How To Track Google AI Mode Traffic In Search Console

On June 3, 2026, Google launched Search Generative AI performance reports in Search Console, devoted views of impressions inside generative AI options on Search, comparable to AI Overviews and AI Mode. Ultimately, there’s official AI Overviews reporting displaying how typically these options contain your pages. What it doesn’t embrace is the queries behind the AI exercise, and the report is UI solely. I re-verified alone property on 11 August 2026 that neither the Search Analytics API nor the BigQuery bulk export exposes the generative AI information, so there’s no official option to monitor AI Mode site visitors at question degree.

Besides the queries flip up anyway. Fragments of actual AI conversations, replies like “sure go on,” follow-ups, and entire pasted prompts, began showing as unusual queries within the efficiency report. Anastasia Kourou posted about the strange strings on LinkedIn, and Google’s John Mueller replied, confirming this information had at all times been obtainable within the efficiency report and pointing to Google’s documentation.

I’ve written the full account with 16 months of my own data individually and deployed my methodology of extracting these queries by way of my Google Search Console MCP, so what follows is simply the sensible methods to drag the AI queries in Search Console out into the open plus a free device powered by my very own custom-trained machine studying mannequin that does it for you.

The Scramble For The Queries

As soon as the leak was public, SEOs began constructing methods to drag these queries out.

The early wave, again in June 2025, was phrase depend regex, surfacing lengthy conversational queries by size alone, something of 32+ phrases, for instance. Barry Schwartz rounded up those early attempts, crediting Metehan Yeşilyurt, Vijay Chauhan, and David Konitzny. The approaches have gotten much more particular since.

Methodology 1: Glenn Gabe’s Full Stock In Excel

The efficiency report UI caps any desk at 1,000 rows, so on an enormous website most of your question information stays hidden earlier than you begin. Glenn Gabe’s answer is to skip the UI. He pulls the entire question set via the Search Analytics API utilizing Analytics Edge in Excel, then has Claude manage the record and flag seemingly AI Mode queries. If you happen to already reside in Excel, this places the complete stock contained in the workflow you recognize.

Methodology 2: Jean-Christophe Chouinard’s Customized Regex

Jean-Christophe Chouinard went after the identical downside from contained in the report. On August 14, 2026 he published a regex on LinkedIn that flags conversational strings straight within the efficiency report’s Question filter, utilizing the Customized regex possibility. He constructed it by evaluating prompts individuals generally kind into LLMs, from information he has entry to, in opposition to strings hardly ever seen in GSC earlier than AI Overviews.

The result’s one lengthy alternation overlaying immediate verbs (write, draft, generate, summarize, clarify, act as), greetings, acknowledgements (sure, okay, sounds good, sure go on), refusals and observe ups (extra, proceed, present me extra, every other choices). Paste it into the filter and also you get an prompt look, free, contained in the report you have already got open.

Individually, his January 12, 2026 case study confirmed AI Mode clicks are tracked in Search Console, however the queries behind them are nearly completely anonymized, a caveat that hangs over each methodology right here.

Methodology 3: Amin Foroutan’s Superior GSC Visualizer

Amin Foroutan’s Advanced GSC Visualizer is a free Chrome extension that bolts superior charting, annotations, and an AI assistant onto your Search Console information, with one-click API entry. There’s no AI Mode-specific filter in it.

Methodology 4: My MCP Servers

My Search Console MCP has the dialog detector in-built as a device referred to as genai_conversation_queries, and my BigQuery MCP runs the identical detector in opposition to the majority export, which is the place the anonymized pool of queries lives. Each run domestically, so your information strikes solely between you and Google. What they do properly is automation and scale with query-level labels on each row. The restrict is setup reasonably than detection, because you want an MCP consumer and a config, in order that they swimsuit technical customers.

The 4 Strategies Facet By Facet

Look down the Limits column and the primary three share a spot. Sample lists and per-run sorting can’t reliably catch edge instances like rank tracker probes, agent harness prompts, pasted strings, or “my location is” probes. They’re constructed for English too, so a reply in Tamil or a code-mixed Hinglish string sails via unlabelled. Methodology 4 will resolve this downside with a {custom} ML mannequin.

