Yes, You Can Track AI Search Engines in Google Search Console! Here's the Workaround

As AI-powered search engines like ChatGPT with Search, Perplexity AI, and Google’s AI Mode become increasingly prevalent, understanding their impact on your website traffic has become crucial for SEO professionals. While these AI search engines don’t identify themselves with specific user agents in Google Search Console, there’s a potential workaround to track their queries based on distinctive search patterns.

Important disclaimer: This is currently a workaround method, not an official tracking solution. The patterns identified may evolve as AI search engines update their query methods. It requires “data mining”. Start by sorting queries in ascending order by Impressions. Focus on those with 1–10 impressions, though some AI-driven queries may go up to 50. While you won’t see the exact AI engine behind each query (like ChatGPT, Perplexity, Gemini, or Claude), the patterns can still be identified.

Understanding AI Search Engine Query Patterns

Based on how AI systems conduct research and web searches, they exhibit specific patterns:

1. Comprehensive Information Gathering

AI search engines are programmed to gather complete information in a single query, leading to multi-faceted questions that humans rarely type.

2. Systematic Research Approach

When AI performs deep searches, it follows structured patterns:

• Temporal boundaries (“last 24 months”, “since 2023”)
• Current status checks (“currently developing”, “latest updates”)
• Future projections (“roadmap”, “upcoming features”)
• Competitive analysis (“market position”, “versus competitors”)
• Research, task assignment (“reply, task, research, search”)

3. Professional Terminology

AI queries often include sophisticated business language:

• Strategic planning terms
• Innovation indicators
• Market analysis vocabulary
• Technical assessment language

Live Examples from Search Console

Huge thanks to Popupsmart for this example. They are building the best no-code popup builder right now. cc: Emre Elbeyoğlu

This is from Vijay Chauhan. See here.

Enhanced Regex Patterns for AI Query Detection

Simple Solution: “Query Contains” filter on GSC

Use these one by one: reply, task, latest, research.

Temporal Research Patterns

(last|past|recent|previous)\s+\d+\s+(months?|years?|quarters?)

(since|from|between|during)\s+(January|February|March|April|May|June|July|August|September|October|November|December)?\s*202[3-5]

(2024|2025)\s+(updates?|trends?|developments?|changes?)

Strategic Business Language

strategic\s+(initiatives?|plans?|roadmap|direction|goals?)

cutting-edge\s+(technology|features?|developments?|innovations?)

innovative\s+(solutions?|approaches?|products?|services?)

Research Intent Patterns

comprehensive\s+(analysis|review|overview|assessment|evaluation)

(analyze|evaluate|assess|examine)\s+.{0,20}\s+(performance|trends?|metrics?|data)

deep\s+dive\s+(into|on|analysis|research)

Multi-Question Patterns

\?\s*(is|are|has|have|does|do)\s+.{0,50}\s*\?

\?\s*.{10,100}\s+(additionally|furthermore|also|plus)\s*

Evidence Gathering Patterns

(gather|collect|find|search)\s+.{0,20}\s+(evidence|proof|data|information|insights?)

(public|available|recent)\s+(materials?|documents?|information|data|sources?)

Current Status Queries

currently\s+(developing|working|planning|building|implementing)

(latest|current|ongoing|active)\s+.{0,20}\s+(projects?|initiatives?|developments?)

Competitive Intelligence Patterns

(competitor|competitive|market)\s+(analysis|landscape|position|intelligence)

versus\s+(competitors?|industry|market\s+leaders?)

Future-Looking Patterns

(roadmap|pipeline|upcoming|future|planned)\s+.{0,20}\s+(features?|releases?|products?)

(forecast|prediction|outlook|projection)\s+.{0,20}\s+202[4-6]

Combining Patterns for Maximum Effectiveness

The most effective approach is to look for queries that match multiple patterns:

High-Confidence AI Query Indicators (2+ matches):

1. Contains temporal marker (e.g., “last 24 months”)
2. Includes research language (e.g., “comprehensive analysis”)
3. Has multiple questions or sub-queries
4. Uses strategic business terminology
5. References current year or future dates
6. Exceeds 75 characters in length

Example AI Query Patterns:

• “strategic initiatives [company] launching [year] comprehensive market analysis”
• “innovative solutions currently developing last 12 months competitive landscape”
• “cutting-edge technology implementations since January [year] future roadmap”

Implementation Strategy in Search Console

Step 1: Basic Time-Based Filter

Start with queries containing temporal markers:

(last|past)\s+\d+\s+months?

Step 2: Add Strategic Language

Layer in business terminology:

(strategic|cutting-edge|innovative|comprehensive)

Step 3: Research Intent

Include research-specific terms:

(analyze|evaluate|assess|deep\s+dive|research)

Step 4: Current Status

Add present-tense indicators:

(currently|presently|ongoing|active)

Real-World Pattern Examples

Based on AI search behavior, look for queries like:

Market Research Patterns:

• “comprehensive market analysis [industry] 2024 strategic positioning”
• “innovative companies [sector] last 12 months cutting-edge developments”

Company Intelligence:

• “strategic initiatives currently implementing competitive advantage analysis”
• “deep dive [company] financial performance [year] versus industry”

Technology Assessment:

• “cutting-edge features launching [year] comprehensive technical review”
• “innovative solutions market leaders developing current roadmap”

Trend Analysis:

• “analyze trends [topic] past 6 months strategic implications”
• “comprehensive overview emerging technologies [year] market impact”

Key Indicators Summary

Primary Indicators:

• Query length > 50 characters
• Multiple distinct concepts in one query
• Temporal boundaries (months/years)
• Business/analytical language
• Multiple question marks
• Current status inquiries

Secondary Indicators:

• Strategic terminology (strategic, innovative, cutting-edge)
• Research verbs (analyze, evaluate, assess)
• Comprehensive scope words
• Future-looking language
• Competitive references

Monitoring and Optimization

1. Weekly Review: Check new query patterns matching these regexes
2. Pattern Evolution: AI search patterns will evolve - update regexes quarterly
3. Length Analysis: Monitor average query length trends
4. Vocabulary Tracking: Note new analytical terms appearing in long queries

Conclusion

This workaround provides valuable insights into AI search engine traffic by identifying their characteristic query patterns. Focus on:

• Combining multiple simple patterns rather than complex single regexes
• Monitoring queries with strategic business language
• Tracking temporal markers and research intent
• Watching for multi-part questions

As AI search engines become more sophisticated, their query patterns will evolve. Stay alert for new patterns and adjust your tracking accordingly.

**If you use BigQuery, you can identify hundreds of queries! **

Additional Resources

• Google Search Console API Documentation
• Search Console Help Center
• Regular expression testing tools for pattern refinement

Remember: This method identifies potential AI traffic based on query patterns. As the technology evolves, so too will the detection methods need to adapt.

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