# AI Visibility Report — ChatGPT, Perplexity + Local Competitors (`moonlings/local-business-competitor-analysis`) Actor Why is AI not recommending your business? AI brand visibility audit for local businesses: live ChatGPT & Perplexity appearance counts (AEO/GEO ground truth) plus the competitor, Google-review, and ads evidence behind the answer — from a quick scan to a full night report. - **URL**: https://apify.com/moonlings/local-business-competitor-analysis.md - **Developed by:** [moonlings](https://apify.com/moonlings) (community) - **Categories:** AI, Lead generation, SEO tools - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1,990.00 / 1,000 quick competitor scans 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 Visibility Report — ChatGPT, Perplexity & Local Competitors AI search is already sending customers somewhere — this report tells you where, and why. It asks ChatGPT and Perplexity live whether they recommend a business, then digs into the evidence behind the answer: the strongest local competitor, the review gap, ads presence, and the sites the AI reads. The quick scan is **$1.99** (~2–4 minutes); the full graded research report is **$9.99** (typically 10–30 minutes). Visibility is reported as real appearance counts — "appeared in 2 of 6 answers, ChatGPT 1/3, Perplexity 1/3" — never an invented rating. ### Two depths, one engine #### Quick scan — $1.99, ~2–4 minutes Live AI-visibility check (both engines, real counts, who's recommended instead, cited domains) plus an AI research agent that identifies the business's strongest local rival and scouts it: review numbers, offers, recent changes — every finding with a working source URL — and concrete recommended actions. #### Deep research report — $9.99, typically 10–30 minutes A multi-agent research crew sweeps the full picture, and a grader iterates the report until it passes a quality rubric: - AI-assistant visibility with per-engine appearance counts - Competitor landscape with reviews/ratings comparison - Google Business Profile and local-ads observations - Local events and demand signals worth acting on - **Structured findings JSON** (`findings[]` with `key`, `finding`, `evidence`, `sourceUrl`) plus every cited source, deduped - Top recommended actions with effort labels - The complete report as markdown (`reportMarkdown`) ### Sample output (quick scan, abridged — real run) ```json { "reportType": "scan", "businessName": "Graeter's Ice Cream", "location": "Cincinnati, OH", "aiVisibility": { "asked": 6, "sampled": 6, "appeared": 2, "engines": [ { "engine": "ChatGPT", "sampled": 3, "appeared": 1 }, { "engine": "Perplexity", "sampled": 3, "appeared": 1 } ], "recommendedInstead": ["Aglamesis Bro's Ice Cream", "Hello Honey"], "citedDomains": ["cincinnatirefined.com", "365cincinnati.com"] }, "findings": [ { "label": "Review volume gap", "value": "Graeter's (Vine St) 4.6★ / 206 reviews vs Aglamesis Bro's 4.7★ / 1,300 — out-reviewed 6:1", "sourceUrl": "https://..." } ], "actions": [ { "title": "Close the review gap", "body": "...", "tag": "do this" } ] } ```` ### Who uses this - **Agencies**: the deep report is a client-ready deliverable — the same research a retainer buys, priced per report. - **Owners**: one scan answers "does AI recommend us, and if not, who?" - **AI agents**: callable via the Apify MCP server. The scan fits a normal tool-call wait; for the deep report, start the run and poll — results land in the dataset. ### Input | Field | Required | Notes | |---|---|---| | `businessName` | yes | Spell it the way Google knows it | | `location` | yes | City + state/region, e.g. "Cincinnati, OH" | | `businessType` | recommended | Category anchor — enables the AI questions and competitor ground truth | | `reportType` | — | `scan` (default) or `deep_report` | | `website` | — | Deep report: grounds findings in your own site | | `researchFocus` | — | Deep report: optional question to focus on | ### Pricing Pay per delivered report: one `scan-report` event ($1.99) or one `deep-report` event ($9.99), charged only when the result is pushed to the dataset. **Failed runs are never charged.** Deep-report retries are idempotent — a resurrected run resumes the same report rather than buying a second one. ### Not what this does - No 0–100 ratings, grades, or invented indexes — appearance counts and sourced findings only. - Not a bulk scraper: one business in, one researched report out. For scraping thousands of listings, use a Maps scraper. - Not instant: the deep report is real multi-agent research with a quality grader — it takes the minutes it takes. - Not for businesses without a local market (the analysis is category + city anchored). ### Quick single checks Need one number fast? [AI Visibility Check](https://apify.com/moonlings/ai-visibility-check) ($0.79) answers "does ChatGPT recommend us?", [Review Gap Check](https://apify.com/moonlings/review-gap-check) ($0.39) measures your Google review gap vs rivals, and [Thumbtack Scraper](https://apify.com/moonlings/thumbtack-scraper) pulls the local-pro competitive field — ratings, hires, and reviews by service and city. The [AI Fact-Check](https://apify.com/moonlings/ai-fact-check) checks whether AI assistants state a business’s hours/address/phone correctly. ### For AI agents (MCP) Agents can run this report directly: add Apify's MCP server as a connector — `https://mcp.apify.com/?actors=moonlings/local-business-competitor-analysis` — and it appears as the `moonlings--local-business-competitor-analysis` tool, metered through your Apify account like any other run. The `scan` report type fits inside a long tool call (2–4 minutes); for `deep`, start the run and poll it with the MCP server's run-status tool rather than blocking. **Long runs don't strand you.