Business users ask questions in plain English. The AI writes the SQL. Analysts do strategic work.
The data team had a 3-week backlog of requests from sales, marketing, and finance. The VP of Sales was making pipeline decisions based on month-old data because he couldn't get a current report. Two data analysts quit citing 'death by ad-hoc request.' The Head of Data told the CTO: 'We're a help desk, not a strategic function.'
Despite $400K invested in BI tools and a modern data warehouse, only 6 people in the 300-person company could actually query the data. Everyone else submitted requests to the data team and waited. The team spent 80% of their time answering repetitive questions ('What were Q3 sales by region?') and 20% on the strategic analysis they were hired to do.
We built a natural language analytics layer that lets any employee ask questions in plain English and get accurate, sourced answers in seconds. A fine-tuned LLM translates questions into SQL against the company's actual schema. Semantic caching handles variations of previously answered queries instantly. Our data engineering team maintains schema mappings and runs weekly accuracy audits during overnight hours.
My sales directors check pipeline health every morning now. They just ask the question and get the answer. Before, they'd submit a ticket and check again in three weeks. We're making decisions with today's data instead of last month's.— VP of Sales
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