AI-First PE/VC Research & Data Automation Specialist

Fixed-price pilot | Potential recurring production assignments We are seeking an experienced AI automation specialist to build accurate, source-backed datasets covering private equity and venture capital firms, portfolio companies, ownership relationships, and relevant executives. This is an AI-first automation assignment, not a manual research or data-entry project. The objective is to establish a repeatable, economical production process that can be reused across additional datasets and industries. What you'll build - Automated discovery of PE/VC investment firms and portfolio companies. - AI-powered research of ownership relationships, investment activity, and company intelligence. - Source-backed classification of current holdings, historical investments, and realized exits. - Automated executive discovery and business-email verification. - Structured datasets with source references, confidence levels, and last-checked dates. - Reusable workflows with documented prompts, APIs, provider configurations, QA rules, and exception handling. Required capabilities You should have demonstrated experience with: - OpenAI APIs, web research, structured outputs, and AI-assisted extraction. - Python, Clay, n8n, Apify, or comparable orchestration tools. - Economical API integrations, caching, provider waterfalls, and deduplication. - Automated data validation and selective human QA. - PE/VC research sources, including SEC EDGAR, investment-firm portfolio websites, and transaction announcements. Operating principles We expect an AI-first approach in which routine research and enrichment are automated. Manual effort should be concentrated on initial setup, calibration, QA sampling, and genuinely unresolved exceptions. We require transparent accounting of labor hours and data/API expenses, including actual provider usage. As workflows are reused across additional datasets, we expect measurable reductions in incremental labor requirements. Accuracy, source integrity, repeatability, and production economics are equally important. Initial engagement We will begin with a small, paid, fixed-price qualification assignment to evaluate the proposed architecture, source quality, production economics, and documentation. Successful candidates may be considered for recurring assignments. To apply, please explain: 1) How you would automate discovery and verification of PE/VC portfolio relationships. 2) Which AI models, public sources, and APIs you would use. 3) How you would distinguish current investments from historical or realized holdings. 4) How you measure research accuracy and production cost. 5) What parts of your existing architecture could be reused across industries. Please do not apply if your proposed approach relies primarily on manual research or data entry.