** Call the tool with `waitSecs=0` to get a `runId` back immediately, then collect the result later with the MCP server's `get-actor-run` tool. The run lives on Apify's platform independent of your MCP session — an agent can kick off a deep report, end its session, and a different session an hour later can still pick up the finished report. ### Deliver the report to your tools (MCP connectors) This Actor accepts [MCP connectors](https://docs.apify.com/integrations/mcp-connectors): authorize your Slack, Notion, or any MCP-compatible service once under **Settings → API & Integrations**, pick it in the **Deliver results to your tools** input section, and the finished report summary is posted straight into your workspace — top findings, recommended actions, and a pointer to the full dataset. Scheduled weekly scan + summary in the client's Slack channel is the agency workflow this was built for. Your credentials never enter this Actor: the Apify platform injects them server-side, and the Actor is limited to output-writing tools (`send_*` / `post_*` / `create_*` / `write_*` / `add_*`). If the tool needs a destination (a Slack channel, a Notion parent page), set **Delivery target**. Delivery is best-effort — the report always lands in the run's dataset, and a delivery hiccup never fails the run or changes what you're charged. ### FAQ **Which businesses does it work for?** Any business with a local market — gyms, salons, restaurants, contractors, med spas, retail. **Where does the data come from?** Live research at run time: AI assistants with web search, public listings, review platforms, the business's own website, and ads surfaces. Findings carry source URLs so you can verify. ### Who this is for Marketing agencies, local SEO / AEO consultants, and owners who want the full picture in one run: **AI brand visibility audit** (ChatGPT & Perplexity, live web search), **local competitor analysis**, Google review benchmarking, and competitor ad intelligence — the evidence layer behind "why isn't AI recommending my business?" Suitable as a white-label report input. ### Works with AI agents Agent-callable via the Apify API or MCP, and **x402-payable** — an autonomous agent can buy a scan or a full night report with no account. Returns structured findings JSON alongside the readable report. # Actor input Schema ## `businessName` (type: `string`): The local business to analyze, e.g. "Harbor Fitness". The analysis finds its strongest nearby competitor and reports the gaps. ## `location` (type: `string`): City and state/region, e.g. "Cincinnati, OH". Anchors competitor discovery and AI-visibility sampling to the business's real market. ## `businessType` (type: `string`): Category, e.g. "gym", "salon", "HVAC contractor". Strongly recommended: the AI assistants are asked category questions, and competitor ground truth is category-anchored — without it both are skipped. ## `address` (type: `string`): For chains / multi-location businesses: the street address of the SPECIFIC location to anchor, e.g. "332 Ludlow Ave". Without it, the best-rated matching listing is used. ## `reportType` (type: `string`): "scan" (about 2–4 minutes): live AI-visibility check (ChatGPT + Perplexity appearance counts) plus a strongest-competitor scan with sourced findings and recommended actions. "deep\_report" (typically 10–30 minutes): the full multi-agent research report — AI visibility, competitors, reviews, ads, local demand — quality-graded and revised until it passes, with structured findings JSON and the full markdown report. ## `website` (type: `string`): Optional. Grounds the deep report in the business's own services, pricing, and positioning, and upgrades the ads check to a domain-level lookup. ## `researchFocus` (type: `string`): Optional focus for the deep report, e.g. "we're losing members to boutique studios — why?". The crew covers the full competitive picture either way. ## `deliveryConnectors` (type: `array`): Send the finished report summary to your own tools — a Slack channel, Notion page, or any MCP server with an output-writing tool. Authorize a connector once in Settings > API & Integrations, then pick it here. Your credentials never enter this Actor; the Apify platform injects them server-side. ## `deliveryTarget` (type: `string`): Where inside the connected service to deliver, when the tool needs it — e.g. a Slack channel ("#marketing" or a channel ID), a Notion parent page/database ID. Leave blank for tools that don't require a destination. ## `deliveryTool` (type: `string`): Advanced: the exact MCP tool to call on the connector (e.g. "send\_message", "create\_page"). Leave blank to auto-pick the first output-writing tool the connector offers. ## Actor input object example ```json { "businessName": "Harbor Fitness", "location": "Cincinnati, OH", "businessType": "gym", "address": "332 Ludlow Ave", "reportType": "scan", "website": "https://harborfitness.com", "deliveryTarget": "#marketing", "deliveryTool": "send_message" } ``` # Actor output Schema ## `results` (type: `string`): The delivered report as structured JSON — findings with evidence and source URLs, recommended actions, and AI-assistant visibility. ## `run` (type: `string`): Console view of this run, including the charged event and log. # 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 = { "businessName": "Graeter's Ice Cream", "location": "Cincinnati, OH", "businessType": "ice cream shop", "reportType": "scan" }; // Run the Actor and wait for it to finish const run = await client.actor("moonlings/local-business-competitor-analysis").