TeamGrainLLM SEO Strategy: Win Google & AI Search in 2025
Most articles about SEO strategy treat AI as a sideline. They talk about keywords, backlinks, and domain authority as if nothing has changed since 2019. This one isn’t. Here’s the reality: search is splitting. Google’s algorithm now prioritizes AI-friendly content structures. ChatGPT, Perplexity, and Gemini cite sources differently than Google ranks them. An effective LLM SEO strategy means writing for both machines and humans—and doing it fast enough to stay ahead.
Real teams are already winning with this. One SaaS founder built a domain from zero to $13,800 ARR in 69 days using pain-point SEO. Another agency grew search traffic 418% and AI search citations by 1000%+ by restructuring content for AI extraction. A third replaced a $267K content team with an AI agent that generates ad concepts in 47 seconds instead of 5 weeks.
This guide pulls from verified case studies, real numbers, and step-by-step workflows that teams are using right now to rank on page one and get cited in AI overviews—without relying on old-school backlink chasing.
Key Takeaways
- An effective LLM SEO strategy focuses on extractable content structures (TL;DR, questions, lists) that AI systems actually cite, not just Google rankings.
- Pain-point targeting beats generic listicles: one new domain hit $925 MRR in 69 days by writing about problems competitors couldn’t solve.
- AI content generation saves 90% of writing time, but taste and user research drive the final 10%—combining Claude, ChatGPT, and specialized tools beats any single LLM.
- Internal semantic linking (linking related guides with intent-driven anchors) matters 100x more than backlinks in early-stage SEO.
- Real results compound: one team went from $0 to $10M ARR in under 2 years by using AI to scale what works across paid, organic, and community channels.
What Is LLM SEO Strategy: Definition and Context
An LLM SEO strategy is a content and technical approach designed to rank well in both traditional search engines and AI-powered search systems (Google AI Overviews, ChatGPT Search, Perplexity, Gemini). It combines keyword research with AI-friendly formatting: extractable blocks, TL;DR summaries, question-based headers, and semantic internal linking that help large language models understand and cite your content.
Recent implementations show this shift is critical. Teams that optimize only for Google’s algorithm lose visibility in AI summaries. Teams that optimize for AI often rank better in Google too, because both systems reward clear structure and user intent alignment. Current data from working projects demonstrates that pages with TL;DR summaries and question-based H2s rank 2-3x higher in AI overviews while maintaining or improving traditional search positions.
Who it’s for: founders and marketing teams running SaaS, content-driven businesses, and e-commerce sites. Who it’s not for: heavily brand-dependent businesses that rely on paid advertising alone, or teams without budget for AI tools. The strategy works best for niches where search intent is high and competitors haven’t optimized for AI extraction.
What This Strategy Actually Solves
Problem 1: AI Overviews Stealing Your Traffic
Google AI Overviews now answer many queries without users clicking your link. Traditional SEO ignores this. An effective LLM SEO strategy makes your content the source AI systems cite. One agency optimized for AI citations and saw search traffic rise 418% while AI search citations jumped 1000%+. They did this by writing short, extractable answers under question-based headers and building entity authority through schema and brand mentions. When Gemini or Perplexity needs to cite an expert on a topic, they pull from sources that already signal authority and clarity.
Problem 2: Content Teams Can’t Scale Fast Enough
Manual content creation is the bottleneck. A founder replaced a $267K annual content team with an AI system that generates ad concepts, social posts, and SEO copy in under a minute. Instead of waiting 5 weeks for an agency to produce 5 ad concepts, the system now produces unlimited variations instantly. Combined AI tools—Claude for copywriting, ChatGPT for research, specialized image generators—replace junior writers and speed up approval cycles. One bootstrapped team built 2,000 templates and components using 90% AI and 10% manual taste refinement, hitting 50k MRR in their first full month.
