Search Console for AI: Why It Doesn't Exist Yet (and What to Use Instead)

Where's the Search Console for AI?

If you work in SEO, you've probably asked this question already. Google Search Console gives you impressions, clicks, average position, and CTR for every query that surfaces your site. It's the single most important free tool in the SEO toolkit. So where's the equivalent for AI search?

When someone asks ChatGPT "best email marketing platform for startups" or Perplexity "top email automation tools," brands get mentioned, recommended, and linked. That's real visibility. But unlike Google, there's no dashboard from OpenAI or Anthropic telling you how often your brand appeared, where you ranked, or which queries triggered mentions.

The short answer: it doesn't exist. Not from OpenAI, not from Anthropic, not from Google's Gemini team, not from Perplexity. And it probably won't for a while. But that doesn't mean you can't track your AI search visibility. You just need a different approach.

Why There's No Official AI Search Console

Google Search Console exists because Google's search model is structured and measurable. Every query produces a ranked list of URLs. Google knows exactly which URLs appeared for which queries, how many times, and whether users clicked. The data is clean and well-defined.

AI search engines work fundamentally differently:

• No fixed result set. AI responses are generated, not retrieved. The same query can produce different brand mentions depending on model version, context, and even time of day.
• No impression/click separation. In AI search, there's no distinction between "appearing in results" and "being clicked." If an AI mentions your brand, the user already saw it. There's no click-through to measure.
• No webmaster verification. Google Search Console works because you verify site ownership. AI models don't have a concept of "your website" - they mention brands and concepts drawn from their training data and retrieval systems.
• No business incentive (yet). Google built Search Console to help webmasters create better content, which improved search quality. AI companies haven't reached the point where helping brands optimize for AI responses benefits their product.
• Non-deterministic outputs. The same prompt can yield different responses across runs. There's no stable "ranking" in the way Google has positions 1-10 on a SERP.

These aren't temporary limitations. They reflect a fundamental difference in how AI search works compared to traditional search. An official "AI Search Console" would need to redefine what impressions, rankings, and clicks mean in a generative context.

What a Search Console for AI Would Look Like

If OpenAI or Anthropic built an analytics console for brands, what metrics would it show? Here's what would map from Google Search Console, and what would need to be reinvented:

Some of these metrics are richer than what Google Search Console provides. Sentiment and share of voice, for example, have no direct equivalent in traditional search. An AI search console wouldn't just replicate GSC - it would offer an entirely new layer of competitive intelligence.

How to Build Your Own AI Search Console with Sellm

You can't wait for OpenAI to build this for you. But you can build it yourself using the Sellm API. Sellm queries AI providers on your behalf, extracts structured data from the responses, and returns the exact metrics you need.

Here's how Sellm's data maps to the AI search console metrics described above:

Prompts = Queries

In Google Search Console, you see which queries triggered impressions. In Sellm, you define the prompts (queries) you want to track. Think of these as the AI-era equivalent of your target keywords:

curl -X POST https://sellm.io/api/v1/async-analysis \
  -H "Authorization: Bearer sellm_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "best email marketing platform for startups",
    "providers": ["chatgpt", "claude", "perplexity", "gemini", "grok"],
    "locations": ["US"],
    "replicates": 3
  }'

coveragePct = Impressions

The summary.coveragePct field tells you what percentage of AI responses mentioned your brand. This is the closest equivalent to Google Search Console impressions - it measures how often your brand appeared across all tracked providers and replicates.

# From the async analysis response (GET /v1/async-analysis/{analysisId})
"summary": {
  "sovPct": 15,
  "coveragePct": 70,
  "avgPos": 2.3,
  "sentiment": 0.72
}
// Your brand appeared in 70% of AI responses

avgPos = Rankings

The summary.avgPos field tells you where your brand typically appears in AI responses. A value of 1.0 means you're always mentioned first. Unlike Google's position 1-10, AI position reflects the order of brand mentions within a single generated response.

brandSentiment = Quality Score

Google Search Console doesn't tell you how Google describes your brand. Sellm does. The sentiment score (0-10) measures how positively AI engines talk about you, broken down into trustworthiness, authority, recommendation strength, and fit for query intent.

