AppForge AI

Standard LLM coding assistants generate raw code without enough structure, validation, or repair logic. That makes prompt-to-application workflows brittle when outputs need to become coherent system designs rather than one-off snippets.

AppForge treats natural-language application generation as a compilation problem. It uses a staged pipeline with intermediate representations, typed Pydantic v2 schema validation, cross-layer checks, and repair loops before packaging the generated application configuration.

Five-stage pipeline: intent extraction → system-design representation → sequential database/API/UI/auth schema generation → cross-layer validation → boot repair engine · Cerebras primary LLM + Groq/OpenAI fallback · FastAPI backend + Next.js 14 frontend · SQLite persistence.