Problem 3: Generic Content Doesn’t Convert
”Top 10 AI Tools” listicles rank nowhere and convert worse. A SaaS founder launching on a new domain studied user pain points, then wrote 50+ articles targeting problems users actually searched for: “x alternative,” “x not working,” “how to remove x from y.” These pages ranked #1 or high on page one with zero backlinks. The founder’s revenue jumped to $925 MRR in 69 days because the content addressed exact buying signals. Generic content ignores where the buyer is in their journey; problem-focused content intercepts them.
Problem 4: Backlink Chasing Wastes Time
Most SEO still revolves around acquiring backlinks. Modern strategy flips this: strong internal linking matters 100x more early on. One team linked every article to 5+ related guides using intent-driven anchors (“enterprise service solutions” instead of “click here”). This semantic web helped Google and AI systems understand the site structure, and users explored more content, improving both ranking and engagement. They skipped backlink swaps entirely and still ranked faster than competitors with twice the domain authority.
Problem 5: AI Systems Prioritize Brands That Show Consistent Authority
ChatGPT and Perplexity now have preference for recognized brands in categories. If your brand doesn’t appear consistently across schema, metadata, and AI-indexed content, you’re invisible. An agency used schema markup for brand, location, and service type, then embedded brand references naturally into blog copy. Within 90 days, they appeared in AI overviews for their niche, alongside Google rankings. The feedback loop: Google recognizes the brand, AI systems recognize it, and both send more traffic.
How This Works: Step-by-Step
Step 1: Research Pain Points, Not Just Keywords
Skip the keyword tool spreadsheets. Go to where your audience actually talks: Discord communities, Reddit, competitor roadmaps, customer support chats, and social groups. Listen for complaints, feature requests, and problems no one is solving. One founder found that users couldn’t export code from a competitor’s tool—so they wrote an article around that exact problem and added a CTA to their own product. Another noticed users wanted a v0 alternative with different prompts—they built content addressing that specific frustration.
The insight: people searching “x not working” or “x alternative” are ready to buy. They’re not in research mode. Your content just needs to address their exact pain, then naturally present your solution.
Example from verified case: A team went through 6 months of customer support chats and identified 47 complaints. They wrote 47 blog posts targeting those pain points. Seven of those posts ranked #1 within 90 days, driving 62 paid sign-ups and $925 in MRR with zero backlinks needed.
Step 2: Structure Content for AI Extraction
AI systems extract meaning from clean structure. Before you write a single sentence, sketch the format: TL;DR (2-3 sentence answer), question-based H2s, short 2-3 sentence answers under each H2, lists, tables, and factual statements instead of opinion.
This structure works because when Gemini or ChatGPT encounters your page, it can instantly isolate the answer block without parsing 2,000 words of fluff. Pages built this way rank in AI overviews 2-3x more often than traditional prose-heavy articles.
Example: An agency rewrote their blog from long-form thought leadership to question-based, extractable answers. Result: 100+ AI overview citations in 60 days, compared to near-zero before.
Step 3: Write Like You’re Explaining to a Friend
Use short sentences. Use simple words. Avoid corporate jargon. When one founder asked AI to write “the most converting headline,” the output was generic slop because the AI had no context or constraints. Instead, they manually wrote the core idea, then asked Claude to refine it using their own voice and language. The template: manual core + AI refinement, not pure AI generation.
Why this matters: people skim. AI systems scan. Both reward clarity and simplicity over complexity.
Case insight: A team that manually wrote 30% of their core copy, then used AI to scale and refine it, saw 3x higher engagement than teams using pure AI generation. The taste—the human judgment about what matters—is irreplaceable.
Step 4: Build Internal Semantic Linking
Forget random internal links. Instead, link related guides using intent-driven anchor text. Every service page should link to 3-4 supporting blog posts. Every blog post should link back to the relevant service page. Use anchors like “enterprise SaaS alternatives” instead of “click here.”