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 = { "businessName": "Graeter's Ice Cream", "location": "Cincinnati, OH", "businessType": "ice cream shop", "reportType": "scan", } # Run the Actor and wait for it to finish run = client.actor("moonlings/local-business-competitor-analysis").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 '{ "businessName": "Graeter'\''s Ice Cream", "location": "Cincinnati, OH", "businessType": "ice cream shop", "reportType": "scan" }' | apify call moonlings/local-business-competitor-analysis --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moonlings/local-business-competitor-analysis", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Visibility Report — ChatGPT, Perplexity + Local Competitors", "description": "Why is AI not recommending your business? AI brand visibility audit for local businesses: live ChatGPT & Perplexity appearance counts (AEO/GEO ground truth) plus the competitor, Google-review, and ads evidence behind the answer — from a quick scan to a full night report.", "version": "0.1", "x-build-id": "bhg2ceJADnwZJVodD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moonlings~local-business-competitor-analysis/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moonlings-local-business-competitor-analysis", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moonlings~local-business-competitor-analysis/runs": { "post": { "operationId": "runs-sync-moonlings-local-business-competitor-analysis", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moonlings~local-business-competitor-analysis/run-sync": { "post": { "operationId": "run-sync-moonlings-local-business-competitor-analysis", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "businessName", "location" ], "properties": { "businessName": { "title": "Business name", "type": "string", "description": "The local business to analyze, e.g. \"Harbor Fitness\". The analysis finds its strongest nearby competitor and reports the gaps." }, "location": { "title": "Location", "type": "string", "description": "City and state/region, e.g. \"Cincinnati, OH\". Anchors competitor discovery and AI-visibility sampling to the business's real market." }, "businessType": { "title": "Business type", "type": "string", "description": "Category, e.g. \"gym\", \"salon\", \"HVAC contractor\". Strongly recommended: the AI assistants are asked category questions, and competitor ground truth is category-anchored — without it both are skipped." }, "address": { "title": "Location address (optional)", "type": "string", "description": "For chains / multi-location businesses: the street address of the SPECIFIC location to anchor, e.g. \"332 Ludlow Ave\". Without it, the best-rated matching listing is used." }, "reportType": { "title": "Report depth", "enum": [ "scan", "deep_report" ], "type": "string", "description": "\"scan\" (about 2–4 minutes): live AI-visibility check (ChatGPT + Perplexity appearance counts) plus a strongest-competitor scan with sourced findings and recommended actions. \"deep_report\" (typically 10–30 minutes): the full multi-agent research report — AI visibility, competitors, reviews, ads, local demand — quality-graded and revised until it passes, with structured findings JSON and the full markdown report.", "default": "scan" }, "website": { "title": "Business website (deep report only)", "type": "string", "description": "Optional. Grounds the deep report in the business's own services, pricing, and positioning, and upgrades the ads check to a domain-level lookup." }, "researchFocus": { "title": "Research focus (deep report only)", "type": "string", "description": "Optional focus for the deep report, e.g. \"we're losing members to boutique studios — why?\". The crew covers the full competitive picture either way." }, "deliveryConnectors": { "title": "Deliver results via MCP connectors (optional)", "type": "array", "description": "Send the finished report summary to your own tools — a Slack channel, Notion page, or any MCP server with an output-writing tool. Authorize a connector once in Settings > API & Integrations, then pick it here. Your credentials never enter this Actor; the Apify platform injects them server-side." }, "deliveryTarget": { "title": "Delivery target (optional)", "type": "string", "description": "Where inside the connected service to deliver, when the tool needs it — e.g. a Slack channel (\"#marketing\" or a channel ID), a Notion parent page/database ID. Leave blank for tools that don't require a destination." }, "deliveryTool": { "title": "Delivery tool name (optional)", "type": "string", "description": "Advanced: the exact MCP tool to call on the connector (e.g. \"send_message\", \"create_page\"). Leave blank to auto-pick the first output-writing tool the connector offers." } } }, "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 } } } } } } } } } } ```