Radical Whale is a data workspace that lets you define datasets, attach AI-driven columns, and run enrichment or research workflows using your LLM keys, your APIs, and your tools.

You can: • Create datasets with AI columns that compute values from instructions • Build custom agents that call any API or MCP tool (Tavily, GitHub, Apollo, Diffbot, etc.) • Mix text, datasets, and agent calls in TipTap-powered notebooks • Run workloads in isolated per-workspace queues for predictable performance

I built this because tools like Attio, Notion, and Freckle hide the real agent calls behind credit systems and markup. By calling models and APIs directly, you can operate these workflows far more efficiently and transparently.

If you work with structured data, enrichment, or AI automation, I’d appreciate feedback on the approach.