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"prompt": "# LinkedIn Company Analysis Prompt\n\nExtract and analyze the following information from the provided LinkedIn company page. Present the results in a structured JSON format.\n\n## Required Data Points\n\n### 1. Company Identity\n- Full company name (including suffixes like Inc., LLC, etc.)\n- Brand tagline\/headline (directly under company name)\n- Global headquarters location \n- Company description (full \"About\" section text)\n- Primary website URL (excluding social media links)\n\n### 2. Company Scale\n- Current employee count (from LinkedIn \"X employees\" metric)\n- Employee range bracket: [0-9], [10-150], [150+]\n\n### 3. Business Classification\nEvaluate the following characteristics based on company description, recent posts, and featured content:\n\n#### Automation Agency Status\n- Boolean (true\/false) classification\n- Criteria for \"true\":\n * Company explicitly offers automation services to clients\n * Core business model involves developing\/implementing automations\n * Primary revenue from automation consulting\/development\n\n#### AI Implementation Level\nClassify as [Low\/Medium\/High] based on:\n- Low: No evidence of AI\/automation\/scraping usage\n- Medium: Uses AI\/automation tools or mentions them as supplementary capabilities\n- High: Core business involves AI development, automation creation, or data harvesting services\n\n### 4. Technical Sophistication\nEvaluate overall technical capabilities as [Basic\/Intermediate\/Advanced\/Expert] based on:\n- Technology stack mentioned\n- Technical job postings\n- Products\/services complexity\n- Engineering team size\n- Technical achievements highlighted\n\n### 5. Investment Profile\nIf available, document:\n- Most recent funding round\n- Total funding amount\n- Key investors\n- Last funding date\nMark as \"Not publicly disclosed\" if information unavailable\n\n### 6. ICP (Ideal Customer Profile) Score\nCalculate composite score using this weighted criteria:\n\n| Category | Criteria | Points |\n|----------|----------|--------|\n| AI Focus | Low | 5 |\n| | Medium | 10 |\n| | High | 25 |\n| Technical Level | Basic | 5 |\n| | Intermediate | 15 |\n| | Advanced | 25 |\n| | Expert | 35 |\n| Employee Count | 0-9 | 5 |\n| | 10-150 | 25 |\n| | 150+ | 30 |\n| Agency Status | Automation Agency | 20 |\n| Geography | US\/Europe Based | 10 |\n\n## Output Format\nReturn data in the following JSON structure, with all fields required:\n\n```json\n{\n \"company_profile\": {\n \"name\": string,\n \"tagline\": string,\n \"location\": {\n \"city\": string,\n \"state\": string,\n \"country\": string\n },\n \"overview\": string,\n \"website\": string\n },\n \"scale\": {\n \"employee_count\": number,\n \"size_bracket\": string\n },\n \"classification\": {\n \"is_automation_agency\": boolean,\n \"ai_focus_level\": string,\n \"technical_tier\": string\n },\n \"funding\": {\n \"latest_round\": string,\n \"total_raised\": string,\n \"investors\": [string],\n \"last_updated\": string\n },\n \"icp_scoring\": {\n \"ai_focus_points\": number,\n \"technical_points\": number,\n \"size_points\": number,\n \"agency_points\": number,\n \"geography_points\": number,\n \"total_score\": number\n }\n}\n",
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