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Clean Collab

Build insights and AI models across organizations while keeping raw data fully private, governed, and within your AWS account.

Collaborate across boundaries with zero data exposure, full policy control, and automated pipelines ready for production scale.

The Problem

Organizations need to collaborate on data to generate insights and build AI models, but current approaches come with real trade-offs.

  • Sharing raw data across environments introduces security risks, compliance exposure, and loss of control
  • Replicating or moving data creates duplication, fragmentation, and governance challenges
  • Integrating data from multiple sources remains slow, manual, and unreliable

As a result, cross-organization data collaboration is either avoided or implemented in ways that are difficult to secure, govern, and scale.

Solution: Clean Collab

Clean Collab is a secure, governed approach to data collaboration that allows organizations to work together on data without moving or exposing raw datasets.

Instead of transferring data between environments, collaboration happens where the data already resides. Each organization retains full control within its own AWS account, while controlled, policy-driven queries enable joint analysis across datasets. Only approved, aggregated results are shared.

  • Data stays within each organization’s AWS environment : no movement, no duplication
  • Access and execution are governed through strict, policy-driven controls
  • Collaboration happens through secure, query-based interactions
  • Outputs are limited to safe, aggregated results
  • Pipelines are automated and ready for production use

This makes it possible to generate shared insights and build AI models across organizational boundaries, without compromising privacy, security, or control.

Where it Applies

Clean Collab fits scenarios where organizations need to work together on data but cannot afford to share or move it.

  • Cross-organization analytics: Combine datasets across organizations to generate insights without sharing raw data or creating duplicate pipelines
  • Joint AI model development: Train models using data from multiple sources while each organization retains full control over its data.
  • Regulated data environments: Enable collaboration in industries with strict compliance requirements—without exposing sensitive or restricted data
  • Partner and ecosystem collaboration: Work with partners, vendors, or clients to extract shared value from data without compromising ownership or governance