txt2sql runs on your messy, real-world SQL Server — hundreds of cryptic tables, missing foreign keys, no clean docs — and writes correct, production-grade SQL. On-prem or air-gapped. Your data never leaves the building.
One question — "customers with an incoming transfer in January but no outgoing transfer in February, dealing with 3+ distinct counterparties." Negation, two date windows, a distinct-count threshold, mixed direction — on a schema with archive and log tables mixed in. A general LLM picks the wrong table, drops the negation, and ships plausible-but-wrong SQL. txt2sql narrows dozens of tables to the few that matter, understands the question, and writes SQL that runs.
Hundreds of tables with cryptic names like MatSvcId, relationships that live in someone's head instead of foreign keys, no docs. txt2sql translates cryptic physical names to meaningful ones, infers the relationships that aren't declared, and hides the archive/log/system tables that throw the model off.
“Who did X but not Y, across two periods, above a threshold?” is where text-to-SQL tools break. txt2sql decomposes the question into a structured plan first — negation, date windows, distinct-count thresholds, source vs destination direction — then generates SQL against that plan.
For banks, insurers, public sector and healthcare, sending schema or data to a cloud API is a non-starter. txt2sql runs in two modes: cloud mode sends only abstracted schema metadata — never a single row — and local mode sends nothing at all.
| Mode | What leaves your network |
|---|---|
| Local SQLCoder / Ollama / LM Studio |
Nothing Air-gapped. Even table names stay inside. |
| Cloud e.g. Gemini |
Schema metadata only Table/column names, abstracted so noise tables are hidden. Never any row data. |
“Row data never leaves, in any mode. If you don't even want schema names to leave, local mode sends nothing at all.”
WITH (NOLOCK) and sargable date ranges, never query-killing YEAR()/MONTH() filters.txt2sql is the entry point. The goal is an autonomous data analyst that lives next to your database — and never sends your data anywhere.
Correct queries on messy, real-world schemas. On-prem.
Missing indexes, runaway tables, concrete tuning suggestions — read by an LLM that actually explains why.
Your recurring checks run on a schedule and the results get interpreted, on-prem — the part cloud tools can't do, because the data can't leave.
Pulls tasks from your tracker, runs the checks, drafts the fix, and routes it to a human for approval. A data ops coworker, fully inside your walls.
Want to shape this roadmap? The first design partners get a direct line.
If you run on-prem SQL Server, have privacy or GDPR constraints, and a data team drowning in ad-hoc report requests from the business — txt2sql is built for exactly your situation. Especially if “just use ChatGPT” is off the table because your schema can't leave the building.
A short call, a live demo against a schema like yours, and an honest answer on whether it fits. No deck, no pressure.
Request a demo →Or email info@txt2sql.app directly.