Audit whether your brand is being cited by AI search (citation-gap fix)

Many brands no longer know whether AI search engines and large language models are citing them when converting consumer queries into concise buyer answers, and that uncertainty is especially acute for local and resource-constrained businesses. Across an addressable set of roughly 20 million SMBs that currently invest in digital and reputation tooling, the consequence is missed discovery, misattributed trust signals, and unmeasured reputation leakage. You could build an "AI citation audit" product that samples LLM outputs and their visible citation chains, maps brand mentions across forums, reviews and knowledge panels, scores a company's citation-gap, and delivers prioritized remediation playbooks (forum seeding, structured data, review prioritization) through a dashboard and API. Core features would include continuous monitoring, simulated prompt probes against major models, integrations with reputation platforms, and an ROI-driven action list that supports an average contract value near $1,200/year for SMB customers. This is timely because AI-search convergence and the shift from page-first to mention-first ranking mean traditional SEO audits miss these signals, and the addressable market is large—$24.0B with a market score of 92/100 and revenue potential of 84/100. To stand out you need proprietary signal collection (prompt libraries and real-time citation scraping), tight partnerships with review and forum platforms, and a pragmatic ROI framework so customers can operationalize fixes without a large content program. Be honest about challenges: model behavior variability, noisy attribution, and a medium competitive landscape that requires ongoing R&D and channel partnerships; if you can reliably demonstrate measurable lift in AI-derived traffic and conversions, the unit economics and clear SMB pricing path make this a defensible opportunity worth piloting.