Model plus context
The quality comes from combining AI reasoning with schema summaries, business rules, and query history.
AI SQL generator
TTSQL combines modern language models with schema understanding, self-correction, and security controls so generated SQL is useful in real operations.
Show revenue by plan for the last 30 days
1SELECT plan_name, SUM(amount) AS revenue
2FROM invoices
3WHERE paid_at >= CURRENT_DATE - INTERVAL 39;30 days39;
4GROUP BY plan_name
5ORDER BY revenue DESC;
Overview
The quality comes from combining AI reasoning with schema summaries, business rules, and query history.
Users can refine prompts instead of rebuilding queries from scratch every time requirements change.
Call versioned endpoints, manage project context, and add AI SQL generation to existing products.
Query examples
Show revenue by plan for the last 30 days
1SELECT plan_name, SUM(amount) AS revenue
2FROM invoices
3WHERE paid_at >= CURRENT_DATE - INTERVAL 39;30 days39;
4GROUP BY plan_name
5ORDER BY revenue DESC;
Use cases
Prototype analytics questions quickly, then harden the outputs for production.
Give engineering, data, and support teams one AI SQL generator they can all use.
Add AI-assisted query generation to a SaaS analytics or reporting product.
FAQ
Accuracy alone is not enough. Look for schema awareness, correction loops, security controls, auditability, and an API that fits production applications.
Yes, when it is paired with structured project context. TTSQL lets teams add rules, descriptions, and examples so the generated SQL follows company logic.
Enterprise
Use TTSQL for teams that need SSO, policy controls, auditability, dedicated support, and deployment flexibility.