2025/Q1 2026 research:

• [ChatGPT: 1+ billion monthly users] (yes, billion with a B)
• [40% of users now ask AI first], before traditional search
• [9% of desktop searches] now trigger Google AI Overviews
• [Traffic from AI search converts at 2.5x] the rate of organic search
• [85% of AI citations] come from earned media, not brand websites

That last one is critical. Your beautiful landing page? AI doesn't care. What AI cares about:

• CoinDesk articles mentioning you
• Reddit discussions about your protocol
• GitHub documentation quality
• Review platform presence

How AI Models Actually Evaluate Web3 Projects

After testing this across 50+ crypto projects, here's the pattern I found:

Layer 1: Content Relevance

AI doesn't match keywords—it understands concepts.

Bad approach: Stuff your page with "best DeFi protocol" 47 times

Good approach: Create comprehensive content that genuinely answers "how does liquid staking work and which protocols are safest"

Example from a project we worked with:

# Liquid Staking Explained (For Humans)

Liquid staking lets you earn staking rewards while keeping 
your assets liquid for DeFi. Here's how:

1. You deposit ETH with a protocol like Lido
2. Receive stETH (liquid staking derivative)
3. Use stETH anywhere in DeFi while earning staking yields
4. Redeem stETH for ETH whenever you want

**Risks:** Smart contract risk, slashing risk, liquidity risk
**Mitigation:** Audits from Trail of Bits, insurance pools, diversification

Why this works: Natural language. Answers the actual question. Addresses obvious follow-ups.

Layer 2: Authority Signals

AI weighs these Web3-specific signals:

High Authority:

• Features in CoinDesk, The Block, Decrypt
• Active GitHub (frequent commits, documentation)
• Audit reports from reputable firms
• TVL growth covered by analysts

Medium Authority:

• Community discussions (Reddit r/DeFi, r/ethfinance)
• Twitter threads from respected voices
• Medium technical posts
• YouTube explainers with transcripts

Low Authority:

• Your own blog (yes, really)
• Press releases
• Promotional content
• Paid placements

This was brutal to learn. We spent months optimizing a client's blog only to realize AI mostly ignored it. Then got them featured in one CoinDesk article → instant citations.

Layer 3: Entity Recognition

AI needs to know you're a distinct thing.

Common problem: "Acme Finance" gets confused with "Acme Token" and "ACME Protocol."

Solution:

• Consistent naming everywhere
• Wikipedia presence (even a stub helps)
• Structured data (JSON-LD schema)
• Clear differentiation: "The only liquid staking protocol on Avalanche"

The 7-Step Implementation (What Actually Works)

After implementing this for 30+ Web3 projects, here's the system:

Step 1: Audit Current AI Visibility (Week 1)

Don't guess. Measure.

Manual testing:

1. List 20 queries your target users ask
2. Run each in ChatGPT, Perplexity, Claude
3. Document: Mentioned? Position? Accuracy?

Example queries for a DeFi lending protocol:

- "safest DeFi lending protocol 2026"
- "how to earn yield on stablecoins with low risk"
- "compare Aave vs Compound vs [YourProtocol]"
- "DeFi lending protocol with best security audits"

Run these across platforms. If you're not mentioned in the top 3 results for relevant queries, you have work to do.

Tool that help (but manual is still most reliable):

• [LLMrefs]: Track keywords across ChatGPT, Perplexity, Gemini, Claude

Step 2: Build Semantic Topic Clusters (Weeks 2-4)

Forget random blog posts. AI rewards comprehensive topical authority.

The Hub-and-Spoke Model:

Hub (Pillar Page): Comprehensive guide (3,000-5,000 words)
Example: "The Complete Guide to Liquid Staking: Mechanisms, Risks, Protocol Comparison"

Spokes (Supporting Pages): Deep dives on sub-topics (1,500-2,500 words each)

• "Liquid Staking vs Traditional Staking: Complete Comparison"
• "How Liquid Staking Derivatives Work Technically"
• "Security Risks in Liquid Staking (And How to Mitigate Them)"
• "Yield Strategies Using Liquid Staking Tokens"

Each spoke links to the hub. Hub links to all spokes. This creates topical authority AI recognizes.

Why it works: [Research shows] AI recognizes topical authority through content clusters. 20 interconnected pages on liquid staking > 1 comprehensive guide.

