Data Bench transforms any SQL database into an AI-native data layer. This enables AI clients to intelligently query and interact with your data, leveraging a sophisticated multi-layered knowledge system.

How does Data Bench learn about my data?

It utilizes a 4-layer knowledge system comprising schemas, query templates, guardrail rules, and automatically learned patterns. It learns and saves validated query patterns from real user sessions with explicit human approval, continuously improving its understanding.

What database backends does Data Bench support?

Data Bench offers pluggable backends, currently supporting DuckDB for local, zero-configuration setups and AWS Athena for serverless SQL querying against your AWS Glue catalog.

Is Data Bench safe to use with my production data?

Yes, Data Bench includes robust safety mechanisms such as SELECT-only operations (blocking INSERT/UPDATE/DELETE), configurable row limits, error sanitization, and a human-in-the-loop approval process for all learned patterns.

How do I integrate Data Bench with my AI client?

Data Bench functions as an MCP (Meta-programming Client Protocol) server. You can integrate it by configuring your AI client (e.g., Claude Code, Cursor) to connect to the Data Bench server via stdio (JSON-RPC), allowing the AI to use its provided data tools.

Data Bench: AI-Native Data Layer for SQL Databases

Data Bench empowers AI clients to interact seamlessly with SQL databases by acting as an intelligent intermediary. It converts existing SQL databases into an 'AI-native data layer,' allowing large language models to query, understand, and learn from data. The tool features pluggable backends like DuckDB and AWS Athena, along with a sophisticated 4-layer knowledge system that includes manual schema documentation, query templates, guardrail rules, and automatically learned patterns from real user sessions. This system enables the AI to progressively understand your data model, write more accurate queries, and streamline data exploration, all while ensuring safety through restricted operations and user validation.

主な機能

01Converts SQL databases into an AI-native data layer for intelligent querying by AI clients.

02Supports pluggable backends including DuckDB (local, zero-config) and AWS Athena (serverless SQL).

03Utilizes a 4-layer knowledge system: schemas, query templates, guardrail rules, and automatically learned patterns.

04Automatically learns and saves validated query patterns from real user sessions with explicit human approval.

05Includes built-in safety mechanisms such as SELECT-only operations, configurable row limits, and error sanitization.

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ユースケース

01Enabling AI clients (e.g., Claude Code, Cursor) to intelligently query and analyze data from SQL databases.

02Building a continuously learning data interface for AI assistants that adapts to specific data models and user patterns.

03Streamlining data exploration and report generation through AI-assisted SQL query construction and validation.