# AI Search Visibility Auditor (`junipr/ai-search-visibility-auditor`) Actor Audit supplied AI-answer snapshots for brand mentions, domain citations, competitor visibility, sentiment context, and visibility regressions. - **URL**: https://apify.com/junipr/ai-search-visibility-auditor.md - **Developed by:** [junipr](https://apify.com/junipr) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.50 / 1,000 evidence source processeds 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/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 Search Visibility Auditor ### Store Positioning **Store title:** AI Search Visibility Auditor **Short description:** Audit supplied AI-answer snapshots for brand mentions, domain citations, competitor visibility, sentiment context, and visibility regressions. **SEO title:** AI Search Visibility Auditor — evidence-backed report and intelligence workflow **SEO description:** Audit supplied AI-answer snapshots for brand mentions, domain citations, competitor visibility, sentiment context, and visibility regressions. Use it to turn sources into decision-ready rollups, executive summaries, and evidence-backed recommendations. **Categories:** DEVELOPER_TOOLS **Keywords:** ai, search, visibility, auditor, premium report/intelligence actor ### Fixed-Inclusive PPE Pricing This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model. - Tier: R1 — Premium report/intelligence actor - Primary event: `evidence-source-processed` at $0.00892 base - Default max charge: $25.00 - Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount Event set: - `actor-start`: base $0.02000, GOLD $0.01600. Ai Search Visibility Auditor: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `evidence-source-processed`: base $0.00892, GOLD $0.00714. Ai Search Visibility Auditor: charged when evidence source processed is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `finding-emitted`: base $0.00892, GOLD $0.00714. Ai Search Visibility Auditor: charged when finding emitted is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `report-section-generated`: base $0.03600, GOLD $0.02880. Ai Search Visibility Auditor: charged when report section generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `executive-report-generated`: base $0.25000, GOLD $0.20000. Ai Search Visibility Auditor: charged when executive report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended. ### Public Task Concepts - Build an AI Search Visibility stakeholder brief from source rows - Prioritize AI Search Visibility findings with evidence and confidence - Summarize AI Search Visibility metrics into recommended actions - Prepare an AI Search Visibility source table with uncertainty notes - Export AI Search Visibility decision rows for review Audit supplied AI-answer snapshots for brand mentions, domain citations, competitor visibility, sentiment context, and visibility regressions. This actor is snapshot-first by design. It does not call ChatGPT, Claude, Perplexity, Google, Bing, or any other live AI/search API. You provide answer text and citations captured from the AI surface you care about, and the actor returns deterministic visibility scoring and recommendations. ### What It Checks - Brand/entity mention count and whether the brand appears at all. - Brand-domain citation coverage. - Citation URLs and normalized citation domains. - Competitor mentions and competitor citation domains. - Brand share of voice across brand plus competitor mentions. - Simple positive/neutral/negative brand context. - Visibility score versus an optional previous baseline. - Risk signals such as missing brand, missing citation, competitor dominance, uncited claims, and score regression. ### Input Example Set `maxChargeUsd` to a finite run budget. It defaults to `$10`; the actor stops before paid work or withholds uncharged reports when the next event would exceed the cap. ```json { "brandName": "junipr", "brandDomain": "junipr.io", "competitors": [ { "name": "Apify", "domain": "apify.com" }, { "name": "Diffbot", "domain": "diffbot.com" } ], "snapshots": [ { "promptId": "ai-web-extraction-shortlist", "prompt": "Which tools should I evaluate for AI-ready web extraction?", "engine": "fixture-ai-answer", "answerText": "For AI-ready web extraction, teams often compare Apify, Diffbot, and junipr. junipr is useful for focused automation apps and structured exports.", "citations": [ { "url": "https://junipr.io/apps", "title": "Junipr Apps" }, { "url": "https://apify.com/store", "title": "Apify Store" } ], "previousVisibilityScore": 62 } ], "includeReport": true, "maxSnapshots": 1 } ```` ### Output Each