NL2SQL
Natural Language Based Database Analysis
Analyzes complex database structures through natural language questions, generates optimal SQL queries, and visualizes results.
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Data Exists, But Can't Be Used
Companies' most valuable asset — their data — sits in databases, but only a handful of employees can write SQL. Analysis requests pile up in the IT department, causing 3–5 day delays that cause businesses to miss critical decision windows.
Anyone Can Analyze Data in Plain Language
No SQL required. Just ask in natural language: 'Show me top 10 products by last month's revenue' — and the AI generates the query and visualizes results. Business users can analyze real-time data directly, without IT.
SUVsoft's NL2SQL
Deployable in on-premise environments so sensitive corporate data never leaves your servers. Compatible with MySQL, PostgreSQL, Oracle, and more. Achieves over 90% query accuracy through domain-specific training on your data schemas.
Key Features
Natural Language Query
Query and analyze DB info without SQL knowledge.
Query Optimization
LLM understands data schemas for high-performance, accurate SQL generation.
Visualized Reports
Instantly converts queried data into charts and graphs.
Cross-DB Support
Perfectly works with major RDBMS like MySQL, PostgreSQL, and Oracle.
Specifications
Supported databases
- MySQL / MariaDB
- Most common setup. Connects without conversion
- PostgreSQL
- Recognises schema and view structures
- Oracle Database
- Packages and synonyms mapped during onboarding
- MS SQL Server
- Generates T-SQL syntax
Runtime environment
- Deployment
- On-premise or private cloud
- LLM
- Runs on your own servers. No external API calls
- Network
- Can run fully air-gapped from the internet
- Access control
- Inherits your database account permissions
Input and output
- Input
- Plain-language questions
- Output
- Result table, chart, and the generated SQL
- Query accuracy
- Over 90% after domain tuning
How Deployment Works
- 1
Schema analysis
We map your tables, columns and relationships, and connect the vocabulary your teams use to the actual column names. Most of the final accuracy is decided here.
- 2
Accuracy validation (PoC)
We take 30-50 questions your teams would really ask and measure accuracy. You decide whether to proceed based on that number.
- 3
Tuning
We analyse which question types failed and strengthen schema comments and the term dictionary to reduce repeat errors.
- 4
Deployment
Installed on your servers, with user accounts connected to database permissions. A read-only account is recommended.
- 5
Operation
Query history is used to keep improving the cases that fail most often.
In Practice
“Top 10 products by revenue per region last quarter”
Teams pull their own weekly reporting numbers instead of queuing behind the IT department.
“Department cost change versus the same period last year”
Numbers needed before close are checked on the spot, and follow-up questions can be asked immediately.
“Defect rate by production line last month”
Floor supervisors query directly and spot anomalies sooner.
“Annual case counts broken down by type”
Departments handle their own statistics requests. Running air-gapped means internal data never leaves the network.
FAQ
Yes, no SQL knowledge required at all. Just ask in plain language like 'Show department expenses for last month' and NL2SQL automatically generates the query and visualizes the results.
MySQL, PostgreSQL, Oracle, MS SQL Server and other major RDBMS are supported. It integrates directly with existing corporate databases without any conversion.
When deployed on-premise, all processing occurs on your internal servers and data is never transmitted externally. Sensitive financial and sales data can be used safely.
Yes, Oracle Database is supported. Oracle environments often make heavy use of packages, synonyms and views, so we map those objects during the initial schema analysis.
We recommend connecting a read-only database account. Writes and deletes are then blocked at the account permission level, so no generated query can change data regardless of its form.
Access follows your database account permissions. If departments have access to different tables, connect an account with those permissions and queries stay within that scope.
Over 90% query accuracy is achieved through domain-specific training. Accuracy is continuously optimized through schema analysis and tuning during the initial onboarding period.
Schema clarity matters more than table count. Abbreviated column names without comments make it hard for the model to infer meaning, so early in the project we map your business vocabulary to actual columns.
A BI tool shows dashboards someone built in advance; a new question means requesting a new dashboard. NL2SQL lets you ask the question directly, as it comes up. Many teams keep BI for recurring metrics and use NL2SQL for ad-hoc questions.
It depends on schema size and the kinds of questions you need answered. We recommend starting with a PoC that measures accuracy on 30-50 of your real queries, then agreeing a timeline from that result.
Interested in NL2SQL?
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