Flatten nested JSON into CSV for spreadsheets — schema-first auto-mapping

Many analytics teams, product managers, and spreadsheet power users at mid-market and enterprise companies struggle routinely to transform deeply nested JSON responses into flat CSVs that work in Excel or Google Sheets; the typical options are brittle hand-written scripts, expensive ETL jobs, or one-off manual reshaping that wastes engineering time. This pain is especially acute for teams integrating dozens of API-first services where each endpoint can return heterogeneous arrays and optional fields, and it affects both technical and non-technical users who need tabular outputs for BI and reporting. A viable product is a schema-first, auto-mapping tool that ingests JSON samples or OpenAPI schemas and produces repeatable, editable CSV extraction templates with previewing, array-unwinding controls, and in-spreadsheet connectors. Built with client-side execution (WASM/browser) and a low-code UI, it would let non-engineers apply transformations without data exfiltration while enabling engineers to manage templates programmatically; with 200,000 target mid-market/enterprise orgs and an addressable spend of roughly $6.0B (200k x $30K ACV), the market timing looks strong, reflected in a market score of 74/100 and revenue potential of 94/100. To stand out, focus on predictable, schema-driven mappings (not just heuristics), reusable mapping libraries, tight integrations into spreadsheets and BI tools, and privacy-preserving browser-side processing—features that reduce ambiguity and adoption friction versus generic ETL or script-based approaches. Challenges include handling extreme schema variability, building and maintaining a broad connector ecosystem, and competing with free scripts and incumbent ETL vendors (competition level: medium), so early wins should target high-frequency, high-value API sources and enterprise templates that justify a $30K ACV through saved engineering hours and reduced report latency.