Connect PostgreSQL to VedaX and ask your database questions in plain English. See how AI turns them into safe, read-only SQL, with no query writing.
Your PostgreSQL database holds the truth about your business — but getting answers out of it usually requires someone who can write SQL, understand the schema, and have the right access. So the marketing lead waits on the analyst, the analyst waits on a free moment, and simple questions take days.
AI closes that gap. Instead of writing SQL, anyone on the team types a question in plain English and gets the answer back as a chart or table. The database stays exactly where it is; what changes is who can ask it questions.
Turning English into a correct query is not magic — it is a few steps done well:
1. Schema awareness. — VedaX reads your table and column names (and their relationships) so the AI knows what "revenue" or "signups" maps to in your actual database.
2. Query generation. — Your question plus that schema context is sent to the model, which writes a SQL query against the real tables — joins, filters, aggregations and all.
3. Execution and visualization. — VedaX runs the query (read-only) and picks the chart that fits the result, whether that is a time series, a bar chart, or a table.
Crucially, VedaX shows you the generated SQL. You are never asked to trust a black box — you can read exactly what ran, copy it, or refine your question if it is not quite right.
Good analytics should never put your production database at risk. The recommended setup is straightforward:
- Create a dedicated read-only user. Grant it SELECT on the tables you want to analyze and nothing more. If the credentials are ever misused, the blast radius is "someone read reporting data," not "someone changed it."
- Point that user at a replica if you have one. Heavy analytical queries then never compete with production traffic.
- Connect it in VedaX under Data Sources by entering the host, port, database, and that read-only user. Credentials are stored encrypted.
This pattern — least-privilege user, encrypted credentials, read-only access — is the same one experienced data teams use, and it means natural-language querying adds insight without adding risk.
Say you run a SaaS product and want to understand churn. Instead of writing a multi-table join, you type:
"How many subscriptions cancelled each month this year, and what was the total at the start of each month?"
VedaX reads your subscriptions schema, generates the SQL with the right date grouping and aggregation, runs it read-only, and returns a clean monthly chart — plus the exact query it used so an engineer can verify it. A question that would have been a ticket in someone's backlog is answered in seconds.
AI querying is powerful, so pair it with sensible guardrails:
- Read-only, always. The connecting user should never have write permissions. This is the single most important rule.
- Review the SQL on important decisions. For anything you will act on, glance at the generated query — VedaX shows it precisely so you can.
- Sanity-check the numbers. AI is excellent at the mechanics of SQL but knows nothing about quirks in your data. Treat the first answer to a brand-new question as a draft to confirm, not gospel.
With those in place, plain-English Postgres querying turns your database from a specialist-only resource into something the whole team can use.
Yes, when you follow least-privilege practice: create a dedicated read-only user that can only SELECT the tables you want analyzed, and ideally point it at a read replica. VedaX stores credentials encrypted and only issues read queries, so analytics can never modify your data. The recommended setup mirrors what professional data teams already use.
Not if you connect with a read-only user, which is the recommended configuration. VedaX generates SELECT queries for analysis and shows you the exact SQL it runs. Granting only read permissions guarantees that no generated query can write to, update, or delete anything in your database.
VedaX always shows the generated SQL, so you can verify it rather than trust a black box. If a result looks off, you can rephrase the question with more detail, and the model regenerates the query. For high-stakes decisions, reviewing the SQL and sanity-checking the numbers takes seconds and removes the guesswork.
See it in VedaX: AI business intelligence for small businesses