Detect whether AI assistants mention your brand and where

Many marketing and analytics teams today are blind to when AI assistants mention their brand: as LLMs get embedded into search and workflow tools, brand mentions and answers become untracked touchpoints that create missed attribution, conversion insights, and reputation risk for potentially millions of businesses. This pain is acute for both SMBs and enterprise marketing groups that need reliable measurement across new assistant channels. You could build a SaaS that continuously indexes assistant/model outputs, extracts brand mentions with model- and prompt-level provenance (model version, answer location, sentiment/context), and delivers attribution-ready data via a dashboard plus webhooks and BI/analytics integrations. Priced around the $1.2K ACV implied by the 3.5M-business TAM ($4.2B), the product would combine model-aware parsers, scheduled assistant crawlers or API collectors, and easy export connectors. The timing is attractive: LLM-driven discovery is a nascent attribution channel, marketing budgets are shifting to data-driven measurement, and the market shows strong signals (market/revenue scores ~88/100) with medium competition — an early window exists to capture share. You can differentiate by offering continuous, model-aware indexing and structured provenance rather than keyword-only monitoring, but expect engineering challenges keeping up with evolving and closed assistant APIs, scaling reliable collection, and navigating privacy/compliance constraints.