Data your team can't query doesn't exist.
Ragnerock creates queryable data from any raw source and connects it to your existing infrastructure.
Loading product demo...
Where data meets intelligence
Auto-accept edits
Jupyter
You
|
Go from question to insight to code—all in one conversation
How it works
Apply your analytical methodology at scale
Ragnerock consolidates ad-hoc AI pipelines into a single platform for data mining, analysis, and research.
- Your research methodology, applied to everything.
- Define exactly what to extract, how to analyze it, and what constitutes a valid output. The system applies that methodology to your data, producing validated results at scale.
- Instant answers from data you've already processed.
- AI extraction runs when data enters the system. Results persist as structured records, queryable with standard SQL or semantic search at millisecond latency. No LLM running at query time. Costs scale with data volume rather than query volume.
- Every conclusion is provable.
- Every output links back to the specific document, page, and passage it came from. Which operator, which model, which prompt version. The audit trail is structural, not reconstructed after the fact. Built for auditable, regulated environments.
- Nothing leaves your infrastructure.
- Outputs flow directly to Snowflake, Databricks, BigQuery, or PostgreSQL. Source documents stay in your cloud storage. Bring your own AI provider keys. Ragnerock adds the structured-data layer; everything else stays where it is.
Deploy intelligence against your data
Extract, structure, and query any data source. Results flow into your existing infrastructure.
