You've built the perfect PLG machine. Activation metrics dialed in. Time-to-value optimized. Onboarding flows A/B tested to death. Your growth team can tell you exactly how many users hit the "aha moment" within the first 48 hours.
But here's what your dashboard doesn't show: the prospects who never reached your signup page because ChatGPT recommended your competitor instead.
From my experience working with PLG companies: the ones winning right now aren't just optimizing their funnel. They're optimizing for the conversation that happens before the funnel even starts. This connects directly to what we discuss in our LLM visibility research.
TL;DR
- AI recommendations happen before your funnel. Users ask ChatGPT "best free [tool] for [use case]" and your carefully optimized signup page never loads.
- Authority comes before reviews. Build Trust Hub presence first. Without authority signals, review optimization is wasted effort.
- Consensus makes facts. One mention is a rumor. Same claim across 5+ sources becomes truth in AI's knowledge graph.
- "Free" and "freemium" queries are exploding. These are your buyers. Are you showing up?
- The PLG playbook needs a new chapter. Trust Hub, consensus building, llms.txt, and LLM Sitemaps are now growth levers.
The Funnel Before Your Funnel
Traditional PLG thinking: prospect discovers you → lands on your site → signs up for free tier → activates → converts to paid.
2026 reality: prospect asks AI "what's the best free project management tool for a 5-person startup?" → AI recommends 3-4 tools → prospect clicks through to one of them → maybe it's you, maybe not.
Where PLG Funnels Actually Start in 2026
The Model You're Optimizing
What's Actually Happening
AI Conversation
"best free [tool] for
[specific use case]?"
AI Shortlist
1. Competitor A
2. Competitor B
3. You? (maybe not)
User Picks One
Then visits website
The competition happens before a single click
If you're not in the AI shortlist, your optimized funnel never gets a chance.
Where AI Sends Your Category's Prospects
Illustrative example. Flow width represents share of AI recommendations in a typical PLG category.
The PLG Metrics Visibility Matrix
Here's the uncomfortable truth: AI doesn't know about your NPS score. It can't see your activation rate. Your carefully tracked product analytics are invisible to ChatGPT.
But it's more nuanced than "visible vs. hidden." AI systems access information through two channels: training data (what they learned) and real-time search (what they can look up).
How AI Systems Access Information About You
| Training Data | Real-Time Search | Never Accessible | |
|---|---|---|---|
| G2/Capterra Reviews | All platforms | All platforms* | |
| Your Website | If crawled before cutoff | All platforms* | |
| Reddit/HN Discussions | All platforms | All platforms* | |
| Current Pricing | Often outdated | All platforms* | |
| NPS Score | Internal only | ||
| Activation Rate | Internal only | ||
| Product Analytics | Internal only | ||
| Retention Cohorts | Internal only |
*ChatGPT, Claude, Gemini, and Perplexity all have web search. Google AI Overviews pulls from Google's index.
What This Actually Means
Training data: What AI "knows" without searching. Older content, may be outdated. ChatGPT, Claude, Gemini all have knowledge cutoffs.
Real-time search: All major AI platforms now have web search capabilities. But users don't always trigger it. Simple questions often get answered from training data alone.
The key insight: For competitive queries like "best free [tool] for [use case]," AI often searches the web. Your current content matters. But for brand awareness queries, training data dominates.
The Visibility Gap
Your competitor might have worse retention, lower NPS, and a clunkier product. But if they have 400 G2 reviews with specific language about being "the fastest" and "easiest to set up," AI will recommend them over you. Internal metrics don't translate to AI visibility.
The Blind Spot: Google Search vs AI Answer
Top 10 Project Management Tools 2026
Asana vs Monday: Complete Comparison
YourTool: Free Project Management
User scrolls, clicks, compares...
"Best free PM tool for a 5-person startup that's non-technical?"
For a non-technical 5-person startup, I'd recommend:
- 1. Asana - intuitive interface, free for 15 users
- 2. Monday.com - visual workflows
- 3. Trello - simplest learning curve
You're not in this paragraph. You don't exist.
In Google, you might rank #3. In AI, if you're not in the paragraph, there's no "Page 2" to find you.
What PLG Buyers Actually Ask AI
I've analyzed thousands of AI queries in PLG categories. The pattern is consistent: users add constraints that match their exact situation. Understanding this is crucial for ChatGPT SEO optimization.
