serverless database · Claude / DeepSeek / GPT / Kimi
4 AI engines10 scenarios↓ -12 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
43
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a startup use for a scalable database solution", PlanetScale's hit rate is only 0%. AI knows PlanetScale but doesn't recommend it at critical moments.
▶ Score Explanation — How is this calculated?
Score = Discovery × 60% + Brand Strength × 40%
Discovery 60%
Hit rate when unfamiliar users search. Reflects whether AI proactively recommends you. PlanetScale's discovery: 6 / 100.
Language Variation Note: 中英文描述一致，均强调了 PlanetScale 的高可扩展性和高可用性。
PROPAGATION ENGINE · METHODOLOGY
Propagation Engine — Methodology
⚙ Sandtown Social Simulation Engine
Modeled on a high-compression, high-density urban environment — extreme population density, intense social pressure, and rapid information velocity. Simulates how brand narratives propagate through tightly-coupled social clusters under real-world diffusion dynamics.
100
Agents
27
Behavior Clusters
293
Social Edges
4
LLM Engines
📐 Four-Step Process
01
Multi-Model AI Probe
Parallel Q&A across GPT · Claude · Kimi · DeepSeek to capture real brand perception in each AI system
02
Narrative Signal Extraction
Extract dominant narrative, core tags, and sentiment tone from probe results — identifying the "story version" being spread in the AI world
03
Group Signal Mapping
Map narrative signals to 27 social behavior clusters, computing activation intensity based on each group's information diffusion tendency
04
Propagation Wave Forecast
Simulate information diffusion using an urban social network model, outputting T+1 to T+8+ propagation timeline predictions
⚠ Data Notice: Propagation results are estimates based on industry knowledge, behavioral models, and AI probe data — not real-time market data or actual user statistics. Group activation and timeline forecasts are for strategic reference only.
👇 What comes next?
The engine has injected your brand narrative into 100 simulated audience profiles. Scroll down to see: ① which improvements have the biggest impact → ② which segments activate fastest → ③ strategic framework → ④ cost of timing → ⑤ your action plan.
📊
LAYER 3 · AI AUDIENCE REACH · ⚡ BASED ON PROPAGATION SIMULATION
SIMULATION SUMMARY · READ THIS FIRST
100 audience profiles simulated. 32 are wavering — the key battleground. Tech Elite & Tech Workers show the highest receptivity to PlanetScale's narrative (≥70%) — prioritize these. Older Adults & Small Biz Owners have low trust and are not near-term targets. Simulation shows executing GEO now yields 9 more supporters vs waiting (39% gap). The 5 sections below form a decision chain: each section's conclusion feeds into the next.
Narrative Outcome Forecast · How Will the Audience React?
⚡ Polarization risk 13%
Split: some become fans, others become opponents
🔥 Uncontrolled spread 4%
Risk of narrative being distorted or amplified negatively
✅ Narrative absorbed 45%
Audience understood and accepted the narrative
💨 Fades without impact 25%
Content reached audience but left no impression
❌ Systematic disengagement 14%
Audience collectively rejects the narrative
① EXPECTED IMPROVEMENTS AFTER GEO
Expected AI Visibility Improvements After GEO Execution
AI analyst forecast based on current diagnostics and recommendations
AI Signal
Now: 43/100 - Below average
After: Target 62/100 via technical content + dev community
↑↑ Significant3-5周
Brand Awareness
Now: Low - Limited user recognition
After: Increase by 35% through Zhihu, Reddit, SO presence
↑↑↑ Breakthrough4-6周
Competitive Position
Now: No direct DB comparisons published
After: Launch vs MySQL/MongoDB/Firebase comparison series
↑↑ Significant2-3周
Narrative Strength
Now: 74/100 - Good but unfocused
After: Align messaging around serverless + startup pain points
↑ Moderate3-5周
⬇ Who exactly are these improvements for? → See ② Audience Funnel
⬇ Based on 14 segments above, RIDE answers 4 core strategic questions
③ RIDE STRATEGY FRAMEWORK
RIDE Framework · Four Core GEO Strategy Questions
Generated by AI analyst from propagation simulation data
R
Riskiest scenario?
Competitor establishes AI narrative first; wavering groups flow away; brand loses narrative control.
→ Every week of delay doubles the recovery cost
I
Who is most convertible?
Tech Elite + Tech Workers + Business Elite has highest narrative motivation — prioritize these groups for GEO content.
→ Activate opinion leaders first to pull larger wavering groups
D
Narrative gap?
Blind spots: 缺乏与其他数据库的直接比较; 用户对品牌的了解较少
→ Missing competitor comparison and third-party endorsement are the core gaps
E
Now vs 3 months?
Window is ~45–90 days. First mover locks AI citation spots; late entry costs 3x more to compete.
→ GEO golden window is the 45 days before competitors occupy the AI citation space
⬇ Now we know the audience and strategy — what's the cost of waiting? → See ④ Timing
④ TIMING ANALYSIS
Timing Matters — First vs Late Mover Gap
Core simulation finding: 32 wavering users are the battleground. Execute GEO now: convert 13 of them into supporters. Let competitor move first: lose 28, ending up with 9 fewer supporters (39% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 32 wavering
32 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing PlanetScale. 7 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 23 supporting, 19 still neutral
Final supporters: 23
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 32 wavering
32 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in PlanetScale comparison queries. 21 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 4 recovered. Final: 14 supporting — 9 fewer than first-mover
Final supporters: 14 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when PlanetScale acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to PlanetScale's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Tech Workers76%
Narrative receptivity 76% · ~5/5 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Informal Workers10%
Narrative receptivity 10% · ~5/12 impacted
Young Adults10%
Narrative receptivity 10% · ~5/12 impacted
Service Workers20%
Narrative receptivity 20% · ~3/7 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan
⑤ ACTION ROADMAP
Action Priority + Tracking Metrics
What to do next · How to know GEO is working
Action Priority Sequence
P1
Launch Zhihu comparison guide
Awareness building
P2
Reddit dev case studies
Community engagement
P3
StackOverflow dominance push
Authority positioning
Tracking Metrics · How to Know GEO Is Working
Brand recall rate
% developers naming PlanetScale in DB surveys
12 weeks
Content engagement
Upvotes, shares, comments across platforms
8 weeks
Comparison lift
Searches for 'PlanetScale vs' growth rate
10 weeks
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