sovPct = Visibility Share

summary.sovPct tells you what percentage of brand mentions are yours vs. competitors. If ChatGPT mentions five email marketing platforms when asked "best email marketing platform for startups," and yours is one of them, your share of voice for that prompt is 20%. This metric has no equivalent in Google Search Console and is one of the most valuable signals for competitive positioning.

citedUrls = Which Pages Drive Visibility

When AI engines cite sources (especially Perplexity, which always links to references), the citedUrls field shows which of your pages are being used as evidence. This is the AI equivalent of Google Search Console's "Pages" report - it tells you which content assets are earning you visibility.

Setting Up Weekly Automated Tracking

A search console is only useful if it runs continuously. Here's how to set up weekly automated tracking that mirrors the cadence of Google Search Console data:

import requests
import json
import os
import time
from datetime import datetime

API_KEY = os.environ["SELLM_API_KEY"]
BASE_URL = "https://sellm.io/api/v1"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}

PROMPTS = [
    "best email marketing platform for startups",
    "top email automation tools for small business",
    "which email marketing service has the best deliverability",
]
PROVIDERS = ["chatgpt", "claude", "perplexity", "gemini", "grok"]


def submit_analysis(prompt):
    """Submit a prompt for async analysis across all providers."""
    resp = requests.post(f"{BASE_URL}/async-analysis", headers=HEADERS, json={
        "prompt": prompt,
        "providers": PROVIDERS,
        "locations": ["US"],
        "replicates": 3,
    })
    resp.raise_for_status()
    return resp.json()["data"]["id"]


def poll_until_done(analysis_id, timeout=300):
    """Poll an async analysis until it succeeds or fails."""
    deadline = time.time() + timeout
    while time.time() < deadline:
        resp = requests.get(
            f"{BASE_URL}/async-analysis/{analysis_id}",
            headers=HEADERS,
        )
        resp.raise_for_status()
        data = resp.json()["data"]
        if data["status"] in ("succeeded", "failed"):
            return data
        time.sleep(10)
    raise TimeoutError(f"Analysis {analysis_id} did not finish in {timeout}s")


def build_ai_search_console_report():
    """Build a weekly report mimicking a search console."""
    results = []
    for prompt in PROMPTS:
        aid = submit_analysis(prompt)
        print(f"Submitted: {prompt} -> {aid}")
        data = poll_until_done(aid)
        results.append(data)

    # Each completed analysis contains summary, providerBreakdown,
    # promptBreakdown, and results[] with per-replicate details.
    report = {
        "reportDate": datetime.now().isoformat(),
        "analyses": [],
    }

    for data in results:
        summary = data["summary"]
        report["analyses"].append({
            "analysisId": data["id"],
            "prompt": data["prompt"],
            "finishedAt": data.get("finishedAt"),
            "overview": {
                "sovPct": summary["sovPct"],
                "coveragePct": summary["coveragePct"],
                "avgPos": summary["avgPos"],
                "sentiment": summary["sentiment"],
            },
            "providerBreakdown": data.get("providerBreakdown"),
            "resultCount": len(data.get("results", [])),
        })

    filename = f"ai_search_console_{datetime.now().strftime('%Y-%m-%d')}.json"
    with open(filename, "w") as f:
        json.dump(report, f, indent=2)
    print(f"\nReport saved to {filename}")

    # Print summary for each prompt
    for analysis in report["analyses"]:
        s = analysis["overview"]
        print(f"\n=== {analysis['prompt']} ===")
        print(f"  Share of Voice: {s['sovPct']}%")
        print(f"  Coverage:       {s['coveragePct']}%")
        print(f"  Avg Position:   {s['avgPos']}")
        print(f"  Sentiment:      {s['sentiment']}")

        breakdown = analysis.get("providerBreakdown", {})
        if breakdown:
            print(f"  --- By Provider ---")
            for p in breakdown.get("sovByProvider", []):
                print(f"    {p['provider']:12s}  SOV: {p['sov']}%")

    return report


if __name__ == "__main__":
    build_ai_search_console_report()

Schedule this script with a cron job or GitHub Action to run weekly:

# crontab -e
0 9 * * 1 SELLM_API_KEY=sellm_xxx python3 ai_search_console.py

Key Reports to Build

A useful AI search console needs five core reports. Here's what to build and why each matters:

1. Visibility Trend

Track share of voice and coverage over time. This is the AI equivalent of the "Performance" graph in Google Search Console. Rising SOV means AI engines are mentioning you more frequently relative to competitors.

