Capture buyer intent by tracking recommendation requests across communities

Many mid-market and enterprise B2B sellers struggle to find high-quality, purchase-ready prospects because buyers increasingly ask peers in public and private communities before engaging vendors, and those recommendation requests are fragmented across forums, Slack channels, Reddit, and LinkedIn groups. These early, concentrated intent signals are routinely buried in threads or behind access controls, so sellers miss timely, pre-qualified opportunities. You could build a platform that continuously tracks and classifies recommendation requests across public and opt-in private communities, using LLM-powered intent classification and semantic matching to map requests to specific seller offerings and surface real-time leads with contextual summaries. Productized CRM integrations, human-in-the-loop verification to reduce false positives, privacy-consent capture for contactable leads, and a white-glove services tier would support a $20K ACV go-to-market model. The market is attractive now because it maps to a roughly $30B addressable opportunity (1.5M mid-market and enterprise sellers x $20K ACV), buyer behavior is shifting to community-first discovery, and buyers/vendors are paying premiums for higher-quality intent signals that shorten sales cycles (Market Score 92, Revenue Potential 86). Advances in NLP/LLMs make accurate intent classification and summarization materially cheaper, but execution risk centers on data access, platform partnerships, legal/privacy compliance, and noisy signals. To stand out you must prioritize precision over volume—targeting north of ~75% verified intent accuracy—combine broad lawful coverage with opt-in flows and human verification, and sell a platform-plus-services motion that justifies premium pricing; these strengths are promising but will require disciplined engineering, legal, and GTM work to realize.