Unlocking Enterprise Data with SQL agents:
In today’s data-rich organizations, the challenge isn’t a lack of information—it’s access. Business users are surrounded by dashboards, reports, and databases, yet still struggle to get timely answers to simple questions like:
“What were our top-selling products last quarter in the Amsterdam region?”
The rise of Large Language Models (LLMs) has made it easier to interact with data using natural language. But there’s a catch: most enterprise datasets are far too large to fit within an LLM’s context window. This means that even the most advanced AI models can’t “see” the full picture when trying to answer your question—unless you give them a smarter way to look.
The Problem: Data Outside the Context Window
LLMs like GPT-4 and Claude are powerful, but they have a limit to how much information they can process at once. When your data lives in multiple tables, spans millions of rows, or requires joining across systems, it’s simply too much to feed into a prompt.
This leads to a frustrating paradox: the data exists, but the AI can’t access it in a meaningful way.
The Solution: Text2SQL Conversational Analytics
To solve this, we built a Text2SQL solution that acts as a translator between natural language and structured data. Instead of trying to cram all your data into the model, we let the model generate SQL queries that retrieve exactly what’s needed—no more, no less.
Here’s how it works:
- Ask in Plain Language
Users type questions like “Show me the average basket size for Q2” or “Which stores had the highest return rates?” - AI Translates to SQL
The system uses a fine-tuned LLM to generate SQL queries tailored to the organization’s schema and business logic. - Query Executes on Live Data
The SQL runs directly against the company’s data warehouse, returning accurate, real-time results. - Results Are Explained
The system can also provide a natural language explanation of the results, helping users understand what they’re seeing.
Real-World Impact
In one retail organization, we embedded this solution into their internal data products. The result?
- Store managers could get answers in seconds—without waiting for BI teams.
- Analysts spent less time writing repetitive queries and more time on strategic work.
- Data adoption increased across merchandising, operations, and marketing teams.
Why This Matters
By decoupling the LLM from the data and using it as a query generator—not a data processor—we avoid the context window limitation entirely. This approach:
- Preserves data fidelity and granularity.
- Keeps sensitive data within secure environments.
- Scales across departments and use cases.
What’s Next
We’re now expanding this approach with orchestration layers that allow users to query across multiple data products at once—without needing to know where the data lives or how it’s structured.
