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Matih
Plain-English questions, Trusted insights from your data
Text-to-SQL tools are schema-blind. they guess the joins and hand you a number you can't check. Matih builds a context graph of your data:-the entities, how tables relate, what each metric means, then reasons over that ontology to answer. Ask in plain English. Matih writes the SQL and auto-routes it to the right engine- DuckDB for a spreadsheet, Spark or Trino for billions of rows- from an Excel file to a full warehouse. You get the answer next to the SQL. Trusted insights, no black box.
Hey Product Hunt 👋
Every "chat with your data" tool I tried had two blind spots. It could read my column names but had no clue what my business actually was:- ask "which customers are at risk?" and it'd confidently join the wrong tables. And the moment the data got big, it fell over.
A good analyst is useful for the opposite reasons: they carry a model of the business in their head — what an "account" is, that refunds live in a different table than orders, what "active" really means here. and they don't care whether the answer lives in a 200-row sheet or a billion-row warehouse.
So we built both into Matih.
It maps your data into a context graph: the entities, the real relationships, the metric definitions; and grounds every answer in that ontology instead of guessing from column names.
And it scales without you thinking about it. Ask a question and Matih writes the SQL, then auto-routes it to the right engine - DuckDB for a quick spreadsheet, Spark or Trino when it's billions of rows. Same question, same plain English, whether it's a CSV you dragged in this morning or your entire warehouse.
Then it show its work: the answer next to the SQL, the rows behind it, the reasoning. Wrong? You see exactly where and fix the graph - you don't re-prompt into the void.
It's live at https://prod.app.matih.ai. I'll be here all day - tell me the parts that annoy you, that's the feedback I actually want.
About Matih on Product Hunt
“Plain-English questions, Trusted insights from your data”
Matih was submitted on Product Hunt and earned 33 upvotes and 7 comments, placing #32 on the daily leaderboard. Text-to-SQL tools are schema-blind. they guess the joins and hand you a number you can't check. Matih builds a context graph of your data:-the entities, how tables relate, what each metric means, then reasons over that ontology to answer. Ask in plain English. Matih writes the SQL and auto-routes it to the right engine- DuckDB for a spreadsheet, Spark or Trino for billions of rows- from an Excel file to a full warehouse. You get the answer next to the SQL. Trusted insights, no black box.
On the analytics side, Matih competes within Data & Analytics, Data and Business Intelligence — topics that collectively have 11.9k followers on Product Hunt. The dashboard above tracks how Matih performed against the three products that launched closest to it on the same day.
Who hunted Matih?
Matih was hunted by Sushrut Ikhar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Matih including community comment highlights and product details, visit the product overview.
Sushrut Ikhar