I added a "database intelligence layer" to my deployment platform. Here's why and how it works.

One pattern I kept seeing after launching NEXUS AI:

Developers would deploy an AI-generated app, the app would hit a database error at runtime, and then they'd be stuck in a loop of reading logs, opening a DB console, trying to figure out what went wrong with the schema.

AI tools are great at generating application code. They're less great at getting the database schema exactly right on the first try. Missing indexes, wrong column types, a migration that assumed a column existed when it didn't.

So I built what I'm calling a database intelligence layer directly into the platform.

Here's what it does:

Schema inspection. You can inspect your tables, columns, types, and indexes directly from the dashboard or via MCP. No need to open a separate DB console.

Query preview. Before running a query against your production database, you can preview it in a sandboxed environment with a statement timeout. No accidental full-table scans.

AI-proposed fixes from runtime logs. This is the part I'm most excited about. When your app throws a database error, Claude can inspect the runtime log, propose a DDL fix (the actual SQL to correct the schema), and apply it after you review and approve it. The change is audited.

The workflow looks like this: app throws a runtime error → Claude reads the log via nexusai_deploy_logs → Claude proposes a fix via nexusai_db_propose_fix → you review it → Claude applies it via nexusai_db_apply_fix.

It closes the loop between code generation and production reliability in a way I haven't seen on other platforms.

Still iterating on this. The approval step is intentional: I don't want AI applying DDL changes to production databases without a human in the loop. But the proposal and review flow is fast enough that it doesn't slow you down.

If you're building with Claude Code or Cursor and hitting schema issues in production, this is what NEXUS AI was built for.