Indie sites invisible to AI assistants — diagnostic + fixes

Executive Summary

Many independent websites and small-to-midsize businesses—roughly 2,000,000 SMBs—are becoming effectively invisible to LLM-based assistants because these systems prioritize answerability, clear provenance, and machine-readable metadata, and site owners often lack the technical resources to provide the structured snippets and citation-ready content assistants prefer. This visibility gap disproportionately affects resource-constrained owners (local retailers, niche publishers, service professionals) who historically spend about $3,000/year on SEO and visibility tools but are seeing diminishing returns as discovery shifts away from traditional SERPs. You could build a diagnostic-and-fix platform that crawls sites, produces an “assistant visibility” score across citationability, structured data, answer extraction, and provenance, and then delivers prioritized fixes—JSON‑LD templates, CMS plugins, copy templates for atomic answers, and optional deployable patches—plus simulations that estimate citation likelihood. Offer a tiered model with a one-time deep audit (~$499), subscription monitoring and auto-patching, and integrations for popular CMSes to keep adoption friction low. The timing is attractive: this is a $6.0B addressable market (2,000,000 SMBs × $3,000 average annual spend), with a Market Score of 90/100 and Revenue Potential 85/100, driven by assistant-first discovery, rising provenance demands, and increasing weight on structured data; competition is medium, so a focused, technical offering can capture share but the underlying assistant signals will continue to evolve. To stand out, prioritize measurable signals (a clear “Citationability Score”), tight CMS integrations, privacy-respecting deployment for indie publishers, and reseller partnerships with agencies; be realistic that ongoing R&D to track assistant algorithm changes and proving ROI to cost-sensitive SMBs are the principal challenges.