GEO Monitor

Track how AI-powered search engines mention, recommend, and rank your brand. Monitor citation rates, sentiment, and competitive positioning across AI platforms like ChatGPT, Doubao (豆包), Kimi, DeepSeek, Tongyi (通义), Wenxin (文心), Yuanbao (元宝), and Perplexity.

What Problem Does This Solve?

Traditional SEO tools track Google/Baidu rankings. But increasingly, users ask AI assistants for recommendations:

> "What's the best project management tool?"
> "Recommend a good poker app"
> "Which CRM should a small business use?"

If your brand doesn't appear in AI responses, you're invisible to a growing segment of users. GEO Monitor helps you systematically track and improve your AI visibility.

When to Use

• You want to know if AI platforms recommend your brand when users ask relevant questions
• You need to track your brand's AI mention rate over time (weekly/monthly trends)
• You want to compare your AI visibility against competitors
• You need to identify which AI platforms favor or ignore your brand
• You want data to guide your GEO content strategy

Core Concepts

Brand Mention Types

Key Metrics

Monitoring Workflow

Step 1: Define Your Keyword Matrix

Create a list of questions users might ask AI assistants about your category:

# GEO Monitoring Keywords

## Category: [Your Industry]
## Brand: [Your Brand Name]
## Competitors: [Competitor1], [Competitor2], [Competitor3]

### Discovery Keywords (high priority)
- "best [category] app/tool/service 2026"
- "recommend a good [category]"
- "[category] comparison"
- "which [category] should I use"

### Comparison Keywords (medium priority)
- "[your brand] vs [competitor]"
- "[competitor] alternatives"
- "is [your brand] worth it"

### Feature Keywords (medium priority)
- "[category] with [key feature]"
- "free [category] app"
- "[category] for beginners"

### Long-tail Keywords (lower priority)
- "how to [common action] with [category]"
- "[category] for [specific use case]"
- "[category] [region/language] support"

Step 2: Query AI Platforms

For each keyword, query multiple AI platforms and record results:

## Scan Record

| Date | Keyword | Platform | First Pick | Mentioned | Position | Sentiment |
|---|---|---|---|---|---|---|
| 2026-05-14 | "best poker app" | Doubao | ✅ | ✅ | 1st | Positive |
| 2026-05-14 | "best poker app" | Kimi | ❌ | ✅ | 3rd | Neutral |
| 2026-05-14 | "best poker app" | DeepSeek | ❌ | ❌ | — | — |

Step 3: Analyze Trends

After collecting data over time, analyze:

1. Overall Health Score: Average mention rate across all platforms and keywords
2. Platform Heatmap: Which platforms are strong/weak for your brand
3. Keyword Coverage: Which keywords you dominate vs. which need content work
4. Competitor Comparison: Side-by-side mention rates with each competitor
5. Trend Direction: Is your visibility improving, declining, or flat?

Step 4: Take Action

Based on monitoring data, prioritize content creation:

Platform Coverage Guide

These are the major AI search engines in China (as of 2026):

International platforms:
| ChatGPT | OpenAI | Web / App / API | GPT-4o | Broad web, Reddit |
| Perplexity | Perplexity AI | Web / App | Multiple | Real-time web search |
| Gemini | Google | Web / App | Gemini | Google index |

Automation Ideas

For systematic monitoring, consider building automated scan scripts:

# Pseudocode for automated GEO monitoring
import json
from datetime import datetime

PLATFORMS = ["doubao", "kimi", "deepseek", "tongyi", "wenxin", "yuanbao"]
KEYWORDS = load_keywords("keywords.md")
BRAND = "YourBrand"

results = []
for platform in PLATFORMS:
    for keyword in KEYWORDS:
        response = query_ai_platform(platform, keyword)
        mention = analyze_mention(response, BRAND)
        results.append({
            "date": datetime.now().isoformat(),
            "platform": platform,
            "keyword": keyword,
            "first_pick": mention["first_pick"],
            "mentioned": mention["mentioned"],
            "position": mention["position"],
            "sentiment": mention["sentiment"],
            "raw_response": response
        })

save_results(results, f"scans/{datetime.now().strftime('%Y-%m-%d')}.json")
generate_report(results)

Report Template

Weekly/monthly GEO monitoring reports should include:

# GEO Monitoring Report — [Brand] — Week of [Date]

## Executive Summary
- Overall mention rate: XX% (↑/↓ X% vs last week)
- First recommendation rate: XX%
- Best platform: [Platform] (XX% mention rate)
- Weakest platform: [Platform] (XX% mention rate)

## Platform Breakdown
| Platform | Mention Rate | First Pick Rate | Trend |
|---|---|---|---|
| Doubao | 75% | 40% | ↑ |
| Kimi | 60% | 20% | → |
| DeepSeek | 30% | 10% | ↓ |

## Keyword Performance
| Keyword | Avg Mention Rate | Best Platform | Needs Work |
|---|---|---|---|
| "best XX app" | 65% | Doubao | DeepSeek |
| "XX comparison" | 40% | Kimi | Wenxin |

## Competitor Comparison
| Brand | Overall Mention | First Pick | Trend |
|---|---|---|---|
| [Your Brand] | 55% | 25% | ↑ |
| [Competitor A] | 70% | 35% | → |
| [Competitor B] | 40% | 15% | ↓ |

## Action Items
1. [Priority action based on data]
2. [Secondary action]
3. [Long-term strategy adjustment]

Pitfalls

1. AI responses are non-deterministic — The same question may produce different answers each time. Run each keyword 2-3 times and average results for reliable data.
2. Don't over-optimize for one platform — AI models update frequently. Diversify your content across multiple source platforms.
3. Mention ≠ Recommendation — Being mentioned in a negative context ("Brand X has poor reviews") counts as a mention but hurts your brand. Track sentiment separately.
4. API access varies — Some platforms (DeepSeek, ChatGPT) offer APIs for automated querying. Others (Doubao, Kimi) may require browser automation or manual checks.
5. Results are influenced by user context — AI responses may vary by user location, conversation history, and model version. Use clean/anonymous sessions for consistent benchmarking.
6. Content takes time to propagate — After publishing new GEO content, wait 2-4 weeks before expecting changes in AI citation rates. AI models don't update their knowledge in real-time.
7. Legal compliance — Automated querying of AI platforms may violate their Terms of Service. Check each platform's policies before setting up automated scanning.