# AI Visibility Audit — AEO / GEO Consultant (`bovi/ai-visibility-audit`) Actor Audit a brand's AI-search visibility across its key queries. Structured rows: per-query AI Overview presence, brand citations, organic rank, citation gaps, outreach targets, plus a roll-up with share-of-voice and recommended focus. Pay per row. - **URL**: https://apify.com/bovi/ai-visibility-audit.md - **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community) - **Categories:** SEO tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.37 / 1,000 audit-rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Visibility Audit — AEO / GEO Consultant Run a full **AI-search visibility audit** for one brand across the queries that matter to it. Where the *tracker* monitors single queries, the *audit* answers the consultant question: **how visible am I in AI answers overall, who is eating my share of voice, and exactly what do I fix?** — delivered as structured rows you can pipe, not a prose report. ### Output (structured rows, two shapes) **`row_type: "query"`** — one per audited query: | Field | Meaning | |---|---| | `ai_overview_present` / `brand_in_ai_overview` | Did the AI answer this query, and are you in it | | `citation_gap` | **true** = the AI answers without you — your opportunity | | `outreach_targets` | The domains the AI cited on a gap query — go earn a mention there | | `brand_organic_rank` | Classic organic position (or null) | | `visibility_score` | 0–100 per query | | `recommended_action` | The concrete next move for this query | **`row_type: "summary"`** — one roll-up: | Field | Meaning | |---|---| | `overall_visibility_score` | Average across all queries | | `ai_overview_coverage_pct` | Share of queries that surfaced an AI Overview | | `brand_ai_share_of_voice_pct` | Of the AI Overviews, how many include you | | `citation_gaps` | How many queries answer without you | | `top_competitor_domains` | Who the AI cites most across your queries (share of voice) | | `outreach_targets` | The deduped, ranked outreach pool | | `recommended_focus` | The single highest-leverage focus | ### How to use it Feed `outreach_targets` straight into a contact-details scraper to find the site owners and request a mention — that is how AEO link-building actually works (~85% of AI brand mentions come from external domains). ### Access & pricing A protected target — runs through the Apify **GOOGLE_SERP** proxy, proxy from the first byte. Pay per row (PPE). Re-run weekly/monthly: 40–60% of AI citations change month-to-month, so a one-time audit goes stale. Results are localized — set `countryCode` / `languageCode` per market. # Actor input Schema ## `queries` (type: `array`): The searches that matter to this brand — the audit set. ## `brand` (type: `string`): Brand name to detect in AI answers. ## `brandDomains` (type: `array`): Your domains — the first is the audited domain. Used to detect citations and organic rank. ## `brandAliases` (type: `array`): Alternate spellings/product names to also match in AI-answer text. ## `countryCode` (type: `string`): Two-letter country for localized results. ## `languageCode` (type: `string`): Two-letter UI language. ## `renderJs` (type: `boolean`): Render each SERP in a real browser to capture the JS-gated AI Overview / AI Mode. Keep ON for an AI-visibility audit. ## `proxyConfiguration` (type: `object`): Apify proxy. RESIDENTIAL is required for the browser-render path that captures AI Overview / AI Mode. ## Actor input object example ```json { "queries": [ "best project management software", "asana vs monday", "best crm for startups" ], "brandDomains": [ "asana.com" ], "countryCode": "us", "languageCode": "en", "renderJs": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ```` # Actor output Schema ## `results` (type: `string`): Dataset of audit rows (row\_type=query findings and row\_type=summary). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "best project management software", "asana vs monday", "best crm for startups" ], "brandDomains": [ "asana.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("bovi/ai-visibility-audit").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": [ "best project management software", "asana vs monday", "best crm for startups", ], "brandDomains": ["asana.com"], } # Run the Actor and wait for it to finish run = client.actor("bovi/ai-visibility-audit").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "queries": [ "best project management software", "asana vs monday", "best crm for startups" ], "brandDomains": [ "asana.com" ] }' | apify call bovi/ai-visibility-audit --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bovi/ai-visibility-audit", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Visibility Audit — AEO / GEO Consultant", "description": "Audit a brand's AI-search visibility across its key queries. Structured rows: per-query AI Overview presence, brand citations, organic rank, citation gaps, outreach targets, plus a roll-up with share-of-voice and recommended focus. Pay per row.", "version": "0.1", "x-build-id": "SaVcgAuue775TDqMG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bovi~ai-visibility-audit/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bovi-ai-visibility-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/bovi~ai-visibility-audit/runs": { "post": { "operationId": "runs-sync-bovi-ai-visibility-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/bovi~ai-visibility-audit/run-sync": { "post": { "operationId": "run-sync-bovi-ai-visibility-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Queries", "type": "array", "description": "The searches that matter to this brand — the audit set.", "items": { "type": "string" } }, "brand": { "title": "Brand name", "type": "string", "description": "Brand name to detect in AI answers." }, "brandDomains": { "title": "Brand domains", "type": "array", "description": "Your domains — the first is the audited domain. Used to detect citations and organic rank.", "items": { "type": "string" } }, "brandAliases": { "title": "Brand aliases", "type": "array", "description": "Alternate spellings/product names to also match in AI-answer text.", "items": { "type": "string" } }, "countryCode": { "title": "Country", "type": "string", "description": "Two-letter country for localized results.", "default": "us" }, "languageCode": { "title": "Language", "type": "string", "description": "Two-letter UI language.", "default": "en" }, "renderJs": { "title": "Render JS (browser)", "type": "boolean", "description": "Render each SERP in a real browser to capture the JS-gated AI Overview / AI Mode. Keep ON for an AI-visibility audit.", "default": true }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Apify proxy. RESIDENTIAL is required for the browser-render path that captures AI Overview / AI Mode.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```