GEO Competitive Benchmark β€” AI Search Readiness vs Competitors avatar

GEO Competitive Benchmark β€” AI Search Readiness vs Competitors

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$10.00 / 1,000 site auditeds

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GEO Competitive Benchmark β€” AI Search Readiness vs Competitors

GEO Competitive Benchmark β€” AI Search Readiness vs Competitors

Benchmark your site vs up to 5 competitors for AI-search readiness in one run. See where rivals out-rank you for ChatGPT, Perplexity, Gemini & Claude citation β€” AI-crawler access, schema, extractability, speed, trust β€” as a ranked scoreboard + per-signal gap list. Deterministic GEO/AEO.

Pricing

$10.00 / 1,000 site auditeds

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Berkan Kaplan

Berkan Kaplan

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GEO Benchmark β€” AI Search Visibility πŸ“ˆ

πŸŽ‰ Benchmark a brand’s visibility in AI answers β€” no login, no API key, one row per query/brand, with mention rate, citations and competitor share. Built for SEO/GEO, marketing and brand teams.

πŸ” What is the GEO Benchmark β€” and when should you use it?

Give this actor brands, domains or prompts and it returns matching brands from live AI-assistant answers (generative-engine visibility) β€” as clean, deduplicated rows you can filter, export or feed to an AI agent. Every run queries the source live, so the data is as fresh as the registry itself.

Use it when you need: how often a brand appears in AI answers; competitor share of voice; or a GEO visibility benchmark.

Use something else when: you need classic SEO rankings β€” this measures AI-answer visibility, not search-engine positions.

πŸ€– Use with AI agents

Already on the Apify MCP server? Ask for this Actor by name: foxlabs/geo-benchmark.

Your agent can pay for its own runs. This Actor is pay-per-event with agentic payments, so an agent can discover it, run it and settle the bill over x402 (USDC on Base) or Skyfire β€” no Apify account or API token of its own. Billing is the same either way: per delivered record, never for errors.

Otherwise paste this into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

I want to pull brand company records using the Apify Actor `foxlabs/geo-benchmark`.
Input: `queries` is a list of brands, domains or prompts. `maxResultsPerQuery` caps rows per query.
Start with: {"queries":["undefined"],"maxResultsPerQuery":50}
Ask me what to look up, run the Actor, then summarise the rows as a table.

The machine-readable API, MCP config and OpenAPI definition live at apify.com/foxlabs/geo-benchmark.md.

πŸ“‹ Overview

Everything you need to turn live AI-assistant answers (generative-engine visibility) into clean, structured data β€” in one actor, with no login, cookies or API key.

Why teams pick this actor:

  • βœ… Whole analysis, one call β€” name or ID in, matching brands out.
  • 🧹 No empty-promise columns β€” only fields this registry actually fills; degenerate columns are removed.
  • πŸ”— Stable identifiers β€” every row carries the source's own IDs, ready to join across runs and to other Fox Labs actors.
  • πŸ’° Pay only for results β€” per-row pricing, empty/failed lookups never billed.
  • πŸ€– Agent-ready β€” MCP + x402 agentic payments.

✨ Features

  • πŸ” Name or ID lookup β€” relevance-ranked name search or exact registry-ID lookup.
  • 🏒 Full entity profile β€” status, legal form, formation date, address and the registry’s own contact fields.
  • 🧹 Clean schema β€” deduplicated camelCase rows, ready for CSV/Excel/JSON.

🎬 Quick Start

curl -X POST "https://api.apify.com/v2/acts/foxlabs~geo-benchmark/runs?token=YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{"queries":["undefined"],"maxResultsPerQuery":50}'

πŸš€ Getting Started (3 steps)

  1. Choose your targets β€” brands, domains or prompts.
  2. Set the cap β€” maxResultsPerQuery limits rows per query.
  3. Run and export β€” get a clean dataset as JSON, CSV or Excel.

