# AI Visibility Report Generator (`geneius/ai-visibility-report-generator`) Actor Generate client-ready AI search visibility reports from AI Brand Visibility Monitor datasets, including scorecards, competitor mentions, gaps, and actions. - **URL**: https://apify.com/geneius/ai-visibility-report-generator.md - **Developed by:** [Gene](https://apify.com/geneius) (community) - **Categories:** AI, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $500.00 / 1,000 ai visibility report generateds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Report Generator Turn raw AI visibility checks into a client-ready report in one run. This Actor takes records from [AI Brand Visibility Monitor](https://apify.com/geneius/ai-brand-visibility-monitor) or inline visibility-check JSON and generates a structured report with scorecards, brand mention rate, citation coverage, competitor mentions, priority gaps, and recommended next actions. Use it when an agency, GEO/AEO consultant, SEO team, or B2B marketer needs a weekly or monthly AI visibility report they can send to a client instead of cleaning up raw dataset rows by hand. The fastest first run is to paste the example input, run the Actor, and open `REPORT.md` in the run's key-value store. After that, replace the sample records with your AI Brand Visibility Monitor dataset ID or your own visibility-check records. Typical workflow: 1. Run [AI Brand Visibility Monitor](https://apify.com/geneius/ai-brand-visibility-monitor) on buyer-intent prompts. 2. Pass the monitor dataset ID into this Actor. 3. Get one summarized dataset item plus a `REPORT.md` file in the key-value store. 4. Copy the report into a client email, Slack update, Google Doc, or recurring dashboard. ### Workflow Hub See the public [AI visibility workflow](https://geneiusk.github.io/apify-actor-powerhouse-hub/ai-visibility.html) for the monitor -> report path and links to both Actors. Agencies can start from the [GEO client reporting use case](https://geneiusk.github.io/apify-actor-powerhouse-hub/geo-client-reporting.html). For the first proof run, use the [AI Visibility Report demo script](https://geneiusk.github.io/apify-actor-powerhouse-hub/ai-visibility-report-demo.html), [sample REPORT.md](https://geneiusk.github.io/apify-actor-powerhouse-hub/ai-visibility-report-sample.html), and [proof GIF](https://geneiusk.github.io/apify-actor-powerhouse-hub/assets/ai-visibility-report-proof.gif). ### 60-Second First Run You can prove the report flow without an API key, a private dataset, or a production client account. 1. Leave the prefilled `brandName`, `reportPeriod`, and `visibilityChecks` sample in place. 2. Run the Actor once. 3. Open the dataset item to confirm scorecard fields such as `averageVisibilityScore`, `brandMentionRate`, `topCompetitors`, `topGaps`, and `recommendedNextActions`. 4. Open `REPORT.md` in the run's key-value store and paste it into a client email, Slack update, Google Doc, or weekly dashboard. The sample uses fictional Northstar CRM records with one competitor-only answer and one partial brand-mention answer. That gives the report enough signal to show missing comparison content, citation gaps, competitor pressure, and concrete next actions. ### What You Learn - Average AI visibility score for a brand - Brand mention rate across successful checks - Citation/domain coverage - Which competitors appear most often - Which buyer queries are the biggest visibility gaps - What actions to prioritize next ### Use Cases - Weekly GEO/AEO reports for agencies - Monthly client reporting from AI visibility checks - Competitive visibility summaries - Content-priority planning - Slack, Sheets, or dashboard summaries from Apify datasets - Lightweight client proof that turns "we checked AI answers" into a readable deliverable ### When To Use This Actor Use this Actor after you already have AI visibility records. It does not query AI models directly. For data collection, start with [AI Brand Visibility Monitor](https://apify.com/geneius/ai-brand-visibility-monitor). This separation keeps the workflow predictable: - Monitor Actor: collects brand-query-model checks - Report Actor: turns those checks into a readable report ### Input Provide AI Brand Visibility Monitor records inline or pass the source `datasetId`. For the lowest-friction Store run, use inline `visibilityChecks`. For automation, pass a `datasetId` from AI Brand Visibility Monitor and keep the same `brandName` and `reportPeriod` fields. ```json { "brandName": "Northstar CRM", "reportPeriod": "May 2026", "visibilityChecks": [ { "status": "succeeded", "brandMentioned": false, "brandDomainMentioned": false, "visibilityScore": 5, "query": "best CRM for B2B SaaS teams", "modelId": "openrouter/auto", "competitorsMentioned": ["HubSpot", "Salesforce", "Pipedrive"], "recommendation": "Publish comparison and alternatives content for B2B SaaS CRM buyers." }, { "status": "succeeded", "brandMentioned": true, "brandDomainMentioned": false, "visibilityScore": 55, "query": "HubSpot alternatives for product-led sales teams", "modelId": "openrouter/auto", "competitorsMentioned": ["HubSpot"], "recommendation": "Strengthen canonical