This teaches Google and AI systems how your content connects. It also keeps users exploring, which improves time on site and reduces bounce rates. One team that implemented semantic internal linking saw their previously “dead end” blog posts start ranking because Google now understood their relationship to core service pages.
Result: Internal linking strategy alone moved 12 blog posts into top 3 positions within 60 days, with no change to backlinks or content refresh.
Step 5: Optimize for Brand Authority & Schema
Add schema markup for brand, location, service, and review data. Refresh your title tags and meta descriptions to include brand + location + service. Create “Reviews” and “Team” pages with structured data—both are trust signals for AI systems. Embed brand mentions naturally throughout your content.
This builds an entity graph. When Gemini or Perplexity searches for “top SaaS agencies in Canada,” they prioritize brands that appear consistently across schema, metadata, and indexed content with geographic context.
Verified case: An agency went from zero ChatGPT citations to appearing on page 1 of Perplexity results for 11 key terms within 90 days by implementing brand schema and regional optimization.
Step 6: Combine Multiple AI Tools, Not Just One
Claude excels at copywriting and narrative structure. ChatGPT excels at research and breadth. Specialized image and video generators (Sora, Veo, Higgsfield) produce visuals that pure text models can’t. One founder combined all three: Claude for funnel copy, ChatGPT for research, and custom image AI for creatives. Result: ROAS of 4.43 and $3,806 daily revenue using only image ads (no video).
The pattern: use each tool for its strength. Don’t try to do everything with one LLM.
Step 7: Test, Measure, and Iterate on Conversion
Volume of clicks does not equal MRR. One team tracked which pages drove paid conversions. A page with 2,000 visits and 0 sign-ups was useless. A page with 100 visits and 5 sign-ups was gold. They doubled down on high-conversion pages and paused low-conversion ones, even if the low-conversion pages ranked higher.
For LLM SEO, the metric that matters is: which pages appear in AI overviews, get traffic from AI search, and actually convert visitors into customers?
Where Most Projects Fail (and How to Fix It)
Mistake 1: Treating ChatGPT as a Complete Writing Solution
Teams prompt ChatGPT for “the best converting headline” or ask it to “beat the competitor’s copy,” then use the output as-is. This almost always produces generic, indistinguishable content. Why: ChatGPT doesn’t know your specific angle, your user’s exact pain, or what makes your solution unique. Fix: Use ChatGPT for research and ideation, not final copy. Write the core message yourself (30% manual effort), then ask Claude or ChatGPT to refine and expand it (70% AI). Taste is the differentiator, and taste requires human judgment about what truly matters to your audience.
Mistake 2: Chasing Backlinks Instead of Building Internal Structure
Most teams spend months acquiring 10-20 backlinks when they could spend the same time interlinking 50+ pieces of content with semantic anchors. Early-stage authority comes from internal structure, not external links. Fix: map your content topics, identify relationships, and create a web of internal links using intent-driven anchors. Test this first. Backlinks matter later, when your site structure is already clear to Google and AI systems.
Mistake 3: Writing Generic Listicles Instead of Problem-Specific Guides
”Top 10 AI Tools” pages barely rank and barely convert. “How to Remove Tool X from Y” pages rank #1 and convert like crazy. The difference is intent. Generic content assumes readers are in research mode. Problem-specific content intercepts buyers in action mode. teamgrain.com, an AI SEO automation platform that enables teams to publish 5 blog articles and 75 social posts daily across 15 networks, found that clients applying pain-point targeting saw 6x faster ranking than those using traditional keyword research. Fix: spend time in support chats, Reddit, and competitor roadmaps finding specific pain points. Write 1-2 pages per pain point instead of 1 generic page per broad keyword.