Why This Wants An ML Mannequin

Classifying each question into named buckets, throughout languages, with the bizarre machine-generated strings included could be very exhausting to do precisely with standard strategies. As a result of there are such a lot of edge instances, and so forth. So the one dependable option to get this working is to coach a machine studying mannequin.

So I made a decision to construct one.

How I Constructed The Detector

The detector has two elements. Deterministic guidelines personal the precise lessons (reply artifacts, tracker probes, agent harness prompts), and a educated mannequin owns the fuzzy boundary between conversational, lengthy tail, and unusual.

The mannequin is FacebookAI’s xlm-roberta-base, pre-trained on 100 languages and fine-tuned alone labelled Search Console dialog exhaust plus artificial rows throughout eight languages, together with code-mixed Tanglish and Hinglish. I validated it throughout simply over 120,000 queries scored, and retrained it twice on no matter broke. It runs quantized on a scale-to-zero Cloudflare container, and the identical mannequin now powers a free device anybody can use with none setup.

The Instrument

That device is the AI Mode and AI Overview query classifier, free on this website, and also you don’t have to enroll or give me your e-mail.

If the GSC MCP route appeared like effort, this is identical detector with that effort eliminated.

How It Works

Drop in Search Console or BigQuery question exports as CSV, and several other information stack into one run.

Step 1: The mannequin will classify your queries into considered one of seven buckets.

Step 2: If you happen to add a Search Console generative AI report, the device will extract web page information and map this in opposition to your queries. (In your report, you’ll see an “AI web page” column. You’ll see the complete URL if you export the CSV.)

The deterministic guidelines label the plain buckets proper in your browser, and people queries by no means depart your gadget. I do that as a result of I wish to reduce the quantity of knowledge I course of and to assist pace issues up.

Every part else goes to the mannequin, will get labeled in reminiscence, and none of that is saved. Every question comes again with a bucket and a confidence rating. In plain phrases, the buckets are full conversational queries, brief replies like “sure go on,” follow-up pivots like “what concerning the professional plan,” rank tracker probes, agent harness prompts, pasted strings, and unusual searches. You possibly can filter by bucket, classifier and confidence, then export each labelled row to CSV with no row cap.

How To Use It

5 steps, begin to end.

1. Get your queries out. Underneath about 1,000 queries, the efficiency report’s regular export is already full. Larger websites can stack a number of filtered exports, pull the complete record via the API with Search Analytics for Sheets or Looker Studio, or dump the BigQuery bulk export desk to CSV. If you happen to’re an search engine optimization, you already know how you can get greater than 1000 queries off GSC.
2. Drop the CSV or CSVs onto the web page.
3. Press Classify.
4. Filter or search the labelled desk.
5. Export the labelled CSV.

Limitations

The device comfortably handles as much as 100,000 distinctive queries per run within the browser, and an enormous run wants the tab stored open, which I settle for is a really 2010 option to compute. The second restrict applies to each approach of monitoring AI Mode in Google Search Console, mine included. Google anonymizes uncommon queries; dialog strings are uncommon, and a part of the pool by no means exhibits, so deal with the output outcomes as an undercount. My BigQuery export exhibits 57.7% of my impressions sat within the anonymized pool over the past 59 days, measured on August 11, 2026.

The mannequin prices me actual cash to run, and as you already know me, I don’t promote something, and none of my instruments require any type of sign-ups or paywalls. So I’ve to place some limitations and caps so everybody can use this device.

So, please play good. You don’t wish to be the one who ruins issues.

The device is in beta, and if a great deal of individuals begin utilizing it, it’ll decelerate, and processing might take longer. In that case, wait and check out once more.

Additionally, kindly be aware I provide my time and experience freed from cost, so don’t demand buyer help, and so forth. Sure, I’ve to write down these things as a result of some individuals don’t act like cheap adults.

Over 100,000 Queries

Above 100,000 distinctive queries, we have to do a batch run as a substitute. Ship yours in, and my company runs it as a correct batch job, then emails you the entire outcomes. We are able to additionally flip the findings right into a full, actionable report, or assist together with your wider AI search engine optimization work.

Extra Assets:

This submit was initially revealed on Suganthan.

Featured Picture: Roman Samborskyi/Shutterstock