When agents fail on messy tables — robust table-cleaning + fallback automation

When agents fail on messy tables — robust table-cleaning + fallback automation targets a $3.0B = 1.0M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% YoY — adoption of AI-driven document automation and agent tooling is accelerating (industry reports and vendor growth signals).

Key trends driving demand: LLM adoption — organizations are embedding LLMs into workflows but face reliability issues when inputs are messy, creating demand for pre-processing layers.; Knowledge ops and RAG growth — more companies are building retrieval-augmented generation pipelines, which increases the need for clean structured inputs.; Human-in-the-loop acceptance — enterprises prefer hybrid flows where humans correct edge cases, so products enabling quick corrections gain trust and adoption.; API and connector standardization — growing ecosystem of integrations (Confluence, Notion, Google Workspace) makes it practical to insert specialized processing layers into existing stacks..