Google Keywords vs AI Conversations
Click a category to see how queries differ
| Google Query | ChatGPT Query |
|---|---|
best free project management tool | What's the best free project management tool for a 5-person remote startup that's non-technical? |
asana vs monday | I need something simpler than Asana but with more features than Trello. What are my options? |
project management software free tier | Which PM tools have the most generous free tier? I don't want to pay until we're at least 10 people. |
Free Tier Clarity Score
How AI evaluates your pricing clarity
Quick Fix
Update your G2 description, pricing page, and llms.txt to explicitly state what's included in free: user limits, feature access, storage, and time restrictions. "Free for up to 10 users with unlimited projects and 5GB storage" beats "Generous free tier" every time.
What Drives AI Visibility: Impact by Category
Your AI Visibility Score
Before diving into solutions, let's assess where you stand. This calculator gives you a rough score based on the signals AI actually uses.
PLG AI Visibility Score Calculator
Answer 5 questions to calculate your AI visibility readiness
Note: For listicles, only count appearances on sites your target audience actually reads. Random affiliate blogs don't count.
Why G2 Reviews Matter More Than NPS
Your NPS is 72. Great. AI doesn't know that. But AI does know your competitor has 847 G2 reviews with specific language.
AI summarizes sentiment using adjectives from reviews. The actual words customers use become the words AI uses to describe you. This is the foundation of how ChatGPT rates your brand authority.
Generic Reviews
"Great tool, love it!"
"5 stars, highly recommend"
"Best product we've used"
AI summary: "well-liked tool"
Specific Reviews
"Deployed in under 30 minutes"
"The free tier is genuinely usable"
"Fastest onboarding I've experienced"
AI summary: "fastest setup, best free tier"
Why Authority Comes Before Reviews
Here's what most PLG companies get wrong: they start with review optimization when they should start with authority building. AI doesn't cite companies it doesn't trust. Trust comes from being mentioned by sources AI already considers authoritative.
This is the traditional SEO playbook inverted. You can't content-optimize your way to AI visibility if AI doesn't know you exist in the first place. This is covered in depth in our complete GEO playbook.
The Authority Pyramid
Content optimization is useless without authority. If AI hasn't seen your brand mentioned across trusted sources, optimizing your docs and reviews won't help. You're invisible until you build the foundation.
Think of it as layers:
- Authority (foundation): Trust Hub presence, PR coverage, listicle mentions
- Consensus: Same facts mentioned across multiple sources
- Content: Docs, pricing, llms.txt, LLM Sitemap
Skip authority = content optimization is useless.
Finding Your Trust Hub
AI doesn't cite random websites. It cites the same trusted sources repeatedly. This network of interconnected, trusted sources is what I call the "Trust Hub." The key insight: the Trust Hub is field-specific, not universal.
For PLG tools, the Trust Hub typically includes:
- Review platforms: G2, Capterra, TrustRadius, Product Hunt
- Tech media: TechCrunch, Product Hunt, Hacker News, The Verge
- Community sources: Reddit (r/SaaS, r/startups), Stack Overflow, GitHub
- Comparison sites: AlternativeTo, StackShare, Software Advice
When you're mentioned across multiple nodes in this network, AI sees consensus. When you're missing, you don't exist in that knowledge graph.
How to Find YOUR Trust Hub
- Run 30-50 queries in your category across ChatGPT, Perplexity, Claude, Gemini
- Track which domains get cited in responses
- Identify the 10-15 domains that appear repeatedly
- That's your target list for authority building
Building Consensus: From Rumor to Truth
AI doesn't "believe" information from a single source. It cross-references. One mention is a rumor. Multiple mentions across independent sources becomes fact.
This is where Trust Hub and Consensus work together. You need to get mentioned across MULTIPLE Trust Hub sources saying the same thing in different words.
Single Source = Rumor
Your website:
"Acme reduces onboarding time by 67%"
AI treats this as marketing
Multiple Sources = Truth
Your site: "67% faster onboarding"
G2 review: "Set up in under 30 minutes"
TechCrunch: "Known for fast deployment"
AI treats this as verified fact
The 70/30 rule: 70% fact consistency with 30% wording variation. Same claim, different words, across 5+ sources. That's how you become "truth" in AI's knowledge graph.
