Valv

This data workspace is designed for analysts, engineers, and teams that want to point an AI coding agent at a database without giving the agent unrestricted control. It connects to PostgreSQL, MySQL, SQLite, ClickHouse, and MCP, while keeping the workflow local on your machine.

You can ask database questions in plain language, have the agent draft the SQL, and then verify the queries and results inside the client. With local credentials, column-level access controls, query citations, and a file-based workspace, the product combines agent-assisted analysis with the ability to inspect and control the underlying database work.

Key Features

AI Agent Database Support

The workspace works with popular coding agents so you can bring database analysis into the tools you already use.

Supported agents include:

• Claude Code
• Codex
• Cursor
• opencode
• Grok

The agent can help translate questions into SQL and work with the database while the user remains able to review the resulting queries.

Plain-Language Database Queries

Instead of manually writing every query from scratch, you can ask questions about your data in natural language.

For example, you can ask about:

• Churn by plan
• Spikes in cancellations
• Business KPIs
• Trends across database tables

The agent drafts the SQL needed to answer the question, while query citations make it possible to inspect the work behind the response.

Local-First Database Workflow

The client runs on your machine and does not require API keys for the workflow described.

Database credentials are kept in your system keychain rather than being handed directly to the agent. This gives teams a local-first approach to working with sensitive database connections.

Column-Level Access Controls

You can hide selected sensitive columns from the agent while still allowing it to work with the rest of the available data.

This provides an additional layer of control for teams that want agent-assisted database analysis without exposing every field in a table.

Query Citations

Answers include the queries behind them so you can verify how the result was produced.

This makes it easier to move from an agent-generated answer back to the underlying SQL and inspect the database logic yourself.

Database Memory

The workspace can remember useful database context, including:

• Schema notes
• Join relationships
• KPI definitions
• Other context needed for recurring analysis

This reduces the need to repeatedly explain how your database is structured or how specific business metrics should be interpreted.

Git-Based Artifacts

Queries, dashboards, and database context can be stored in a file-based workspace inside a Git repository that you own.

That means database work can be:

• Versioned
• Shared with a team
• Reviewed alongside other project files
• Kept with query history and schema documentation

Instead of locking analysis inside a separate BI environment, the workspace treats it more like a code project.

SQL Editor and Live Table Browsing

The product does not require you to rely entirely on an AI agent. You can browse database schemas, open live tables, and write or edit SQL directly.

This gives analysts and engineers a manual path for inspecting data whenever they want to verify or modify an agent-generated query.

Sandboxed Code for Data Work

The workflow starts with SQL, but the system can also run sandboxed code when SQL alone is not enough.

This can be useful for:

• Joining larger datasets
• Additional data processing
• Creating exports
• Handling analysis that goes beyond a single SQL query

The approach keeps raw database data from simply being sent back to the model for every operation.

Built For Analysts and Engineering Teams

Data analysts can use natural-language requests to accelerate SQL work while still inspecting the queries and database results.

Engineers can connect coding agents to databases and keep database context, queries, and artifacts alongside their existing development workflow.

Data teams can give agents controlled access to operational data while restricting sensitive columns and retaining ownership of the resulting workspace.

Teams using coding agents can add database work to Claude Code, Codex, Cursor, opencode, or Grok without moving the workflow into a separate hosted BI environment.

Common Use Cases

Exploring Database Schemas

Browse schemas and live tables directly while using an agent to help understand relationships and structure.

Investigating Business Metrics

Ask questions about KPIs such as churn or cancellations and have the agent draft the SQL required to investigate the underlying data.

Reviewing Agent-Generated SQL

Use query citations and the SQL editor to inspect, verify, and modify the queries behind an agent's answer.

Working With Sensitive Data

Hide selected columns from the agent while continuing to use the available database fields for analysis.

Building Versioned Data Work

Store queries, dashboards, schema notes, and other artifacts in a Git repository so database analysis can be tracked and shared like code.

Joining and Exporting Larger Datasets

When a SQL-only workflow is insufficient, sandboxed code can handle additional joins and exports without requiring raw data to be sent back to the model.

Why It Matters

Giving an AI agent access to a database creates a useful automation opportunity, but it also raises questions about credentials, sensitive fields, query correctness, and control over the resulting work.

This workspace is built around those concerns. Credentials stay in the keychain, selected columns can be hidden, queries behind answers are visible, and the resulting work lives in a file-based workspace you control.

The result is an agent-assisted database workflow where the AI can help with the work without becoming the only place where your data analysis lives.

Bring AI Agents Into Your Database Workflow

This data workspace connects coding agents with PostgreSQL, MySQL, SQLite, ClickHouse, and MCP while keeping database work local, inspectable, and versionable. With plain-language queries, SQL generation, database memory, sandboxed code, access controls, and Git-based artifacts, it gives analysts and engineers a controlled way to use AI for real database work.