PLG AI Funnel: Product-Led Growth in the Agent Era
The Paradigm Shift
Old PLG Funnel:
Landing Page → Free Trial → Activation → Conversion
New PLG Funnel:
Agent Query → Documentation Scan → Feature Match → Recommendation
The buyer's first interaction is no longer your landing page—it's an AI agent scanning your documentation to answer their question.
The Four Stages
Stage 1: Agent Query
What happens: User asks AI "What tool can help me [problem]?"
Optimization goals:
- Brand appears in AI's consideration set
- Correct category association
- Problem-solution mapping exists in AI's knowledge
Tactics: | Action | Why It Works | |--------|--------------| | Entity building | AI must know your brand exists and what category it's in | | Third-party mentions | Reviews, comparisons, listicles feed AI training data | | Clear positioning | "X is a [category] that [primary benefit]" statements |
Audit questions:
- Does AI know your brand when asked directly?
- Does AI associate your brand with your category?
- Do competitors appear but you don't?
Tool: entity-builder agent for authority building
Stage 2: Documentation Scan
What happens: AI scans your docs, help center, marketing pages to understand capabilities.
Optimization goals:
- Content is AI-extractable (chunked, structured)
- Answers are front-loaded (not buried)
- Each page passes the "Taco Bell Test" (stands alone)
Tactics: | Action | Why It Works | |--------|--------------| | Answer-first structure | AI extracts the first sentence as the answer | | FAQ sections | Pre-formatted Q&A is ideal for extraction | | Structured data | Tables, bullets, headers signal discrete facts | | Standalone sections | AI may only see one chunk, not the full page |
The Extractability Checklist:
☐ First sentence directly answers the page's implied question
☐ H2/H3 headers are questions or clear topic labels
☐ Tables used for comparisons and feature lists
☐ Each section makes sense without surrounding context
☐ No "as mentioned above" or "see below" dependencies
Tool: llm-optimizer agent for content optimization
Stage 3: Feature Match
What happens: AI matches user's specific needs to your product's capabilities.
Optimization goals:
- Features described in user-problem terms
- Use cases explicitly mapped to capabilities
- Limitations clearly stated (builds trust)
Tactics: | Action | Why It Works | |--------|--------------| | Problem → Feature mapping | "If you need X, [Product] does Y" | | Use-case pages | Dedicated pages per job-to-be-done | | Integration lists | AI checks compatibility requirements | | Pricing clarity | AI needs to match budget constraints |
Feature Documentation Template:
## [Feature Name]
**Problem it solves:** [User problem in their words]
**How it works:** [1-2 sentence explanation]
**Best for:** [Specific use cases]
**Limitations:** [What it doesn't do]
**Example:** [Concrete scenario]
Anti-pattern: Feature pages that describe functionality without connecting to user problems.
Stage 4: Recommendation
What happens: AI decides whether to recommend your product and how to position it.
Optimization goals:
- Clear differentiation from alternatives
- Social proof AI can cite
- Product tie-backs throughout content
Tactics: | Action | Why It Works | |--------|--------------| | Comparison content | "X vs Y" pages AI directly references | | Quantified outcomes | "Reduces time by 40%" > "saves time" | | Review presence | G2, Capterra reviews influence AI recommendations | | Product mentions in answers | Every content piece connects back to product |
The Product Tie-Back Rule: Every 1-2 paragraphs of educational content should include how your product relates.
- ❌ "Lead scoring helps prioritize prospects"
- ✅ "Lead scoring helps prioritize prospects—[Product] automates this with AI-powered scoring"
Tool: aeo-scorecard skill for measuring recommendation success
PLG × AEO Integration
| PLG Stage | AEO Concept | Metric | |-----------|-------------|--------| | Agent Query | Entity/Authority | AI Visibility % | | Documentation Scan | Extractability | Citation Rate | | Feature Match | Fact-Density | Feature mention accuracy | | Recommendation | Product Tie-Back | AI Share of Voice |
Quick Audit Workflow
1. Test 10 queries your buyers ask
→ Does your brand appear? (Stage 1)
2. Check if AI cites YOUR content
→ Or competitor/third-party? (Stage 2)
3. Ask AI about specific features
→ Does it know your capabilities? (Stage 3)
4. Ask "Should I use [Product] for [use case]?"
→ What's the recommendation? (Stage 4)
Common PLG AI Gaps
| Symptom | Stage Broken | Fix | |---------|--------------|-----| | Brand unknown to AI | Query | Entity building, third-party mentions | | AI cites competitors' content | Documentation | Improve extractability, answer-first | | AI misunderstands features | Feature Match | Rewrite feature docs with problem framing | | AI recommends competitor | Recommendation | Strengthen differentiation, add social proof |
Related Tools
llm-optimizer- Deep content optimization for Stage 2entity-builder- Authority building for Stage 1aeo-scorecard- Metrics framework for all stages/aeo-workflow- Full implementation workflowquery-expansion-strategy- Understanding query fan-out