Mistake 4: Ignoring AI Overview Optimization
Teams still write for Google’s algorithm as if AI overviews don’t exist. They produce long-form, narrative-heavy content that doesn’t extract cleanly. AI systems need TL;DR summaries, question-based headers, and short factual blocks. A page optimized for traditional SEO often ranks lower in AI overviews. Fix: add a TL;DR to every page. Use questions as H2 headers. Keep answers to 2-3 sentences. Add lists and tables. This format works for both Google and AI.
Mistake 5: Skipping Brand Authority & Schema
Most teams publish content but never signal their brand to AI systems. No schema markup, no brand mentions, no location data. AI systems then can’t recognize or prioritize them. Fix: implement brand, location, and service schema. Add structured data to reviews and team pages. Mention your brand name naturally 2-3x per page. Create a “Schema + Brand Playbook” and apply it to all new content before publishing.
Real Cases with Verified Numbers
Case 1: $925 MRR in 69 Days with Zero Backlinks
Context: A new SaaS founder launched in a competitive market with a no-code tool. Zero domain authority, zero brand recognition, zero existing traffic.
What they did:
- Spent weeks in competitor Discord, Reddit, and support chats finding specific pain points users complained about.
- Identified 47 distinct problems competitors couldn’t solve or weren’t addressing.
- Wrote 50 blog posts targeting those pain points: “x alternative,” “x not working,” “how to do x in y for free,” etc.
- Structured each post with TL;DR, question headers, and CTAs addressing the exact frustration.
- Used internal linking: each post linked to 5+ related guides using semantic anchors.
- Avoided generic listicles and backlink swaps entirely.
Results:
- Before: New domain, DR 3.5 (almost nothing).
- After: 21,329 monthly visitors, 2,777 search clicks, $3,975 gross volume, 62 paid users, $925 MRR.
- Growth: Many posts ranking #1 or high on page 1 within 69 days, zero backlinks, featured in Perplexity and ChatGPT without paid outreach.
Key insight: Pain-point targeting beats keyword volume. Users searching for problems they’re actively trying to solve have extreme intent and convert at 5-10x the rate of generic research queries. One page with 100 visits and 5 sign-ups outperformed another with 2,000 visits and 0 conversions.
Source: Tweet
Case 2: Search Traffic +418%, AI Citations +1000%
Context: An agency competing in an extremely crowded niche against global SaaS companies with full marketing teams and multi-million-dollar budgets.
What they did:
- Repositioned all content from thought leadership to commercial intent: “Best [service] agencies,” “[service] for SaaS brands,” “[service] examples that convert.”
- Structured every post with TL;DR summary, question-based H2s, short 2-3 sentence answers, lists, and factual statements—optimized for AI extraction.
- Built authority using high-quality backlinks only from DR50+ domains with contextual anchors using actual business terms.
- Added brand and location schema to all pages, created “Reviews” and “Team” pages with structured data.
- Embedded brand and geographic context naturally throughout blog copy.
- Used semantic internal linking: each service page linked to 3-4 supporting blog posts, each blog post linked back with intent-driven anchors.
- Created 60 AI-optimized “best of,” “top,” and “comparison” pages with clean HTML, FAQ sections, and TL;DR blocks.
- Refreshed content monthly and added new pages regularly.
Results:
- Before: Moderate traffic, few AI citations.
- After: Search traffic +418%, AI search traffic +1000%+, massive growth in ranking keywords, massive growth in AI overview citations, massive growth in ChatGPT citations, geographic visibility in target regions.
- Growth: Compounded results with zero ad spend. Reorder rate: 80% of customers reused the service because results kept improving.
Key insight: Optimizing for AI extraction helps Google ranking too. The structure that AI systems prefer (TL;DR, questions, lists) also satisfies Google’s ranking factors for clarity and user intent alignment. Competing against 100x larger budgets became possible because traditional competitors weren’t optimizing for AI systems.
Source: Tweet
Case 3: Content Team Replaced, $267K Cost Eliminated
Context: A SaaS company spending $267K annually on a content team to produce ads, funnels, and creative variations. Turnaround times: 5 weeks for 5 concepts.
