Avoid anti-bot blocks — a pipeline to extract 29 structured G2 review fields

Many software, product and competitive-intelligence teams struggle to keep reliable pipelines feeding their analytics and ML workflows with G2 review data because anti-bot defenses, CAPTCHAs and shifting HTML cause brittle scrapers and constant break-fix work that diverts engineering time from product differentiation. These teams number in the order of 100,000 potential buyers (enterprise and mid-market product/competitive intelligence orgs), and they value repeatable, structured review data more than raw HTML dumps. You could build an API-first, monitored ingestion pipeline that continuously extracts a fixed schema of 29 structured G2 review fields (rating, title, body, pros/cons, role, company size, verified-buyer flag, sentiment, feature mentions, upvotes, review date, product version, etc.), normalizes and deduplicates records, and delivers them into warehouses (S3, BigQuery, Snowflake) with lineage metadata and quality scores. The market is attractive now because review-driven buying and the rising demand for labeled review datasets to fine-tune LLMs have increased the value of high-quality review data, and with a $4.0B addressable market framed as roughly 100,000 teams at a $40K ACV the commercial runway is clear. This idea’s strengths are a defensible operational playbook (residential proxies, headless browser orchestration, human-in-the-loop captcha resolution as needed), a persistent 29-field schema that enables downstream NLU and summarization use cases, and enterprise-grade integrations and SLAs that justify $30K–$60K ACVs. Real challenges are material and ongoing: upstream anti-bot changes force continuous engineering and proxy costs, there are legal/terms-of-service and privacy considerations that require compliance and counsel, and selling to enterprise buyers will require a customer-success and ops organization to sustain the service.