Cocoon

RAG for Large Data Warehouses

GenAI fails because enterprise data is large and messy with 1000+ tables.
Cocoon unlocks robust and scalable GenAI by cataloging your data warehouse for RAG.

Thoroughly profile, clean, and document data with human verification
Leverage existing knowledge from pipelines, documents, and BI objects
Continuously monitor, maintain, and optimize your data + pipelines
Provide RAG for reliable GenAI apps like Pipeline Copilot and Self-Service BI

Based on Cocoon RAG, we provide cursor-style chatbot for ...

Large Data Pipelines

Model Discovery: Find the models related to churn.
Pipeline Optimization: Recommend partitions based on usage.
Pipeline Update: Rename columns and update downstream models.
Pipeline Prototype: Draft a model for customer retention.

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Large Data Warehouse Tables

Data Discovery: Explore tables related to customer churn.
Report Prototyping: Create MRR report for products.
Feature Extraction: Extract features for customer segmentation.

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Both Pipelines and Tables

Data Integration: Integrate Salesforce and Hubspot customer tables into one.
Database Migration: Migrate from Hubspot to Salesforce database.
Report Reconstruction: Recreate Hubspot models, but use Salesforce data.

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Cocoon is open-sourced

Explore other features of Cocoon

Profile

Semantically understand your table and identify anomalies.

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Clean

Clean, format and document your tables with SQL and YAML.

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Standardize

Standardize column values to given vocabularies.

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Catalog

Catalog your data warehouses into easy-to-understand forms.

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Tranform

Transform from source to target databases with SQL.

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GenAI

Build a semantic catalog to organize your data, ready for GenAI.

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Lineage

Learn your data semantically from existing data pipelines.

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Pipeline

RAG large data pipeline using lineage

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