Natural Language to SQL AI
Share your schema; describe what you want in plain language. Ryna AI writes optimized SQL with index hints and performance notes.
As a data analyst, PM, or backend dev, SQL has moments of feeling adversarial: 6-table JOINs, window functions, CTE-heavy queries… You sit down to write 'MAU trend in PostgreSQL' and lose 20 minutes to syntax errors. AI plays a 'translate plain English to SQL' role here.
Ryna AI takes your schema and your plain-English (or Turkish) request, emits optimized SQL. PostgreSQL, MySQL, SQLite, SQL Server, BigQuery, Snowflake — state your dialect upfront and it uses the right functions (LATERAL JOIN, ARRAY_AGG, JSON operators). Index suggestions and performance notes come automatically.
For complex work, asking 'how does this scale to 1M rows?' produces an estimated EXPLAIN ANALYZE, bottleneck identification, and refactor suggestions. Free plan covers near-unlimited queries; uploading your DB schema export (CREATE TABLE statements, ER diagram PDFs) requires Plus ($12/mo, 399.99 TRY).
Why use Ryna AI for this
6 dialects supported: PostgreSQL, MySQL, SQLite, SQL Server, BigQuery, Snowflake — dialect-specific functions and syntax.
Natural language → SQL: describe in English or Turkish, get optimized output — JOIN, CTE, window functions, subqueries.
Index suggestions: automatic 'this slows on 1M rows — add this composite index' analysis.
Performance estimation: simulated EXPLAIN ANALYZE — bottleneck + refactor suggestion.
Query explanation: 'why this JOIN?' / 'how could this CTE be rewritten?' for interactive learning.
Error debugging: paste your query + error message → root cause + fix + tip to avoid similar errors.
Example prompts
Copy any prompt below and paste into chat.rynaai.com. Each prompt is tuned for a different scenario — try them all to see how Ryna AI adapts.
How it works — step by step
- 1
Specify the schema cleanly
Paste CREATE TABLE statements or table descriptions. 'orders (id, user_id, total, created_at)' is enough; types as a bonus ('int', 'varchar(100)', 'timestamp'). For 4+ tables, upload an ER diagram PDF (Plus).
- 2
State the dialect
'PostgreSQL 15' or 'MySQL 8' or 'BigQuery' — dialect-specific functions differ (PG: LATERAL JOIN, BQ: STRUCT, MySQL: JSON_EXTRACT). Wrong dialect won't execute. For dialect-agnostic, say 'standard ANSI SQL'.
- 3
Describe the need in natural language
'Top 10 customers by spend in the last 30 days, filtered by country' — Ryna turns this into CTE + JOIN + ORDER BY LIMIT. For perf hints, add 'should run fast on a 5M-row table'.
- 4
Ask for explanation
'Walk through this query line by line, why CTE, how could it be rewritten?' Learning mode — critical for junior analysts. After a few queries you'll understand CTE vs subquery vs window function tradeoffs.
Example: input → output
You ask
PostgreSQL. orders (id, user_id, total, created_at), users (id, name, country). Top 10 US customers spending $5K+ in the last 30 days.
Ryna AI returns
```sql WITH recent_orders AS ( SELECT o.user_id, SUM(o.total) AS total_spent, COUNT(*) AS order_count FROM orders o WHERE o.created_at >= NOW() - INTERVAL '30 days' GROUP BY o.user_id HAVING SUM(o.total) >= 5000 ) SELECT u.id, u.name, ro.total_spent, ro.order_count FROM recent_orders ro JOIN users u ON u.id = ro.user_id WHERE u.country = 'US' ORDER BY ro.total_spent DESC LIMIT 10; ``` **Performance note:** A composite index on orders.created_at and orders.user_id is recommended: ```sql CREATE INDEX idx_orders_created_user ON orders (created_at DESC, user_id); ``` **Why CTE?** We filter + aggregate first (assume orders has ~5M rows), then JOIN to users — this carries 70-80% less data because we only join qualified users. CTE over subquery for readability.
