Turn the web into structured data—without scraping complexity.

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Data Rig builds production-grade data extraction systems that turn websites into clean, structured outputs.

No brittle scrapers. No manual parsing. No maintenance overhead.

Just usable data—ready for workflows, automation, and AI.

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Run a real browser as an MCP-native tool and extract data using natural language.

This is the foundation of Data Rig.

Instead of writing scrapers:

• describe what you want
• the browser handles navigation + interaction
• you get structured JSON back

This flips the model:

Old way	Data Rig way
Write selectors	Describe intent
Maintain scripts	Reuse workflows
Break on changes	Adapt dynamically

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Extract signals from websites → enrich → push into outbound systems

Monitor pricing, positioning, and changes across competitors

Aggregate and structure data across multiple sources

Feed clean datasets directly into agents, RAG, or automation

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• Automation → n8n, Zapier, Make
• Backend → Node, Python, Postgres
• AI → OpenAI, Claude, MCP-compatible agents
• Data → JSON pipelines, enrichment layers

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Data Rig starts with extraction.

It expands into:

• enrichment (emails, companies, signals)
• workflow-ready datasets
• agent-native data pipelines

The goal:

Any website → usable data → downstream action

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Data Rig is used in:

• outbound systems
• internal tools
• data pipelines
• AI-driven workflows

If it doesn’t work in production, it doesn’t ship.