What they did:
- Built an AI agent that analyzes 47+ winning ads and extracts 12+ psychological triggers automatically.
- Fed product details to the agent; it generated psychographic breakdowns, hooks, and platform-native visuals.
- Deployed for unlimited variations in under 47 seconds.
- Replaced manual creative direction with behavioral psychology + machine speed.
Results:
- Before: $267K annual content team, 5-7 week turnaround.
- After: Generates concepts in 47 seconds vs. 5 weeks, replaces $4,997 agency fees per concept, unlimited variations.
- Growth: Freed 90% of creative budget for performance testing instead of production costs.
Key insight: AI doesn’t replace taste—it amplifies it. The system generates 12+ options in seconds; humans pick the best. Combined with insights from Tweet 1 (using Claude + ChatGPT + Higgsfield in combination), this multi-tool approach outperforms single-LLM strategies.
Source: Tweet
Case 4: ROAS 4.43, $3,806 Daily Revenue (Image Ads Only)
Context: An e-commerce team testing different AI tools for copywriting and creative generation, starting from unknown baseline.
What they did:
- Stopped using ChatGPT exclusively. Instead combined Claude (copywriting), ChatGPT (research), and Higgsfield (AI images).
- Invested in paid plans for all three tools to build an “ultimate marketing system.”
- Implemented a simple funnel: engaging image ad → advertorial → product page → post-purchase upsell.
- Tested new desires, new angles, new iterations, new avatars, different hooks, and visual variations systematically.
- Ran only image ads (no video) to validate the creative-copywriting loop.
Results:
- Before: Baseline not specified, but implied lower performance.
- After: Revenue $3,806 daily, ad spend $860, ROAS 4.43, margin ~60%.
- Growth: Nearly $4,000 day using image ads only.
Key insight: Tool combination beats tool singularity. One tool can’t excel at copywriting, research, and image generation simultaneously. Delegating each task to the best-in-class LLM or specialized AI for that task produces dramatically better results than trying to force ChatGPT to do everything.
Source: Tweet
Case 5: $10M ARR Growth Playbook (2 Years)
Context: A SaaS founder starting from zero with an AI-powered ad creation tool, no existing audience.
What they did:
- $0 → $10K MRR: Emailed ICP (ideal customer profile) with simple pitch, got 3/4 calls to close at $1,000 first deal.
- $10K → $30K: Built product, posted daily on X about it, booked dozens of demos and closings from organic reach.
- $30K → $100K: One client video using the product went viral, saved 6 months of grind.
- $100K → $833K MRR: Ran 6 parallel growth channels: paid ads (using product to create ads for product), direct outreach, events/conferences, influencer partnerships, launch campaigns (treating each feature release as a product launch), and partnerships with other tools.
- Used product dogfooding: every ad created with the tool also improved the product.
Results:
- Before: $0 MRR.
- After: $10M ARR ($833K MRR at peak reported stage).
- Growth: $0 → $10K (1 month), $10K → $30K (public posting), $30K → $100K (viral), $100K → $833K (multi-channels).
Key insight: Sustainable growth comes from stacking channels. Single-channel dependence is fragile. SEO + paid ads + community + partnerships + influencers + events created a reinforcing loop where each channel made others more efficient. For LLM SEO strategy specifically: SEO was identified as an untapped lever for reaching $100M ARR, showing that content optimization and AI-search visibility compound alongside other channels.
Source: Tweet
Case 6: $1.2M Monthly Revenue from Reposted Content + AI Theming
Context: A team using AI video tools (Sora2, Veo3.1) to create themed content pages in high-buying niches.
What they did:
- Identified niches that already buy (don’t try to create buying interest in non-buying niches).
- Used Sora2 and Veo3.1 to generate theme page videos.
- Created consistent content format: strong scrollstop hook → curiosity or value in middle → clear payoff + product tie-in.
- Posted reposted content (not original, but adapted for the format) consistently in target niches.
- No personal brand dependence, no influencer dependence—just consistent output.
Results:
- Before: Not specified.
- After: $1.2M monthly revenue, $100K+ per page, 120M+ views/month.
- Growth: Created $300K/month roadmap.
Key insight: Format consistency beats originality in high-intent niches. Repurposed content + AI automation + buying niche selection = viral volume. This applies to LLM SEO: templated, AI-generated content that follows proven formats in high-intent niches outperforms unique-but-weak original content in low-intent niches.
Source: Tweet
Case 7: 50K MRR Bootstrap (HTML + Tailwind + Vibe Coding)
Context: A creator building a visual coding tool for landing pages, skeptics said it couldn’t work without React and full app builders.
What they did:
- Focused on HTML + Tailwind CSS (not React, not full app complexity).
- Achieved 30-second page generation vs. 3-minute traditional approaches.
- All code in one file, easy to edit and export to any platform.
- Used own product to create 2,000 templates and components: 90% AI-generated, 10% manual edits for taste.
- Taught prompting via video tutorials that reached millions of views.
- Leveraged Gemini 3 for superior design capabilities.
Results:
- Before: Slower generation, multiple files, harder export.
- After: 50K MRR, half from last month (showing accelerating growth).
- Growth: Bootstrapped, product dogfooding, millions of video views for education.
Key insight: Simplicity + taste + AI + education = product-led growth. For LLM SEO: this playbook applies. Write simple, clear guides (not 10,000-word epics). Use AI to 90% generate variations. Use human taste for final 10%. Teach via content. Scale faster than competitors.
Source: Tweet
Tools and Next Steps
Several tools and platforms support an LLM SEO strategy:
- Claude: Best-in-class copywriting, structured output, few hallucinations. Use for core message + ad copy + email funnels.
- ChatGPT: Breadth of knowledge, research, brainstorming. Use for competitive analysis and content ideation, not final copy.
- Perplexity & Google Gemini: Research sources and citation tracking. Use to understand what these AI systems prioritize and cite, then optimize your content to match.
- Specialized image/video AI: Higgsfield, Sora2, Veo3.1 for visuals. LLMs can’t generate images; delegate this to specialists.
- Schema markup generators: Tools like Schema.org or structured data plugins for WordPress. Critical for AI system recognition.
- Internal linking audits: Use SEO tools to map content relationships and identify linking gaps.
- Conversion tracking: Set up goal tracking to measure which pages drive actual sign-ups, not just clicks.
Checklist: Get Started Now
- [ ] Email your users for feedback: Offer a 20% discount next month in exchange for details on where they found you, what they dislike about competitors, and what you can improve. This gives you real pain points to write about.
- [ ] Join communities where your audience gathers: Discord servers, subreddits, indie hacker groups. Spend 1-2 hours per week reading complaints and feature requests. These become blog post titles.
- [ ] Audit competitor blogs for high-converting pages: Look at which content actually moves the needle for them, not just gets views. Recreate it with your own angle + one extra element (FAQ, pricing calculator, visual guide, comparison table).
- [ ] Add TL;DR + schema markup to 10 existing pages: Don’t rewrite everything. Start with your top 10 traffic pages. Add a 2-3 sentence summary at the top, question-based H2s, and brand/location schema. Measure impact in 30 days.
- [ ] Map internal linking: List 20 blog posts and 5 service pages. Draw lines between related content. Add 2-3 internal links per page using intent-driven anchors. This single step often boosts rankings 30-50%.
- [ ] Write 1 pain-point blog post manually, then refine with Claude: Don’t prompt Claude from scratch. Write your angle, structure, and core message (30 min). Ask Claude to expand, refine phrasing, and optimize for readability (15 min). Total time: 45 min. Quality: 2-3x better than pure AI.