Pro tips
- Provide schema with types: 'created_at TIMESTAMP', 'total NUMERIC(10,2)' — Ryna picks the right function (DATE_TRUNC vs CAST).
- Break complex queries into CTEs — improves readability and gives the planner optimization wins.
- When index suggestions come in, use CREATE INDEX CONCURRENTLY before applying to production — else the table locks.
- Window functions (LAG, LEAD, ROW_NUMBER) are often faster than GROUP BY + JOIN.
- Don't 'SELECT *' on BigQuery — pick only required columns to reduce billable bytes.
- Paste EXPLAIN ANALYZE output and Ryna identifies the real bottleneck (Seq Scan vs Index Scan) and optimizes.
Ryna AI vs ChatGPT (free) / GitHub Copilot
| Feature | Ryna AI | ChatGPT (free) / GitHub Copilot |
|---|---|---|
| Dialect-specific functions | Built-in syntax differences for 6 dialects | Knows them but mixes dialects (PG fn in MySQL) |
| Performance analysis | EXPLAIN simulation + index suggestion | Generic; real query-plan analysis limited |
| Schema upload (PDF/CSV) | Built into Plus ($12/mo) | Available on ChatGPT Plus; not in Copilot |
| Turkish natural language → SQL | Built-in (TR-first development) | Possible but adds a translation layer |
Common mistakes to avoid
- ✕Asking for a query without specifying dialect — Ryna defaults to PostgreSQL; don't be surprised if it fails on MySQL.
- ✕Using SELECT * on large tables — especially on BigQuery, blows up billable bytes.
- ✕Applying a composite index suggestion to production without testing — table-locking risk.
- ✕Writing 8+ JOIN queries without optimization — the planner struggles, latency balloons.
- ✕Not sharing schema types — Ryna guesses, may pick the wrong function (TIMESTAMPTZ vs DATE).
- ✕Saying 'this query is slow, optimize' without an error or EXPLAIN — blind optimization wastes time.
Who this is for
Data analysts, PMs, backend devs, data scientists.
FAQ
Which SQL dialects are supported?
PostgreSQL (9-16), MySQL (5.7-8.x), SQLite, SQL Server, BigQuery, Snowflake. Basic support for Redshift and Oracle, but dialect-specific functions are more limited. State your dialect upfront.
Does it connect to my production DB and execute?
No — Ryna writes queries, doesn't execute. Run output in your own DB client (DBeaver, pgAdmin, MySQL Workbench, BigQuery Console). Security plus: no accidental DELETE in production.
Safe to share schema with PII-containing tables?
Schema structure (column names, types) is safe — no actual data. To be extra cautious, share with anonymized column names like 'email_hash' or 'pii_redacted'. AI doesn't need real email patterns.
Can it optimize my existing slow query?
Yes — paste the query + EXPLAIN ANALYZE output + table size info. Ryna identifies bottleneck, suggests refactor, recommends indexes. Typical 2-5× speedup.
Does it write stored procedures or triggers?
Yes — say 'write a trigger that copies INSERT/UPDATE/DELETE to users_audit'. Supports plain SQL, Stored Procedures (PG/MySQL), T-SQL (SQL Server).
Does it generate ORM code (Prisma, TypeORM, SQLAlchemy)?
Yes — ask 'how do I write this PostgreSQL query in the Prisma query API?'. Supports Prisma, TypeORM, SQLAlchemy, Sequelize, Hibernate, Eloquent. State the version.
Does pasting an error message fix it?
Yes — one of the strongest use cases. Paste query + full error message + (optional) dialect + version. Ryna explains root cause, gives fixed query, adds tip to prevent similar errors.
Any limit on the free plan?
Free has near-unlimited daily messages — easily 100+ queries per day. Only schema PDF or ER diagram uploads require Plus ($12/mo, 399.99 TRY).
Related use cases
Free — near-unlimited daily messages
No credit card. Plus at $12/mo (399.99 TRY) unlocks image analysis, file analysis (PDF/Word/Excel), deep thinking, web research, and assistants.