Mark M.

𝗔𝗻𝘆𝗼𝗻𝗲 𝗰𝗮𝗻 𝗱𝗲𝗺𝗼 𝗮 𝘀𝗰𝗿𝗮𝗽𝗲𝗿. 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗮𝗿𝘁 𝗶𝘀 𝗼𝗻𝗲 𝘀𝘁𝗶𝗹𝗹 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝘀𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀, 𝗮𝗳𝘁𝗲𝗿 𝘁𝗵𝗲 𝗱𝗲𝗳𝗲𝗻𝘀𝗲𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝘁𝗵𝗿𝗲𝗲 𝘁𝗶𝗺𝗲𝘀. Web scraping and data extraction for sources that fight back, or scale that breaks the tools: blocked, login-walled, scattered across systems, past the point where off-the-shelf stays economical. 𝟱𝟬𝟬𝗠+ 𝗽𝗮𝗴𝗲𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱, 𝗳𝗼𝘂𝗿 𝘆𝗲𝗮𝗿𝘀 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘂𝗽'𝘀 𝗱𝗮𝘁𝗮 𝗯𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗮𝘁 𝟭𝟱𝟬𝗞 𝗶𝘁𝗲𝗺𝘀 𝗮 𝗱𝗮𝘆, 𝟵𝟵% 𝘂𝗽𝘁𝗶𝗺𝗲. 𝗡𝗼 𝗽𝗶𝘃𝗼𝘁𝘀 𝘀𝗶𝗻𝗰𝗲 𝟮𝟬𝟭𝟲. Founders, CTOs, and operators hire me to keep engineers on product, not babysitting scrapers. I build and run these systems end to end: disconnected web and API sources fused into decision-ready data and market intelligence, with LLM judgment and AI automation built in. Classification, entity resolution, calls a regex cannot make. Built in Python. Run for years. No cost spiral. AI handles the easy pages now, and I will tell you when that is all you need. On hard sources, it fails quietly, or worse, confidently wrong. 𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗮𝘁 𝗹𝗶𝗻𝗲 𝘀𝗶𝘁𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗷𝗼𝗯: 𝗔𝗜 𝗳𝗼𝗿 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗮𝘁 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝘀𝗰𝗮𝗹𝗲, 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮𝗰𝗰𝗲𝘀𝘀. I build orchestrated agents with automated tests that catch errors before they reach you. It compounds on every project and delivers the output of a five-person team. ⭐ Top Rated Plus · $700K+ Earned · 100% Job Success Score · 10 Years in Data Acquisition ⭐ 𝗥𝗘𝗖𝗘𝗡𝗧 𝗪𝗢𝗥𝗞 Production systems I delivered recently, and the decisions they power: → 𝗚𝗹𝗼𝗯𝗮𝗹 𝗺𝗮𝗿𝗸𝗲𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. I track surgical-robot installs across dozens of countries and languages for a medical-device intelligence firm: press releases, regulatory filings, clinical announcements. An AI layer makes the judgment a plain scraper can't: real install, or just a demo? They see where the market moves before competitors do. → 𝗟𝗶𝘃𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, 𝗹𝗶𝘃𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁. I deliver the data engine behind a niche job board: nursing roles from multiple ATS platforms (Workday, Greenhouse, Lever), deduped, tagged by specialty and license, published to the client's live site twice a day. His business runs on data it collects for itself. → 𝗠𝘂𝗹𝘁𝗶-𝘀𝗼𝘂𝗿𝗰𝗲 𝗳𝘂𝘀𝗶𝗼𝗻, 𝗿𝗲𝗰𝗲𝗻𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Unmasking LLC owners via Secretary of State filings, cross-referenced with deed records and permits from 8 city systems. Rebuilding a VIN-to-spec layer from manufacturer PDFs, federal safety data, and EPA records. Classifying 27,000 businesses by revenue tier from PPP loans, franchise benchmarks, and geospatial signals. 𝗣𝗥𝗢𝗩𝗘𝗡 𝗔𝗧 𝗦𝗖𝗔𝗟𝗘 My longest engagement: the entire data backbone for a venture-backed AI tutoring startup. 84.5M solutions from Chegg, 128M items from Brainly, 100M+ from Quizlet, CourseHero, and more. $560K contract. All 15+ platforms fought back hard, and I kept the pipelines running for years. Serious AI teams hire me because I speak their language: schema consistency, deduplication, training-set hygiene. 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗔𝗣𝗣𝗥𝗢𝗔𝗖𝗛 Your project moves through three layers: → Access. Cloudflare, DataDome, PerimeterX, CAPTCHAs, login walls, rate limits, JavaScript-heavy apps, undocumented APIs. Python, Playwright, Scrapy, and rotating proxies get past them at scale. → Intelligence. Classifying records, resolving entities, making judgment calls at machine scale. Also RAG-ready corpora and ML training data: multi-source, deduplicated, embeddings-ready. → Reliability. Monitored, tested, still running months later, and delivered in whatever your stack expects: CSV, JSONL, Parquet, a database, or S3/GCS. Sources I have worked: court dockets, business registrations, federal import-tariff filings, Zillow, Trulia, county assessors and permit systems, multi-country marketplaces, 50M+ Instagram profiles, attorney profiles at scale. 𝗪𝗛𝗬 𝗖𝗟𝗜𝗘𝗡𝗧𝗦 𝗦𝗧𝗔𝗬 Before building data systems, I spent five years in private banking at J.P. Morgan. In that world, a dropped ball is not an option, and I bring that exact standard to my engineering. The code is only half of what you get; the rest is absolute reliability. → Fixed price, every time. I quote it, you approve it, I deliver it. No hourly surprises. → Straight answers. If it is not a fit, or there's a cheaper path, you hear it first. → No handoffs, no disappearing. You work with the person who built it, and I fix what breaks. 𝗛𝗢𝗪 𝗪𝗘 𝗦𝗧𝗔𝗥𝗧 Sample first: a proof-of-concept in days so you can check the data before committing, then milestone-based phases delivered into your environment. Send me the target source and the business decision it needs to power. I will give you a candid assessment within 24 hours and a data sample before you commit a single dollar. Mark Mindlin | Ronin Data