Step 3: Write for AI Comprehension

This is different from SEO writing.

Front-load answers: AI extracts clean answers from well-structured content.

❌ Bad:

Liquid staking has become increasingly popular in the DeFi 
ecosystem, with numerous protocols offering various approaches 
to solving the capital efficiency problem inherent in 
proof-of-stake networks...

✅ Good:

Liquid staking lets you earn staking rewards while keeping 
your assets liquid for DeFi. You deposit ETH with a protocol 
like Lido, receive stETH, and use that token anywhere in DeFi 
while still earning staking yields.

Use conversational queries: Average AI query = 23 words, conversational.

Target: "explain the difference between optimistic and zk rollups for someone building a DeFi app"

Not: "rollup comparison" or "optimistic vs zk rollups"

Include citable data:

- Lido holds $22.3B TVL (as of January 2026, per DefiLlama)
- 500K+ daily active users
- 7-day average APY: 4%
- Security audit by Trail of Bits (December 2025)

AI loves specific, verifiable numbers with sources.

Step 4: Earn High-Authority Media Coverage

This is where most Web3 projects fail—and where winners dominate.

The brutal truth: [85% of AI citations come from earned media], not your website.

Tier 1 Publications (maximize these):

• General: Forbes, WSJ, TechCrunch, The Verge
• Crypto: CoinDesk, The Block, Decrypt, Cointelegraph
• Developer: GitHub documentation, dev.to, Hackernoon

Tactical approaches:

A. Founder Thought Leadership

• Write guest posts for top publications
• Offer unique data (on-chain analysis, user surveys)
• Comment on industry news with expert perspective

Example: We helped a DeFi founder write a CoinDesk analysis piece on yield farming risks. That single article generated more AI citations than 6 months of blog content.

B. Product Launches

• Exclusive announcements to crypto media
• Embargoed access for in-depth reviews
• Data reveals (TVL milestones, partnership announcements)

C. Original Research

• Publish market analysis or on-chain data studies
• Create tools others cite (yield calculators, risk assessors)
• Educational content publications want to reference

D. Community Amplification

• Encourage authentic Reddit discussions
• AMAs on r/CryptoCurrency, r/DeFi, r/ethfinance
• Create shareable content community members actually want to share

Step 5: Technical SEO Foundation

Your content can be perfect, but if Google can't crawl it, AI won't train on it.

Critical elements:

Schema Markup (see end of article for Wix-specific implementation):

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Your Protocol Name",
  "applicationCategory": "DeFi Protocol",
  "description": "Clear one-sentence description"
}

Crawl ability:

• Server-side rendering for JS-heavy sites
• Fast load times (<2s First Contentful Paint)
• Clean, semantic HTML5
• XML sitemap updated automatically

Internal Linking:

• Every page within 3 clicks of homepage
• Cluster content interconnected
• Descriptive anchor text

Step 6: Reddit Strategy (AI's Secret Weapon)

[Reddit has become] one of the most influential sources for AI training data.

Citation frequency:

• ChatGPT: 12%
• Perplexity: 47% (nearly half!)
• Google AI Overviews: 21.0%

The RIGHT way (critical—don't screw this up):

1. Participate authentically for weeks before mentioning your project
2. Answer questions, share insights, help others
3. Build karma and credibility
4. Only then share your experience/project

Wrong approach: Create account, immediately post about your protocol → Get destroyed by community

Right approach: Spend 3-4 weeks being genuinely helpful → When relevant question appears, share your experience → Community upvotes because you've earned trust

Example posts that work:

• "Case Study: How we turned a defamation attack into our best marketing (Lessons for crypto projects)"
• "What I learned implementing liquid staking for 50K users"
• "The non-obvious risks in DeFi yield farming (from running a protocol)"

Frame as educational. Be humble. Focus on helping others. Let community support speak for you.

Step 7: Track and Optimize (Ongoing)

LLM visibility isn't set-and-forget.

