Ask Your Data Anything,
We'll Squeel It into SQL

SqueelGPT instantly translates natural language into accurate SQL queries, empowering teams to explore data.

User Query

Processing Agents

Intent
Format
Tables
Logic

SqueelGPT Core

Query Processor

Database Schema

Orders
Users
Products
Regions

Generated SQL

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The Database Dilemma

Why working with SQL databases is both essential and challenging

Top 4

SQL is the industry standard

Universally adopted, unavoidable for data work

StackOverflow 2024

Top 4 most used databases are SQL-based: PostgreSQL, MySQL, SQLite, and Microsoft SQL Server

21

Multiple dialects create complexity

Different syntax across databases

21 different dialects

SQL comes in many flavors—PostgreSQL, MySQL, SQLite, SQL Server, Oracle, etc.—each with unique syntax variations

57%

Specialized knowledge required

SQL expertise is a rising barrier

57% of organizations

Rate “skill requirements and team training” as their biggest challenge when managing database technologies, up from 38% in 2023

45 %

AI struggles with SQL

Low accuracy on complex SQL generation

45.34 % high score

Single AI chatbots like ChatGPT struggle with SQL, achieving only 38.21% score accuracy in the Spider 2 Benchmark 2025

SqueelGPT solves these challenges by translating natural language to SQL

Ask Your Data Anything

SqueelGPT instantly transforms natural language into accurate SQL queries, no technical expertise required.

SqueelGPT

Hello! I'm connected to your sales_data database. What would you like to know?

For each sales region, identify the product category that generated the highest total revenue and show the total revenue for that category in that region.

Processing request...

Identifying relevant tables: customers, orders, order_items

Here's the SQL query:

WITH RegionalCategoryRevenue AS (
    -- Calculate total revenue for each category within each region
    SELECT
        c.region,
        pc.category_name,
        SUM(oi.price * oi.quantity) AS total_category_revenue
    FROM
        customers c
    JOIN orders o ON c.customer_id = o.customer_id
    JOIN order_items oi ON o.order_id = oi.order_id
    JOIN products p ON oi.product_id = p.product_id
    JOIN product_categories pc ON p.category_id = pc.category_id
    GROUP BY
        c.region,
        pc.category_name
),
RankedRegionalCategoryRevenue AS (
    -- Rank categories by revenue within each region
    SELECT
        region,
        category_name,
        total_category_revenue,
        RANK() OVER (PARTITION BY region ORDER BY total_category_revenue DESC) as rank_in_region
    FROM
        RegionalCategoryRevenue
)
-- Select the top ranked category (rank = 1) for each region
SELECT
    region,
    category_name,
    total_category_revenue
FROM
    RankedRegionalCategoryRevenue
WHERE
    rank_in_region = 1
ORDER BY
    region;

Key Features

70% Time Savings

Reduce SQL query formulation time by up to 70%, matching efficiency gains reported by similiar implementation.

Democratize Data Access

Empower non-technical team members to extract insights without learning complex SQL syntax or database structures.

Multi-Dialect Support

Works across PostgreSQL, MySQL, SQLite, and Microsoft SQL Server with dialect-specific optimizations.

Context-Aware AI

Proprietary pipeline understands your database schema and relationships for more accurate queries than general-purpose chatbots.

Safe & Secure

Executes in a sandboxed environment with read-only permissions to protect your data integrity while validating results.

Save & Reuse Queries

Bookmark frequent queries for quick reuse and modification, building a personalized SQL query library.

Access your database insights from anywhere by anyone.

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