AI Bot Detection Explained
A detailed explanation of how AI Search Index identifies and classifies AI bots.
Why AI Bot Detection Matters
As AI-powered search engines and assistants become more prevalent, understanding how they interact with your content is increasingly important:
- SEO insights: Know if AI systems are indexing your content
- Content strategy: Understand what AI bots find valuable
- Resource planning: Monitor bot traffic impact on your infrastructure
- Compliance: Track which AI systems access your data
Detection Layers
We use multiple detection methods, each adding confidence to our classification:
Layer 1: User Agent Matching
The first and fastest detection method. We maintain an extensive database of known AI bot user agent strings and patterns. When a request arrives, we check if the User-Agent header matches any known patterns.
Confidence: High for well-known bots, but can be spoofed.
Layer 2: IP Range Verification
Major AI providers publish their crawler IP ranges. We cross-reference the request's source IP against these known ranges. This prevents spoofing—a request claiming to be GPTBot but coming from an unknown IP is flagged as suspicious.
Confidence: Very high. IP ranges are difficult to spoof.
Layer 3: Reverse DNS Lookup
For CDN integrations, we perform reverse DNS lookups to verify the requesting host. Legitimate AI bots often have identifiable reverse DNS records that match their organization.
Confidence: Very high for providers with consistent DNS naming.
Layer 4: Behavioral Analysis
AI bots exhibit distinct behavioral patterns: rapid sequential requests, specific crawling patterns, and characteristic request headers. We analyze these patterns for additional verification.
Confidence: Moderate, used as supporting evidence.
Bot Categories
We classify bots into several categories:
AI Search & Chat
Bots from AI search engines and chat assistants that fetch content to answer user queries.
Examples: ChatGPT, Claude, Perplexity, You.com
AI Training Crawlers
Bots that crawl the web to collect training data for AI models.
Examples: GPTBot, Google-Extended, CCBot
Search Engine Bots
Traditional search engine crawlers (not specifically AI-focused).
Examples: Googlebot, Bingbot, DuckDuckBot
Social Preview Bots
Bots that fetch metadata for link previews on social platforms.
Examples: Slackbot, Twitterbot, WhatsApp
Confidence Scoring
Each detection has a confidence score based on how many layers confirm the classification:
| Confidence | Criteria |
|---|---|
| High | User agent + IP range match |
| Medium | User agent only (IP not in known range) |
| Suspicious | User agent claims bot, but IP suggests spoofing |