Matih is an AI-driven data intelligence platform engineered to eliminate the unreliability of standard Text-to-SQL tools. While typical tools are schema-blind and often guess table joins or return unverified metrics, Matih creates a context graph of your data. This graph maps out entities, table relationships, and metric definitions to reason over your data ontology before providing an answer.
Users can ask questions in plain English, and Matih will write the corresponding SQL and automatically route the query to the appropriate engine. It uses DuckDB for lightweight data like spreadsheets, and scales up to Spark or Trino when processing billions of rows across complex data warehouses.
To ensure full transparency and trust, Matih displays the generated SQL right next to the answer, removing the "black box" effect and allowing users to verify how their results were calculated.
Matih is a data intelligence platform that allows users to ask plain-English questions about their data. It uses a context graph to understand table relationships and metric definitions, then automatically generates and executes the appropriate SQL.
Instead of guessing table joins and providing unverified numbers, Matih displays the generated SQL directly alongside the answer so users can verify the logic behind every insight.
Matih supports data sources ranging from Excel spreadsheets to full enterprise data warehouses. It automatically routes execution to DuckDB for small files or Spark and Trino for massive datasets with billions of rows.
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Co-founder, Matih Labs
