Healthcare · United Kingdom · LLM / Text-to-SQL
Medicine shortage intelligence
Natural-language querying over live medicine shortage data — staff ask in plain English and get a real answer, no SQL required.
847/day
NL→SQL queries
Around 847 natural-language queries a day, answered directly from live data — analyst-grade access for the whole team.
—Context
Frontline pharmacy staff needed to interrogate fast-moving shortage and tariff data, but only a handful of people could write the queries to do it.
—Challenge
Make a complex, frequently-changing dataset answerable by anyone on the team, accurately, without exposing them to SQL or stale reports.
—What we built
- A text-to-SQL layer translating plain-English questions into validated queries over the live dataset.
- Retrieval and guardrails so answers stay grounded in the actual data, not the model's guesses.
- A clean query interface surfacing results, not raw tables.
—Outcome
Around 847 natural-language queries a day, answered directly from live data — analyst-grade access for the whole team.
—Stack
LLMText-to-SQLPythonPostgreSQL
→Ready to ship
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