On-site B2B feedback & NPS as a lightweight drop-in embed

Product and support teams at roughly 1,200,000 product-led companies struggle to collect contextual in-app feedback and reliable NPS without introducing third-party trackers or bloating page weight, which leads to low response rates, fragmented signals, and compliance risk. These teams need lightweight, first-party capture that ties responses to product context and routes high-signal items into prioritized tickets rather than a noisy inbox. You could build a drop-in B2B embed engineered for sub-5KB gzip payloads that captures NPS and free-text feedback as first-party data, offers privacy-preserving consent flows, and runs AI-enabled summarization (edge or customer-keyed inference) to output sentiment, topic extraction, and autogenerated tickets for Jira/Slack. With a plausible $10,000 ACV and an addressable base of 1.2M companies, the market maps to a $12.0B TAM, which aligns with the market score of 90/100 and revenue potential of 88/100 noted above. This moment favors the idea because product teams increasingly prioritize performance and first-party data to meet privacy laws, and AI summarization materially reduces the triage burden so customers see ROI quickly. To stand out you must be rigorous about minimizing payload, providing clear provenance and explainability on AI outputs, and offering excellent developer DX and integrations; challenges include a medium-competition landscape, the engineering cost of reliable edge/secure inference, and proving incremental conversion or retention lift through case studies.