Your team's corrections carry into the next answer.
BeyondQueries writes SQL from a plain-English question and shows you the query it ran. What makes it different is what happens after someone says the answer was wrong.
From a question to grounding, once.
The first five stages are what every text-to-SQL tool does, in some form. The sixth is the one that makes later answers better than the first.
You ask in plain English
“Which accounts churned last quarter and what did they pay us?” No SQL, no knowledge of the schema, no request to a data team.
Retrieval before generation
Before writing anything, BeyondQueries searches a vector store of questions your team has already settled on this database, plus the table and column instructions people have written. The model starts from what is known rather than from the schema alone.
It writes the SQL and shows it
The generated query is displayed next to the answer, always. You read the join and the filter before you trust the number — the query is not hidden behind a chat bubble.
Anyone can mark it wrong
Correct, incorrect or partially correct, with a comment explaining why. One piece of feedback per person per query, so a disagreement stays visible instead of being overwritten.
A reviewer approves the correction
Flagged queries enter a review queue. Someone with review permission approves or rejects, and can leave their own reason. Nothing enters the knowledge base because one person disagreed with it.
The next question starts there
Approved corrections become retrievable grounding. Ask the same question a month later, or a question near it, and the answer arrives already carrying the correction.
A correction is only worth keeping if it is shared.
The loop is not a model-training claim. Nothing is fine-tuned. Approved corrections are stored and retrieved — which is why they take effect immediately, and why you can read exactly what was retrieved.
One-shot tools start from zero
A tool that only sees your schema makes the same mistake about the same ambiguous column every time, for every person, forever. Nothing accumulates.
A desktop client cannot pool knowledge
Corrections that live on one analyst's laptop help one analyst. The loop only compounds if the store is shared and the approvals are governed.
Schema notes outlive the person who wrote them
Table and column instructions are attached to the instance, not to a conversation. The colleague who joins next year inherits them.
A sandbox, then a promotion.
Schema instructions and connection changes can be exercised in a sandbox copy of an instance first, then promoted once they behave. People with production access do not have to be the people experimenting with the configuration — the role matrix separates the two.
Connect a database and ask it something badly.
The first answer might be wrong. Correct it, and watch the second one arrive already knowing.