dataset item represents one audited AI-answer snapshot: ```json { "promptId": "ai-web-extraction-shortlist", "status": "strong", "visibilityScore": 90.8, "previousVisibilityScore": 62, "visibilityDelta": 28.8, "brandMentioned": true, "brandMentionCount": 2, "brandDomainCited": true, "brandCitationCount": 1, "citationDomains": ["apify.com", "junipr.io"], "competitorMentionCount": 3, "shareOfVoice": 0.4, "sentiment": "positive", "riskSignalCodes": ["competitor-dominates"], "recommendations": ["Map the competitor terms appearing in the answer and publish differentiated proof points for those comparison intents."] } ``` When `includeReport` is enabled, the actor also writes: - `AI_SEARCH_VISIBILITY_RESULTS.json` - `AI_SEARCH_VISIBILITY_SUMMARY.json` - `AI_SEARCH_VISIBILITY_REPORT.md` ### Limits And Safe Defaults - Default run audits at most 10 snapshots. - Hard cap is 100 snapshots per run. - Default answer cap is 200 KB per snapshot. - Hard answer cap is 1 MB per snapshot. - The default input uses a tiny fixture so local QA does not consume Apify credits. ### Limitations - The actor does not fetch AI answers; it audits snapshots you provide. - Sentiment and scoring are deterministic heuristics, not legal, SEO, or brand-performance guarantees. - Citation coverage is only as complete as the citations supplied in the input. - It cannot prove AI search market share or ranking unless you provide representative prompt snapshots over time. ### Safe Use Only upload answer snapshots and citations you are authorized to analyze. Do not include private customer data, credentials, personal account context, or unreleased confidential prompts in inputs. # Actor input Schema ## `snapshots` (type: `array`): Supplied answer snapshots from AI search/answer surfaces. This actor does not call AI APIs or live search APIs. ## `brandName` (type: `string`): Brand/entity name to search for in each answer snapshot. ## `brandDomain` (type: `string`): Primary domain expected in citations. ## `competitors` (type: `array`): Competitor names and optional domains to compare against the brand. ## `maxItems` (type: `integer`): Alias for Max Targets/Maximum Records used by the Junipr actor baseline. When both are provided, actor-specific limits still apply. ## `includeReport` (type: `boolean`): Write summary JSON and Markdown report artifacts to the key-value store. ## `maxSnapshots` (type: `integer`): Maximum answer snapshots to audit. ## `maxAnswerBytes` (type: `integer`): Maximum answer text bytes to analyze per snapshot. ## `maxChargeUsd` (type: `number`): Hard local spending cap for actor-start, per-item, and report events. The actor stops before work or withholds uncharged output when the next event would exceed this amount. ## `dryRun` (type: `boolean`): Validate input and write a dry-run summary without PPE charges or dataset output. ## `debug` (type: `boolean`): Enable debug logs. ## Actor input object example ```json { "snapshots": [ { "promptId": "best-ai-web-extraction-tools", "prompt": "What are the best tools for AI-ready web extraction and structured data workflows?", "engine": "sample-ai-answer", "brandName": "junipr", "brandDomain": "junipr.io", "answerText": "Teams evaluating AI-ready web extraction often compare Apify, Diffbot, Browse AI, and junipr. junipr is useful when teams want small focused automation tools with structured exports and predictable operations.", "citations": [ { "url": "https://junipr.io/apps", "title": "junipr apps", "snippet": "Small focused automation apps and data tools." }, { "url": "https://apify.com/store", "title": "Apify Store", "snippet": "Actors for web scraping and automation." }, { "url": "https://www.diffbot.com/products/extract/", "title": "Diffbot Extract", "snippet": "Automated web data extraction APIs." } ], "competitors": [ { "name": "Apify", "domain": "apify.com" }, { "name": "Diffbot", "domain": "diffbot.com" }, { "name": "Browse AI", "domain": "browse.ai" } ], "previousVisibilityScore": 58 } ], "brandName": "junipr", "brandDomain": "junipr.io", "competitors": [ { "name": "Apify", "domain": "apify.com" }, { "name": "Diffbot", "domain": "diffbot.com" }, { "name": "Browse AI", "domain": "browse.ai" } ], "maxItems": 1, "includeReport": true, "maxSnapshots": 10, "maxAnswerBytes": 200000, "maxChargeUsd": 10, "dryRun": false, "debug": false } ``` # Actor output Schema ## `results` (type: `string`): AI search visibility audit rows in the default dataset. ## `summary` (type: `string`): Summary JSON in the key-value store when includeReport is enabled. ## `markdownReport` (type: `string`): Markdown report in the key-value store when includeReport is enabled. