Fix unreliable AI recursive schemas by flattening filter generation schemas

Fix unreliable AI recursive schemas by flattening filter generation schemas targets a $3.6B = 1.2M developer teams × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 15% YoY (developer tools + AI integration growth estimate based on Stack Overflow and industry analyst reports).

Key trends driving demand: LLM integration into product features is mainstream — teams increasingly rely on structured outputs (filters, JSON responses) rather than plain text, creating demand for schema compatibility tooling.; Shift to schema-driven prompts and response_format in LLM APIs — this increases the importance of deterministic schema compatibility layers that handle model quirks.; Open-source-first developer tools gain rapid adoption and then convert to paid hosted tiers — this pattern favors an OSS + hosted business model for tooling.; Increased enterprise adoption of LLMs drives demand for SLA-backed, validated pipelines and telemetry to manage model regressions and version changes..