πŸ“₯ Input

{"queries":["undefined"],"maxResultsPerQuery":50}
FieldTypeDescription
queriesarrayBrands, domains or prompts.
maxResultsPerQueryintegerCaps rows per query.
maxConcurrencyintegerHow many queries to fetch at once.
includeRawbooleanAttach the source’s untouched record under raw.

πŸ“€ Output

One row per company, saved to the dataset. Every row also carries query, scrapedAt, and β€” when a lookup fails β€” an error explaining why (never silently dropped, never billed).

FieldDescription
typeType
domainDomain
isYouIs You
rankRank
overallGeoScoreOverall Geo Score
bandBand
pagesAuditedPages Audited
accessScoreAccess Score
structuredScoreStructured Score
extractabilityScoreExtractability Score
speedScoreSpeed Score
trustScoreTrust Score
aiAccessTextAi Access Text
blockedAiBotsTextBlocked Ai Bots Text
schemaTypesTextSchema Types Text
jsRenderedPagesJs Rendered Pages
hasLlmsTxtHas Llms Txt
yourDomainYour Domain
yourRankYour Rank
totalSitesTotal Sites
yourScoreYour Score
leaderDomainLeader Domain
leaderScoreLeader Score
scoreGapToLeaderScore Gap To Leader
scoreboardTextScoreboard Text
dimensionGapsTextDimension Gaps Text
botGapsTextBot Gaps Text
schemaGapsTextSchema Gaps Text
winsTextWins Text
prioritizedMovesTextPrioritized Moves Text
generatedAtIsoGenerated At Iso

πŸ’Ό Use cases

1. GEO benchmarking β€” measure a brand’s AI-answer visibility. Input: brands or prompts. Output: mention rate + citations. Use: a GEO scorecard.

2. Competitor share β€” compare share of voice vs rivals. Input: brand + competitors. Output: share of voice. Use: a positioning view.

3. Content gaps β€” find prompts where a brand is absent. Input: prompts. Output: presence per prompt. Use: a content plan.

πŸ”— Integration

JavaScript / Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('foxlabs/geo-benchmark').call({"queries":["undefined"],"maxResultsPerQuery":50});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')
run = client.actor('foxlabs/geo-benchmark').call(run_input={"queries":["undefined"],"maxResultsPerQuery":50})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
print(item)

Automation (n8n / Zapier / Make): schedule or webhook β†’ HTTP request to the actor API with your queries β†’ handle the JSON dataset β†’ push to a sheet, CRM or dashboard.

πŸ“Š Pricing

Pay-per-event: per delivered record. Empty or failed lookups are never billed. View current pricing.

❓ FAQ

Do I need an account, login or API key? No. This reads live AI-assistant answers (generative-engine visibility).

What do I search by? Brands, domains or prompts.

How current is the data? Every run queries the source live, so results are as fresh as the registry.

What does it measure? Brand mention rate, citations and competitor share of voice across AI-assistant answers.

Can I export to CSV / Excel / JSON? Yes β€” directly from the Apify dataset.

πŸ› Troubleshooting

  • Fewer rows than expected β€” raise maxResultsPerQuery, or refine the name.
  • A name returns an unexpected entity β€” it matched a similar registered name; search the exact registry ID.
  • No rows for a name β€” try the entity’s exact legal name or its registry ID.

This actor queries public AI assistants and analyses their answers for brand visibility. Results can still contain personal data (e.g. a person’s name); personal data is protected by the GDPR and similar laws, so only process it with a legitimate basis. See Apify’s blog post on the legality of web scraping.

🀝 Support & contact

Changelog

0.1 β€” 2026-09-07

  • Enabled AI-agent payments (x402) + rebuilt the README to the full standard (What-is / when, AI-agents + x402 agentic payments + MCP, Overview, Features, Use cases, Integration, FAQ, Troubleshooting, Support & contact).

0.0

  • Initial release: data from live AI-assistant answers (generative-engine visibility) by name or registry ID.