AI-native research intelligence for capital markets.
A unified layer for AI-powered data ingestion, parsing, and analysis that sits on top of your existing data lake.
Built for quantitative research
Your team's research capability, transformed.
Ragnerock replaces the sprawl of bespoke, siloed AI pipelines with a single platform for your entire research data workflow.
- Composable AI workflows.
- Define multi-step analytical pipelines as DAGs of AI agents. Each operator has a strict data contract — what it receives, what it produces, validated against JSON Schema. Go beyond simple extraction: segment documents, run specialized analysis per section, call external tools, benchmark results. The entire flow is declarative and versioned.
- Process once, query with SQL.
- AI extraction runs when data enters the system. Results persist as structured annotations, queryable with standard SQL or semantic search at millisecond latency. No LLM running at query time. Costs scale with data volume, not query volume.
- Full provenance on every output.
- Every annotation 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 environments where capital is at risk and regulators ask questions.
- Sits on your data lake.
- 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 intelligence layer; everything else stays where it is. Notebook integration with Jupyter means your quants keep working in the tools they already know.
Turn artificial intelligence into genuine knowledge.
Unlock insights and transform your data into structured, queryable intelligence.
