Governed 'talk to your data' for enterprise HR and operations
Business users ask questions in plain English and get accurate, access-controlled answers from SQL Server, with every query validated before it runs.
The challenge
Business users depended on IT for every report. Generic AI chat over a database fabricates table and column names, and without strict controls it lets employees see records they shouldn't.
What we built
- A curated metadata catalog with descriptions, synonyms, approved joins and metrics, so the model never guesses the schema.
- Vector search retrieves only the 3–4 tables relevant to each question.
- A 7-stage validator checks every generated query against approved tables, columns, joins and business rules before execution.
- A deterministic rewriter injects row-level filters so employees only ever see their own records.
Results
- No SQL knowledge needed to query enterprise data
- Hallucinated tables, columns and joins blocked before execution
- Employees can only ever see their own records, regardless of model behaviour
- New business domains added through configuration, not code
Step by step.
Ask
A user asks a question in plain English.
Retrieve
Vector search returns only the relevant, permitted tables.
Generate
A locally hosted model writes SQL constrained to that schema.
Validate
Seven checks block hallucinated tables, columns, joins and unsafe SQL.
Enforce
Row-level filters are injected for the user's role.
Answer
Results stream back as a table, chart or summary.
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Let's see what this could look like for you.
Tell us about your process and systems. We'll outline an approach, the guardrails it needs and a pilot plan.
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