SQL Query Generator

Chion SQL Query Generator — Try It on Your Postgres in 60 Seconds

Paste a read-only Postgres connection string, ask a question in plain English, and get a verified SELECT back in under a minute. Schema-profiled, LIMIT-enforced, capped at 1,000 rows. The SQL is shown under every chart. Each verified query joins your AI SQL workforce as an exportable skill.

Chion is an AI SQL workforce that turns plain English into verified PostgreSQL. Schema-aware, read-only, and auditable under every chart.

Problem. Analysts spend hours writing SQL by hand. PMs wait days for the data team. What Chion does. Paste a read-only PostgreSQL connection, ask in plain English, get a verified SQL query and an interactive chart in under a minute.

Question: "Who are our top 10 customers by revenue last quarter?"

SELECT
  c.customer_id,
  c.company_name,
  SUM(o.total_amount) AS revenue
FROM customers c
JOIN orders o ON o.customer_id = c.customer_id
WHERE o.order_date >= DATE_TRUNC('quarter', CURRENT_DATE - INTERVAL '3 months')
  AND o.order_date <  DATE_TRUNC('quarter', CURRENT_DATE)
GROUP BY c.customer_id, c.company_name
ORDER BY revenue DESC
LIMIT 10;

Generated by Chion's 13-step pipeline. Read-only SELECT, LIMIT enforced, schema-validated.

Type your question, Chion profiles your schema, builds a SQL contract, generates SQL inside that contract, validates it through L1 (read-only) and L2 (runtime lint), runs it read-only with LIMIT enforced, and renders the chart. Every step is auditable.

Ask "top 10 customers by revenue" and you get TopK ranking, which picks a different SQL shape than "compare revenue by region over time," which routes to comparison. The strategy determines the query structure before the model writes anything.

A ranking query, a multi-CTE ratio, and a window-function running total: three of the most common patterns Chion generates.

SELECT
  c.customer_id,
  c.company_name,
  SUM(o.total_amount) AS revenue
FROM customers c
JOIN orders o ON o.customer_id = c.customer_id
WHERE o.order_date >= DATE_TRUNC('quarter', CURRENT_DATE - INTERVAL '3 months')
  AND o.order_date < DATE_TRUNC('quarter', CURRENT_DATE)
GROUP BY c.customer_id, c.company_name
ORDER BY revenue DESC
LIMIT 10;

WITH monthly AS (
  SELECT
    DATE_TRUNC('month', canceled_at) AS month,
    COUNT(*) AS churned
  FROM subscriptions
  WHERE canceled_at IS NOT NULL
  GROUP BY 1
),
active AS (
  SELECT
    DATE_TRUNC('month', period_start) AS month,
    COUNT(DISTINCT user_id) AS active
  FROM subscriptions
  GROUP BY 1
)
SELECT
  m.month,
  m.churned,
  a.active,
  ROUND(m.churned::numeric / NULLIF(a.active, 0) * 100, 1) AS churn_rate_pct
FROM monthly m
JOIN active a ON a.month = m.month
ORDER BY m.month;

WITH weekly AS (
  SELECT DATE_TRUNC('week', order_date) AS week, SUM(amount) AS revenue
  FROM orders GROUP BY 1
)
SELECT
  week,
  revenue,
  SUM(revenue) OVER (ORDER BY week) AS running_total
FROM weekly
ORDER BY week;

A standalone SQL query generator gives you a query: one off, ad-hoc, regenerated next time you ask. Chion’s pipeline gives you a verified SQL agent skill: every verified query auto-promotes into your team’s SQL skill library, scoped to the persona that owns the data. Finance personas inherit revenue queries; ops personas inherit logistics queries. The library compounds with every question your team asks.

Every query is a valid PostgreSQL SELECT. Explore specific patterns: