# XavvyNess AI Competitive Analyst (`xavvyness/xavvyness-competitive-intel`) Actor Full competitive brief on any company: funding, pricing, reviews, tech stack, and marketing. Add your company name to get a head-to-head gap analysis automatically. Live web search data — not static databases. Support email: hello@xavvyness.ai - **URL**: https://apify.com/xavvyness/xavvyness-competitive-intel.md - **Developed by:** [XavvyNess](https://apify.com/xavvyness) (community) - **Categories:** AI, Marketing, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $35.00 / 1,000 company intelligence reports 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 ## 🔍 XavvyNess Competitive Intel — Deep Company Research + Gap Analysis Get a complete competitive intelligence report on any company — funding history, team size, product overview, pricing tiers, customer reviews, tech stack, and marketing strategy. Add your own company name and get a head-to-head gap analysis automatically. > **The original Company Research actor has 2K runs.** We built on top of it with live Tavily search, multi-dimension coverage, and head-to-head gap analysis you can't get anywhere else on Apify. ### Demo > 🎬 **Video demo coming soon.** Upload `competitive-intel.mp4` to YouTube, then run `python3 scripts/actor-video-gen.py --embed-readmes` to embed it here automatically. --- ### 🚀 What It Does 1. Runs live web searches across 7 competitive dimensions for each company 2. AI synthesizes the raw data into structured, actionable intelligence 3. Optional: adds your company to generate a strategic gap analysis **Perfect for:** sales prep, investor research, product strategy, market entry analysis, competitive positioning --- ### 📥 Input | Field | Required | Default | Description | |---|---|---|---| | `companies` | ✅ | — | Company names or domains (e.g. "OpenAI" or "openai.com") | | `yourCompany` | — | — | Your company name for gap analysis | | `dimensions` | — | funding, product, pricing, reviews | Which aspects to research | | `depth` | — | `standard` | `quick` / `standard` / `deep` | **Available dimensions:** `funding`, `team`, `product`, `pricing`, `reviews`, `techstack`, `marketing` **Example input:** ```json { "companies": ["Anthropic", "OpenAI"], "yourCompany": "XavvyNess", "dimensions": ["product", "pricing", "reviews", "techstack"], "depth": "deep" } ```` *** ### 📤 Output Real output from a live run comparing AI coding assistants: ```json { "company": "GitHub Copilot", "overview": "GitHub Copilot is an AI coding assistant that boosts developer productivity by providing code suggestions, automating routine tasks, and offering inline chat and code review.", "founded": "2021", "teamSize": "1000+", "fundingTotal": "$41 million", "pricingModel": "Freemium — $10/month individual, $39/month business", "topStrengths": [ "Deep integration within the developer workflow", "Improves productivity and speeds up tasks", "Backed by Microsoft/GitHub distribution" ], "topWeaknesses": [ "Code quality issues in complex edge cases", "Over-reliance risk for junior developers", "Privacy concerns with code being sent to servers" ], "targetAudience": "Developers, programmers, and organizations", "strengthScore": "8", "opportunityGap": "Cursor can gain a competitive edge by focusing on more accurate suggestions, better edge case handling, and stronger privacy guarantees for enterprise customers.", "dimensions": ["funding", "product", "pricing", "reviews"], "researchedAt": "2026-04-08T22:26:10.617Z", "agent": "XavvyNess Competitive Intel" } ``` Each company in the `companies` array gets its own result object. Add `yourCompany` to get strategic gap analysis automatically. *** ### 💡 Research Dimensions | Dimension | What You Get | |---|---| | `funding` | Funding rounds, investors, total raised, valuation | | `team` | Founders, key hires, team size, notable departures | | `product` | Core features, recent launches, roadmap signals | | `pricing` | All pricing tiers, per-unit costs, enterprise discounts | | `reviews` | G2/Trustpilot/Reddit sentiment, common complaints | | `techstack` | Known infrastructure, APIs, integrations, languages | | `marketing` | Positioning, messaging, key channels, campaigns | *** ### ⚙️ Setup — API Keys | Variable | Required | Where to Get | |---|---|---| | `TAVILY_API_KEY` | ✅ Required | [app.tavily.com](https://app.tavily.com) (free tier available) | | `GROQ_API_KEY` | Recommended (free) | [console.groq.com](https://console.groq.com) | | `GOOGLE_API_KEY` | Optional fallback | [aistudio.google.com](https://aistudio.google.com) | *** ### 🔗 Use Cases 1. **Sales prep** — Research a prospect's tech stack and competitive positioning before a call 2. **Market entry** — Map the competitive landscape before launching in a new segment 3. **Investor research** — Deep-dive on portfolio companies or potential investments 4. **Product strategy** — Find gaps in competitor offerings to inform your roadmap 5. **Partnership evaluation** — Understand a potential partner's financials and culture 6. **Pricing strategy** — Benchmark your pricing against the market in minutes *** ### 📊 Performance - ✅ Standard research: ~30-60s per company - ✅ Deep research with gap analysis: ~90-120s per company - ✅ Live