WhatA platform where users describe their data transformation needs in natural language, and the system generates deterministic, testable scripts — never passing live data through an LLM at runtime.
SignalSeveral commenters articulate a clear principle: LLMs should write the code that processes data, not process the data directly — yet many teams still feed API responses and spreadsheets through LLMs for summarization and get wildly inaccurate results because they treat the LLM as a data pipeline rather than a code generator.
Why NowNon-technical teams are increasingly using LLMs to process business data directly (summarizing API outputs, converting spreadsheets), creating a growing class of silent data quality failures that organizations are just starting to recognize.
MarketBusiness analysts, ops teams, and data teams at mid-market and enterprise companies who currently misuse ChatGPT/Claude for data processing. Adjacent to the data pipeline/ETL market (~$15B). Competes with traditional ETL tools but targets the new LLM-native user.
MoatLibrary of verified, tested transformation templates that grows with usage; trust layer built on guaranteed deterministic output with audit trails.
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