Weekly:

• Spot-check 5-10 key queries
• Track mention frequency, position, accuracy
• Document changes

Monthly:

• Full audit of all tracked queries
• Sentiment analysis (how is your protocol described?)
• Citation sources (which content gets cited?)
• Competitor movements

Quarterly:

• Deep strategy review
• Content gap analysis
• Earned media ROI
• Budget reallocation based on performance

Key Metrics:

1. Visibility: How often you appear in AI answers
2. Mentions: Frequency regardless of citations
3. Citations: When AI links to your content
4. Context: How you're positioned (positive/negative/neutral)
5. Sharing: How often AI recommends your protocol

Platform-Specific Tactics

Each AI platform has preferences:

ChatGPT

Priority: Training data authority, brand popularity

Strategy:

• Get featured in major publications
• Build Wikipedia presence
• Create extensive GitHub documentation
• Establish brand as category leader

Perplexity

Priority: Real-time content, Reddit, YouTube

Strategy:

• Weekly content updates
• Active Reddit participation
• Video content with transcripts
• News-worthy announcements
• Live data and current metrics

Google Gemini

Priority: Traditional SEO signals, Google ecosystem

Strategy:

• Optimize Google Business Profile
• Strong reviews across Google properties
• YouTube optimization
• Traditional on-page SEO

Claude

Priority: Well-structured content, technical accuracy

Strategy:

• Comprehensive technical documentation
• Balanced analysis (pros and cons)
• Clear logical flow
• Proper heading hierarchy

Common Mistakes That Kill LLM Visibility

After auditing 100+ crypto projects, here are the failures I see repeated:

Mistake 1: Optimizing Only for Google

Reality: Users have migrated. Google rankings ≠ AI citations.

Mistake 2: Thin Content Across Too Many Topics

Better: 5 comprehensive topic clusters than 50 shallow posts.

Mistake 3: Ignoring Earned Media

Your blog matters less than you think. One CoinDesk feature > 100 blog posts.

Mistake 4: Writing for Developers When Users Are Non-Technical

Match content to who's actually asking AI. If retail users are targets, write for them.

Mistake 5: No Monitoring

Can't improve what you don't measure. Run manual audits regularly.

Mistake 6: Inconsistent Entity Information

Confusing AI with different names/positioning kills visibility.

Mistake 7: Expecting Instant Results

LLM visibility builds over 3-6 months as AI models update. Be patient.

Real Results: What This Looks Like

Client example (DeFi protocol, can't share name):

Starting point (Month 0):

• Google ranking: #3 for main keyword
• ChatGPT mentions: 0 out of 50 test queries
• Perplexity mentions: 1 out of 50
• Monthly organic traffic: 15K visits

After 4 months:

• Google ranking: #2 (slight improvement)
• ChatGPT mentions: 23 out of 50 test queries
• Perplexity mentions: 31 out of 50
• Monthly organic traffic: 15.5K (slight increase)
• AI-referred traffic: 2.8K visits (new channel)
• Conversion rate from AI traffic: 8.3% (vs. 3.1% from Google)

The game-changer wasn't traffic volume—it was traffic quality. Users coming from AI recommendations already understood the protocol and were ready to use it.

Tools and Resources

Tracking:

• [LLMrefs]: Multi-platform tracking

SEO Foundation:

• Google Search Console: Track which content gets indexed
• Ahrefs/SEMrush: Monitor rankings and backlinks
• Schema.org: Structured data reference

Community:

• r/CryptoCurrency
• r/DeFi
• r/ethfinance
• Dev.to (you're here!)

The Hard Truth

If your Web3 project isn't optimizing for LLM visibility in 2026, you're invisible to an increasingly large portion of your potential users.

Google rankings still matter. But they're not enough.

Start here:

1. Run a manual AI visibility audit this week
2. Identify your 3 core topic clusters
3. Create your first comprehensive hub content
4. Start authentic Reddit participation
5. Pitch your first tier-1 media piece

The protocols dominating AI search in 2027 are building their foundation now, in early 2026.

Will yours be one of them?

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*I'm Iaros, founder of Belkin Marketing. We've spent 17+ years building brands from the ground up, and the last 2 years specifically optimizing for AI visibility. If you're building in Web3 and want help with this, please DM me.

Follow for more on Web3 marketing, AI search optimization, and crypto growth tactics.

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Discussion Questions

• Have you tested how often AI mentions your project?
• What's been your biggest challenge with AI visibility?
• Anyone seeing meaningful traffic from ChatGPT/Perplexity yet?

Drop your experiences in the comments—would love to learn what's working for others.

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Tags: #web3 #ai #seo #blockchain #crypto #defi #marketing #llm #chatgpt #perplexity

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