domain citations and publish a direct alternatives page." } ] } ```` ### Output The dataset contains one summarized report item, and the run writes a Markdown report for easy sharing: ```json { "status": "succeeded", "brandName": "Northstar CRM", "reportPeriod": "May 2026", "checksAnalyzed": 2, "averageVisibilityScore": 30, "brandMentionRate": 0.5, "citationRate": 0, "topCompetitors": [{"name": "HubSpot", "mentions": 2}], "recommendedNextActions": [ "Create or refresh buyer-intent pages where Northstar CRM is absent from AI answers." ] } ``` The run also writes `REPORT.md` to the key-value store for easy copying into docs, emails, or reporting workflows. ### Example Report Sections The generated markdown report includes: - Executive summary - Scorecard - Recommended actions - Top competitors - Priority gaps ### FAQ #### Does this Actor query ChatGPT, Perplexity, Claude, or other AI models? No. This Actor only turns existing visibility-check records into a report. Use [AI Brand Visibility Monitor](https://apify.com/geneius/ai-brand-visibility-monitor) first if you need to collect AI answer visibility data. #### What input do I need for the first run? You need a `brandName` plus either inline `visibilityChecks` or a `datasetId` from AI Brand Visibility Monitor. The Store example uses fictional Northstar CRM records so you can test the report format without credentials or private data. #### What do I get after a run? You get one structured dataset item with summary metrics and a `REPORT.md` file in the key-value store. The Markdown file is designed to paste into a client email, Google Doc, Slack update, or recurring reporting workflow. #### How should I automate this weekly? Schedule AI Brand Visibility Monitor first, then run this Actor with the monitor dataset ID. Use `reportPeriod` to label the week or month, then route `REPORT.md` into your reporting destination. #### How much does it cost? The configured paid event is `ai-visibility-report-generated` at `$0.50` per generated report. Check the live Store pricing panel before running large volumes because Apify platform pricing and account-level charges can change. #### Can I use my own visibility data? Yes. Inline records should include fields such as `status`, `brandMentioned`, `visibilityScore`, `query`, `modelId`, `competitorsMentioned`, and `recommendation`. Records with more context usually produce a more useful report. ### Pricing Default monetization model: pay per event. Recommended chargeable event: - Event name: `ai-visibility-report-generated` - Event meaning: one generated AI visibility report - Store price: `$0.50` per report This Actor is public at `https://apify.com/geneius/ai-visibility-report-generator`. PPE was configured before publication, and public smoke runs have recorded the expected `ai-visibility-report-generated` charge. ### Limitations - This Actor does not query AI models directly. Use AI Brand Visibility Monitor first. - Report quality depends on the breadth and freshness of supplied visibility checks. - It is best sold as a companion workflow, not as a raw standalone scraper. ### Automation And Agent Use - Schedule AI Brand Visibility Monitor weekly, then run this Actor on the resulting dataset. - Store `REPORT.md` in a client folder or send it to Slack. - Append report metrics to Google Sheets for trend tracking. - Use `topGaps` and `recommendedNextActions` to drive content backlog planning. ### Local Development ```bash python3 -m pip install -r requirements.txt ACTOR_TEST_PAY_PER_EVENT=true apify run --purge --input-file examples/smoke-input.json ``` # Actor input Schema ## `brandName` (type: `string`): Brand or client name for the report. ## `reportPeriod` (type: `string`): Human-readable report period, such as May 2026 or Week of 2026-05-11. ## `visibilityChecks` (type: `array`): Optional AI Brand Visibility Monitor records. Leave the prefilled Northstar CRM sample in place for a no-credential first run that generates REPORT.md immediately, or replace it with your own visibility-check records. ## `datasetId` (type: `string`): Optional Apify dataset ID from AI Brand Visibility Monitor. ## `maxChecks` (type: `integer`): Safety cap for visibility checks analyzed in one report. ## `datasetItemLimit` (type: `integer`): Maximum number of records to load from the source dataset. ## Actor input object example ```json { "brandName": "Northstar CRM", "reportPeriod": "May 2026", "visibilityChecks": [ { "status": "succeeded", "brandMentioned": false, "brandDomainMentioned": false, "visibilityScore": 5, "query": "best CRM for B2B SaaS teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot", "Salesforce", "Pipedrive" ], "recommendation": "Publish comparison and alternatives content for B2B SaaS CRM buyers." }, { "status": "succeeded", "brandMentioned": true, "brandDomainMentioned": false, "visibilityScore": 55, "query": "HubSpot alternatives for product-led sales teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot" ], "recommendation": "Strengthen canonical domain citations and publish a direct alternatives page." } ], "maxChecks": 1000, "datasetItemLimit": 1000 } ``` # Actor output Schema ## `results` (type: `string`): Dataset item with the report summary, key metrics, gaps, competitors, recommended actions, and embedded Markdown report. ## `report` (type: `string`): Client-ready Markdown report saved to the run key-value store. ## `summary` (type: `string`): Run-level summary with analyzed check count, average score, and charge event status. # 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 = { "brandName": "Northstar CRM", "reportPeriod": "May 2026", "visibilityChecks": [ { "status": "succeeded", "brandMentioned": false, "brandDomainMentioned": false, "visibilityScore": 5, "query": "best CRM for B2B SaaS teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot", "Salesforce", "Pipedrive" ], "recommendation": "Publish comparison and alternatives content for B2B SaaS CRM buyers." }, { "status": "succeeded", "brandMentioned": true, "brandDomainMentioned": false, "visibilityScore": 55, "query": "HubSpot alternatives for product-led sales teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot" ], "recommendation": "Strengthen canonical domain citations and publish a direct alternatives page." } ] }; // Run the Actor and wait for it to finish const run = await client.actor("geneius/ai-visibility-report-generator").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 = { "brandName": "Northstar CRM", "reportPeriod": "May 2026", "visibilityChecks": [ { "status": "succeeded", "brandMentioned": False, "brandDomainMentioned": False, "visibilityScore": 5, "query": "best CRM for B2B SaaS teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot", "Salesforce", "Pipedrive", ], "recommendation": "Publish comparison and alternatives content for B2B SaaS CRM buyers.", }, { "status": "succeeded", "brandMentioned": True, "brandDomainMentioned": False, "visibilityScore": 55, "query": "HubSpot alternatives for product-led sales teams", "modelId": "openrouter/auto", "competitorsMentioned": ["HubSpot"], "recommendation": "Strengthen canonical domain citations and publish a direct alternatives page.", }, ], } # Run the Actor and wait for it to finish run = client.actor("geneius/ai-visibility-report-generator").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 '{ "brandName": "Northstar CRM", "reportPeriod": "May 2026", "visibilityChecks": [ { "status": "succeeded", "brandMentioned": false, "brandDomainMentioned": false, "visibilityScore": 5, "query": "best CRM for B2B SaaS teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot", "Salesforce", "Pipedrive" ], "recommendation": "Publish comparison and alternatives content for B2B SaaS CRM buyers." }, { "status": "succeeded", "brandMentioned": true, "brandDomainMentioned": false, "visibilityScore": 55, "query": "HubSpot alternatives for product-led sales teams", "modelId": "openrouter/auto", "competitorsMentioned": [ "HubSpot" ], "recommendation": "Strengthen canonical domain citations and publish a direct alternatives page." } ] }' | apify call geneius/ai-visibility-report-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=geneius/ai-visibility-report-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Visibility Report Generator", "description": "Generate client-ready AI search visibility reports from AI Brand Visibility Monitor datasets, including scorecards, competitor mentions, gaps, and actions.", "version": "0.1", "x-build-id": "PKAHV4uMLx2gddmzU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/geneius~ai-visibility-report-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-geneius-ai-visibility-report-generator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/geneius~ai-visibility-report-generator/runs": { "post": { "operationId": "runs-sync-geneius-ai-visibility-report-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/geneius~ai-visibility-report-generator/run-sync": { "post": { "operationId": "run-sync-geneius-ai-visibility-report-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "brandName" ], "properties": { "brandName": { "title": "Brand name", "type": "string", "description": "Brand or client name for the report." }, "reportPeriod": { "title": "Report period", "type": "string", "description": "Human-readable report period, such as May 2026 or Week of 2026-05-11.", "default": "latest run" }, "visibilityChecks": { "title": "Inline visibility checks", "minItems": 1, "type": "array", "description": "Optional AI Brand Visibility Monitor records. Leave the prefilled Northstar CRM sample in place for a no-credential first run that generates REPORT.md immediately, or replace it with your own visibility-check records." }, "datasetId": { "title": "Source dataset ID", "minLength": 1, "type": "string", "description": "Optional Apify dataset ID from AI Brand Visibility Monitor." }, "maxChecks": { "title": "Maximum checks", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Safety cap for visibility checks analyzed in one report.", "default": 1000 }, "datasetItemLimit": { "title": "Dataset item limit", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of records to load from the source dataset.", "default": 1000 } } }, "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 } } } } } } } } } } ```