# Run weekly analyses and compare results over time
# Each GET /v1/async-analysis/{id} response includes summary metrics
analysis = poll_until_done(analysis_id)
s = analysis["summary"]
print(f"SOV: {s['sovPct']}%  Coverage: {s['coveragePct']}%  "
      f"Avg Position: {s['avgPos']}  Sentiment: {s['sentiment']}")

2. Position Trend

Monitor your average mention position over time. In Google Search Console, moving from position 8 to position 3 is a major win. In AI search, moving from position 4 (mentioned fourth) to position 1 (mentioned first) has a similar impact on user perception.

3. Provider Comparison

Your visibility may differ dramatically across AI platforms. You might rank #1 in ChatGPT but barely appear in Claude. This report shows where to focus optimization efforts, much like comparing Google Search vs. Google Discover in GSC.

4. Prompt Performance

The per-prompt breakdown is the AI equivalent of GSC's "Queries" report. It shows which queries trigger your brand mentions and which don't. Prompts where you have low or zero visibility are optimization opportunities.

5. Citation Sources

Which of your pages are AI engines citing as sources? This tells you which content assets are earning you visibility, similar to GSC's "Pages" report. If your blog post on "email marketing platform comparison" gets cited frequently but your product page doesn't, that's an actionable insight.

Google Search Console vs. AI Search Console: What Maps and What Doesn't

Here's a detailed comparison to help SEO professionals translate their existing workflow:

The key takeaway: an AI search console actually provides more competitive intelligence than Google Search Console. GSC only shows your own data. With Sellm, you see exactly which competitors appear alongside you and how you compare on every prompt.

The Future: Will OpenAI or Anthropic Ever Release Analytics?

It's worth asking whether the AI companies themselves will eventually build a search console. Here's the landscape as of early 2026:

• OpenAI: Has not announced any webmaster or brand analytics tools. Their focus remains on model capabilities, ChatGPT consumer features, and enterprise API. A search console would require them to track and expose which brands appear in responses at scale, which raises privacy and legal questions.
• Anthropic: Similarly focused on model safety and capabilities. Claude doesn't have a "webmaster tools" equivalent and there's no public indication one is planned.
• Google (Gemini): The most likely candidate to build something, since they already have Search Console infrastructure. However, Gemini's AI responses are distinct from traditional search results, and Google has kept them separate in their analytics so far.
• Perplexity: The closest to a traditional search engine among AI platforms, since it always cites sources. A Perplexity publisher analytics tool is plausible but hasn't been announced.

Even if AI companies do release analytics tools, they'll face limitations. Each company would only show data for their own platform. You'd need to check five different consoles to get a complete picture. And they're unlikely to show you competitor data, which is one of the most valuable aspects of AI search tracking.

A third-party solution like Sellm solves both problems: cross-platform visibility in one place, with built-in competitive intelligence.

How Sellm Fills the Gap Today

Waiting for an official AI search console means flying blind while your competitors figure out AI visibility. Sellm gives you the data layer now:

• Cross-platform tracking: Monitor ChatGPT, Claude, Perplexity, Gemini, Grok, and Copilot from a single dashboard and API
• Structured metrics: Share of voice, coverage, position, sentiment, and citation data - extracted and normalized from raw AI responses
• Automated monitoring: Weekly scheduled runs that track your visibility over time without manual effort
• Competitive intelligence: See which competitors appear in AI responses alongside your brand, and how you compare
• API-first: Pull all data into your existing BI tools, data warehouses, or custom dashboards via the REST API
• Historical trends: Track how your AI visibility changes week over week, just like GSC's date comparison feature

Paid plans offer weekly automated tracking at less than 1 cent per prompt analysis.