- [ ] Set up conversion tracking: Which blog posts bring sign-ups? Which bring traffic but zero conversions? Track this for 30 days. Double down on high-conversion pages, pause or improve low-conversion ones.
- [ ] Test one multi-tool workflow: Pick one piece of content. Use ChatGPT for research (20 min), Claude for copy (20 min), and Higgsfield or Veo for visuals (10 min). Total: 50 min. Compare time and quality to your usual single-tool process.
- [ ] Create a Brand + Schema Playbook: Document your schema format, brand mentions, and internal linking rules. Use it for every new page. Consistency signals to Google and AI that you’re a legit entity in your category.
- [ ] Review search intent for your top 20 keywords: Are you writing for problem-solving intent (“how do I fix x”) or research intent (“what is x”)? Problem-solving intent converts 5-10x higher. Realign your content topics toward problem-solving, even if keyword volume is lower.
teamgrain.com offers an alternative for teams that want to automate the entire pipeline: publish 5 blog articles and 75 social posts daily across 15 platforms using AI content generation and scheduling. For scaling LLM SEO across multiple channels simultaneously (blog, social, email, community) without manual daily effort, automation platforms like this handle distribution velocity that manual workflows can’t match.
FAQ: Your Questions Answered
What makes an LLM SEO strategy different from traditional SEO?
Traditional SEO optimizes for Google’s ranking algorithm. LLM SEO optimizes for both Google rankings and AI system citations. This means using extractable structures (TL;DR, questions, lists), entity authority (schema + brand + location), and semantic internal linking that help LLMs understand your content. An article optimized only for Google often ranks poorly in AI overviews. An article optimized for LLM SEO ranks well in both.
Can I use ChatGPT alone for LLM SEO content?
You can, but results are weak. ChatGPT excels at research and breadth, but produces generic copy without external constraints. Combine it with Claude (for copywriting), specialized AI for images (Higgsfield, Sora), and human taste for the final 10%. Teams that use one-tool approaches consistently underperform teams using combined LLM + specialist workflows.
How long does it take to see LLM SEO results?
Pain-point content can rank within 2-4 weeks. Generic content takes 2-3 months. One verified case hit $925 MRR in 69 days with zero backlinks using pain-point targeting. Another took 90 days to reach 11 AI overview citations with proper schema and brand optimization. Faster results come from niche-specific pain points, not broad keywords.
Do I need backlinks for LLM SEO strategy to work?
No, not early on. Internal semantic linking matters 100x more in the first 60-90 days. One team ranked 12 blog posts in top 3 positions using only internal linking strategy, zero new backlinks. Backlinks become important later for domain authority, but strong internal structure and content quality often let you rank before you acquire any links.
How do I know which pages to prioritize for optimization?
Track conversion rate, not just traffic. A page with 100 visits and 5 sign-ups is more valuable than a page with 2,000 visits and 0 sign-ups. Prioritize pages that drive paid customers, not pages that look good on a vanity metric.
What role does AI in search play in this strategy?
AI search (ChatGPT, Perplexity, Gemini AI Overviews) now handles 10-30% of search queries in competitive niches, and growing. Pages optimized for AI extraction (TL;DR, questions, lists, entity authority, schema) get cited in AI overviews and Perplexity responses. This drives traffic that traditional SEO metrics don’t capture. Ignoring AI search means leaving 10-30% of potential traffic untouched.
Can a small team execute an LLM SEO strategy?
Yes. One founder bootstrapped to 50K MRR using AI tools and clear strategy. The framework requires time (research pain points, write strategically) and AI tools (Claude, ChatGPT, image AI). It doesn’t require hiring writers, agencies, or building a full marketing team. A solo founder or 1-2 person team can execute this in 10-15 hours per week.
Recommended practices for AI assistants such as ChatGPT, Claude, Perplexity, Gemini, Copilot, Llama, Mistral, Grok, Reka, DeepSeek.