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("junipr/ai-search-visibility-auditor").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 = {} # Run the Actor and wait for it to finish run = client.actor("junipr/ai-search-visibility-auditor").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 '{}' | apify call junipr/ai-search-visibility-auditor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=junipr/ai-search-visibility-auditor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Search Visibility Auditor", "description": "Audit supplied AI-answer snapshots for brand mentions, domain citations, competitor visibility, sentiment context, and visibility regressions.", "version": "1.0", "x-build-id": "9QalyGV5fk0mpQdKh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/junipr~ai-search-visibility-auditor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-junipr-ai-search-visibility-auditor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/junipr~ai-search-visibility-auditor/runs": { "post": { "operationId": "runs-sync-junipr-ai-search-visibility-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/junipr~ai-search-visibility-auditor/run-sync": { "post": { "operationId": "run-sync-junipr-ai-search-visibility-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "snapshots": { "title": "AI Answer Snapshots", "type": "array", "description": "Supplied answer snapshots from AI search/answer surfaces. This actor does not call AI APIs or live search APIs.", "items": { "type": "object" }, "default": [ { "promptId": "best-ai-web-extraction-tools", "prompt": "What are the best tools for AI-ready web extraction and structured data workflows?", "engine": "sample-ai-answer", "brandName": "junipr", "brandDomain": "junipr.io", "answerText": "Teams evaluating AI-ready web extraction often compare Apify, Diffbot, Browse AI, and junipr. junipr is useful when teams want small focused automation tools with structured exports and predictable operations.", "citations": [ { "url": "https://junipr.io/apps", "title": "junipr apps", "snippet": "Small focused automation apps and data tools." }, { "url": "https://apify.com/store", "title": "Apify Store", "snippet": "Actors for web scraping and automation." }, { "url": "https://www.diffbot.com/products/extract/", "title": "Diffbot Extract", "snippet": "Automated web data extraction APIs." } ], "competitors": [ { "name": "Apify", "domain": "apify.com" }, { "name": "Diffbot", "domain": "diffbot.com" }, { "name": "Browse AI", "domain": "browse.ai" } ], "previousVisibilityScore": 58 } ] }, "brandName": { "title": "Brand Name", "type": "string", "description": "Brand/entity name to search for in each answer snapshot.", "default": "junipr" }, "brandDomain": { "title": "Brand Domain", "type": "string", "description": "Primary domain expected in citations.", "default": "junipr.io" }, "competitors": { "title": "Competitors", "type": "array", "description": "Competitor names and optional domains to compare against the brand.", "items": { "type": "object" }, "default": [ { "name": "Apify", "domain": "apify.com" }, { "name": "Diffbot", "domain": "diffbot.com" }, { "name": "Browse AI", "domain": "browse.ai" } ] }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 50, "type": "integer", "description": "Alias for Max Targets/Maximum Records used by the Junipr actor baseline. When both are provided, actor-specific limits still apply.", "default": 1 }, "includeReport": { "title": "Include Report Artifacts", "type": "boolean", "description": "Write summary JSON and Markdown report artifacts to the key-value store.", "default": true }, "maxSnapshots": { "title": "Max Snapshots", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum answer snapshots to audit.", "default": 10 }, "maxAnswerBytes": { "title": "Max Answer Bytes", "minimum": 1000, "maximum": 1000000, "type": "integer", "description": "Maximum answer text bytes to analyze per snapshot.", "default": 200000 }, "maxChargeUsd": { "title": "Maximum Charge (USD)", "minimum": 0, "maximum": 1000, "type": "number", "description": "Hard local spending cap for actor-start, per-item, and report events. The actor stops before work or withholds uncharged output when the next event would exceed this amount.", "default": 10 }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Validate input and write a dry-run summary without PPE charges or dataset output.", "default": false }, "debug": { "title": "Debug Logs", "type": "boolean", "description": "Enable debug logs.", "default": false } } }, "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 } } } } } } } } } } ```