Most text-to-SQL systems treat the task as translation. Feyn AI (YC-backed startup) reframes it around inspection. The Feyn team has released SQRL, a family of models that turn natural language questions into SQL. Instead of generating a query immediately, SQRL can inspect the database first. This lets it resolve ambiguity and write only queries the data actually supports.
Feyn team reports that the flagship SQRL-35B-A3B reaches 70.6% execution accuracy on BIRD Dev. That figure edges Claude Opus 4.6 at 68.77% under the same evaluation. Three checkpoints ship openly on Hugging Face: SQRL-4B, SQRL-9B, and SQRL-35B-A3B.
A query can be valid SQL and still be wrong
Text-to-SQL is often described as a translation problem, but that framing misses the hardest part. A query can be perfectly valid SQL and still return the wrong answer. It can join the wrong tables, read an ambiguous column incorrectly, or filter for values that do not exist. None of these mistakes throws an error, so none is caught by execution alone.
Schema information does not prevent them. A schema lists tables, columns, types, and sometimes relationships. It does not reveal whether a county is stored as Alameda, Alameda County, or ALAMEDA. It cannot tell you which join produces duplicate rows.
