Automated cleanup for repeated words and duplicated phrases

Repeated words and duplicated phrases are a persistent, low-signal/high-cost editing problem that undermines clarity and perceived quality across documentation, marketing, and customer-facing content; it disproportionately burdens knowledge workers—an addressable set of roughly 120 million writers and editors. Most organizations still rely on manual review or brittle regex-based tools, turning what can be a 1–10 minute fix per document into a recurring productivity tax that scales with content volume. You could build an API-first cleanup engine plus editor and CMS plugins that use transformer-based models to detect contextual repetitions, suggest precise fixes, and batch-clean large corpora while avoiding the false positives of simpler tools. Core features would include near-real-time inline suggestions for editors, bulk scanning for CMSs, custom style guides per team, and enterprise privacy controls; target >95% precision on duplicated-phrase detection and under 200 ms inference for interactive use. Monetization is straightforward: a $50/year seat model or per-GB batch pricing maps to a $6.0B TAM (120M users × $50/yr) and aligns with existing spend on writing-quality tooling. This market is attractive now because transformer improvements reduce false positives, content volumes are exploding, and editors/CMSs increasingly accept third-party plugins—trends that support the provided Market Score (92/100) and Revenue Potential (84/100). To stand out you must be pragmatic: deliver measurable precision gains over medium-competition incumbents, ship seamless integrations (Google Docs, Word, WordPress, VS Code), and address privacy and multilingual challenges up front, while recognizing that linguistic ambiguity and edge cases will be the primary operational hurdles.