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Product overview

DataFoundry is a local-first data analysis workbench. An AI agent connects the full flow from asking questions, understanding data, running read-only queries, explaining results, to preserving deliverables.

It fits teams that need to understand data quickly, validate metrics, and explore business questions. Users describe problems in natural language; the agent inspects table structure, generates and runs read-only SQL, and surfaces the analysis process and results.

Problems it addresses

Common friction in traditional data analysis:

  • Unfamiliarity with database schemas, leading to repeated lookups or documentation searches.
  • Clear business questions but no immediate ability to write correct SQL.
  • Opaque analysis processes that make conclusions hard to trust.
  • Results scattered across chat, SQL, tables, and screenshots, making review and export difficult.

DataFoundry puts these steps in one workbench: the user asks a question, the agent understands the data structure, runs queries within read-only boundaries, and shows tool calls, SQL, result tables, charts, or reports as traceable outputs.

Core workflow

Choose data source and model
  -> Ask an analysis question in natural language
  -> Agent inspects schema and plans analysis steps
  -> Run read-only queries or read relevant knowledge
  -> Show trace, SQL, tables, charts, and conclusions
  -> Export or reuse analysis outputs

Entry points

DataFoundry currently offers two main entry points:

Entry Best for Notes
Web workbench Product trials, customer demos, daily analysis Three-column UI suited for trace, outputs, and multi-task sessions.
TUI Terminal users, remote environments, script-friendly workflows Chat, configuration, stats, and export inside the command line.

The backend also exposes REST API and CopilotKit / AG-UI runtime endpoints for Web, TUI, and other clients.

Capability boundaries

DataFoundry emphasizes data safety and traceability by default:

  • Data queries run through controlled tools; there is no arbitrary SQL REST passthrough.
  • The agent must inspect table structure before executing SQL.
  • Queries are read-only by default, with SQL guard, row limits, timeouts, field masking, and audit.
  • Data source credentials are submitted only on create or update; read APIs do not return plaintext secrets.

Public docs cover local trials, open-source integration, development demos, and the built-in password-auth path. Production deployments still need deployment-specific access policy, centralized secret management, monitoring, and operations design. For a first trial, use the built-in DuckDB demo data source to experience the core flow.

Next steps