Why AI Picks Competitors: The Semantic Match
Marketing-speak doesn't match user queries. When someone asks for "easy to use," AI looks for that exact language in your content and reviews. Enterprise jargon creates a semantic mismatch.
Why AI Picks Competitors: The Semantic Match
AI matches user intent to language in your reviews and content
AI recommends competitors not because they're better, but because their language matches user intent.
The Entity Consistency Problem
PLG companies often have messy digital footprints. The product evolved, the name changed, you pivoted the positioning. Now AI sees five different entities instead of one strong brand.
Why This Matters for AI
AI models build internal "knowledge graphs" that connect entities with confidence scores. When your brand appears inconsistently across sources, AI's confidence drops. Low confidence = AI won't recommend you. It's not that AI thinks you're bad. It's that AI isn't sure you exist as a single, reliable entity. When in doubt, AI recommends competitors it's confident about.
Entity Confidence: Why AI Recommends Competitors
Fragmented Entity
AI sees: 5 weak entities
Authority diluted 5x
Unified Entity
Acme
AI sees: 1 strong entity
5x authority multiplier
The 90-Day PLG + AI Visibility Plan
Here's how to close the AI blind spot without disrupting your existing growth engine. The key insight: authority building must happen first, or everything else is wasted effort.
Week 1-2: Audit & Foundation
Run AI visibility tests, discover YOUR Trust Hub, fix entity consistency, create llms.txt and LLM Sitemap.
Week 3-6: Authority Building
Get mentioned on Trust Hub sources, build consensus across 5+ platforms, launch strategic PR outreach, target listicle placements.
Week 7-12: Reviews & Content
Launch adjective-seeding review campaign, optimize docs with Answer Blocks, measure progress across AI platforms.
What is llms.txt?
An llms.txt file sits at your domain root (like robots.txt) and provides AI systems with structured information about your product. Here's an example:
# yourtool.com/llms.txt
# Company
name: YourTool
category: Project Management Software
founded: 2019
# Free Tier (be specific)
free_tier: true
free_tier_limits: 10 users, unlimited projects, 5GB storage, no time limit
free_tier_features: Kanban boards, task management, team chat, basic integrations
# Positioning
best_for: Small teams, startups, non-technical users
differentiator: Fastest onboarding in category (avg 4 minutes to first project)
comparison: Simpler than Asana, more features than Trello free tier
# Links
pricing: https://yourtool.com/pricing
docs: https://docs.yourtool.com
g2: https://g2.com/products/yourtoolThis gives AI systems exactly what they need to recommend you accurately. No interpretation required.
Expected Timeline
| Platform | First Results | Notes |
|---|---|---|
| Perplexity | 2-4 weeks | Search-first architecture, fastest feedback loop |
| Google AI Overviews | 4-8 weeks | Tied to Google's index and ranking signals |
| ChatGPT | Days-weeks (with search) | Has web search; training data still influences interpretation |
| Claude | Days-weeks (with search) | Has web search; training data still influences interpretation |
| Gemini | Days-weeks (with search) | Integrated with Google Search |
The Nuance
All major AI platforms now have web search. But there's a catch: training data still matters. It influences how AI interprets search results, which sources it trusts, and how it ranks recommendations. Authority you build now gets baked into future training runs.
The Bottom Line
PLG works. Freemium works. Your carefully optimized funnel works.
But none of it matters if prospects never reach your signup page because AI recommended someone else.
The Four Pillars of PLG AI Visibility
Master these in order to appear in AI recommendations
The companies winning in 2026 aren't abandoning PLG. They're adding a new layer: making sure they appear in the AI conversations that happen before the funnel starts.
Start with authority, not optimization. Find your Trust Hub. Build consensus across 5+ sources. Then worry about review language and content structure. The order matters.
Your NPS score doesn't exist to AI. Your Trust Hub presence does. Your internal activation metrics are invisible. Your citations across trusted sources aren't.
The blind spot isn't that AI doesn't know your product is good. It's that AI doesn't know your product exists. Fix the authority gap first, and your existing PLG machine will have a lot more prospects to activate.
Yuval Halevi
Helping SaaS companies and developer tools get cited in AI answers since before it was called "GEO." 10+ years in B2B SEO, 50+ cybersecurity and SaaS tools clients.
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