Self-Learning NL2SQL: How inferonIQ Gets Smarter Daily
Every query, correction, and match decision feeds back into the system. Here's how the feedback loop works.
Natural Language to SQL (NL2SQL) is the backbone of inferonIQ's “Ask AI” interface. Users type questions in plain English — “Show me all invoices over $10K from Q4 that don't have a matching PO” — and get back accurate SQL queries against their own data.
But the hard part isn't generating SQL. It's generating correct SQL, against your schema, with your naming conventions, on the first try.
The Schema Intelligence Layer
Before inferonIQ can answer questions, it needs to deeply understand your database. The Schema Intelligence Agent performs auto-cataloging and profiling:
- Auto-discovery — Scans every table, column, view, and relationship. Builds a complete catalog without manual input.
- AI-generated descriptions — Uses LLMs to generate human-readable descriptions for every column (e.g.,
INV_AMT→ “Invoice amount in USD”). - Data profiling — Analyzes value distributions, null rates, cardinality, and min/max ranges to understand what each column actually contains.
- Relationship inference — Detects foreign keys, even when they aren't declared, by analyzing value overlap between columns.
The Self-Learning Feedback Loop
Every interaction improves the system through three feedback channels:
1. Query Corrections
When a user edits a generated SQL query before executing it, inferonIQ records the original and corrected versions. The correction is stored as a “gold prior” that biases future generation toward the corrected pattern.
2. Execution Feedback
Queries that execute successfully and return reasonable result sets (not empty, not cartesian explosions) are scored higher. Failed queries are flagged for pattern analysis.
3. Match Decision Learning
When users accept, reject, or modify match results, those decisions train the matching confidence model. Over time, the system learns your organization's specific tolerance thresholds and exception patterns.
Dialect-Aware SQL Generation
inferonIQ generates native SQL for each dialect — Snowflake, BigQuery, PostgreSQL, Oracle, SQL Server, and MySQL — including dialect-specific functions, quoting rules, reserved word handling, and date formatting. The same natural language question produces different (but correct) SQL depending on the target database.
Benchmarked Against the Best
We benchmark inferonIQ's NL2SQL engine against the Spider 2.0 benchmark, the industry standard for text-to-SQL evaluation. Our architecture achieves competitive accuracy on both the Spider 2.0 Lite (SQLite/DuckDB) and Snow (Snowflake) variants, particularly on complex multi-table joins and aggregations that are common in enterprise financial queries.
What This Means for Your Team
Instead of writing SQL tickets for the data team or waiting for reports, finance analysts can ask questions in plain English and get answers in seconds. The system gets more accurate with every interaction, meaning adoption drives improvement in a virtuous cycle.