case study — shipped
Describe a workflow in plain English — get deployable n8n JSON back.
Building n8n workflows by hand means knowing hundreds of node schemas. LLMs hallucinate node configs unless they are grounded in the real schema.
built for — Internal tooling — accelerating every client build.
“Ground the model in 794 real schemas and it stops guessing.”
A natural-language enhancer sharpens the brief, the retriever pulls exact node schemas from pgvector, and the builder (Claude + Gemini) assembles JSON that validates against the real schema before it ships.
Unassisted LLMs invent plausible-looking n8n node parameters that fail on import.
resolutionIndexed all 794 node schemas into Supabase pgvector with OpenAI embeddings; the builder only sees retrieved, real schemas, and output is validated before delivery.
Production-ready n8n workflow JSON from natural language.
794 node schemas indexed and retrievable by semantic search.
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