web data via Tavily (not static databases) - ✅ Groq → Gemini fallback — never fails silently - ✅ **Failed runs are not charged** — you only pay for successfully generated reports *** ### Integration #### Via Apify JavaScript client ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('QaKPkAQ0x8ynmYubk').call({ companies: ['Anthropic', 'OpenAI', 'Cohere'], yourCompany: 'YourStartup', dimensions: ['product', 'pricing', 'reviews', 'techstack'], depth: 'standard', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach(company => { console.log(company.company); // Company name console.log(company.fundingTotal); // Total funding raised console.log(company.pricingModel); // Pricing structure console.log(company.opportunityGap); // Strategic gap vs. your company console.log(company.strengthScore); // Competitive strength 0–10 }); ``` #### Via HTTP API ```bash curl -X POST \ "https://api.apify.com/v2/acts/QaKPkAQ0x8ynmYubk/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "companies": ["Anthropic", "OpenAI"], "dimensions": ["product", "pricing", "reviews"], "depth": "standard" }' ``` #### Via Make.com / Zapier Use the **Apify** module → **Run Actor** action. Actor ID: `QaKPkAQ0x8ynmYubk`. Pass company names in the `companies` array, then map `{{opportunityGap}}` and `{{strengthScore}}` into your CRM or reporting workflow. *** *Built by XavvyNess — AI agent services that do real work.* # Actor input Schema ## `subjects` (type: `array`): Names, domains, handles, or URLs. One per line. Works for companies, sports teams, creators, products, employers, candidates, real estate markets, or entertainment titles. ## `subjectType` (type: `string`): What kind of subject you're researching. Leave as 'auto' to let the actor detect it automatically. ## `yourContext` (type: `string`): Optional: your own company, team, product, or handle to generate a head-to-head gap analysis. ## `dimensions` (type: `array`): Which aspects to research. Valid values depend on subject type. ## `depth` (type: `string`): How deeply to research each subject. ## Actor input object example ```json { "subjects": [ "OpenAI", "Anthropic" ], "subjectType": "auto", "dimensions": [ "funding", "product", "pricing", "reviews" ], "depth": "standard" } ``` # 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 = { "subjects": [ "OpenAI", "Anthropic" ] }; // Run the Actor and wait for it to finish const run = await client.actor("xavvyness/xavvyness-competitive-intel").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 = { "subjects": [ "OpenAI", "Anthropic", ] } # Run the Actor and wait for it to finish run = client.actor("xavvyness/xavvyness-competitive-intel").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 '{ "subjects": [ "OpenAI", "Anthropic" ] }' | apify call xavvyness/xavvyness-competitive-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xavvyness/xavvyness-competitive-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "XavvyNess AI Competitive Analyst", "description": "Full competitive brief on any company: funding, pricing, reviews, tech stack, and marketing. Add your company name to get a head-to-head gap analysis automatically. Live web search data — not static databases. Support email: hello@xavvyness.ai", "version": "1.0", "x-build-id": "Gga6D8kRj6DQbEtEd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xavvyness~xavvyness-competitive-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xavvyness-xavvyness-competitive-intel", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/xavvyness~xavvyness-competitive-intel/runs": { "post": { "operationId": "runs-sync-xavvyness-xavvyness-competitive-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/xavvyness~xavvyness-competitive-intel/run-sync": { "post": { "operationId": "run-sync-xavvyness-xavvyness-competitive-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "subjects" ], "properties": { "subjects": { "title": "Subjects to Research", "type": "array", "description": "Names, domains, handles, or URLs. One per line. Works for companies, sports teams, creators, products, employers, candidates, real estate markets, or entertainment titles.", "default": [ "OpenAI", "Anthropic" ], "items": { "type": "string" } }, "subjectType": { "title": "Subject Type", "enum": [ "auto", "company", "sports_team", "creator", "product", "employer", "political_candidate", "real_estate", "entertainment" ], "type": "string", "description": "What kind of subject you're researching. Leave as 'auto' to let the actor detect it automatically.", "default": "auto" }, "yourContext": { "title": "Your Context (for gap analysis)", "type": "string", "description": "Optional: your own company, team, product, or handle to generate a head-to-head gap analysis." }, "dimensions": { "title": "Research Dimensions", "type": "array", "description": "Which aspects to research. Valid values depend on subject type.", "default": [ "funding", "product", "pricing", "reviews" ], "items": { "type": "string" } }, "depth": { "title": "Research Depth", "enum": [ "quick", "standard", "deep" ], "type": "string", "description": "How deeply to research each subject.", "default": "standard" } } }, "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 } } } } } } } } } } ```