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Octave built a shared foundation for AI features on the Reliance platform with customer data isolation

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Octave built the foundation for AI features on the Reliance platform using Amazon Bedrock and Cohere Embed v4. The architecture isolates customer data and user permissions. According to the case study, it reduced deployment time for individual use cases by 50 %.

Octave built shared infrastructure for the Reliance platform, intended to support various AI features for quality management in regulated industries. The first production deployment was Complaint Triage Assistant, a tool for summarizing and classifying complaints. According to the case study on the AWS blog, the shared foundation reduced the deployment time for one use case by 50 %.

The architecture has three layers. Data preparation uses Cohere Embed v4 through Amazon Bedrock and stores vector representations of documents in a separate store for each customer in Amazon OpenSearch Service. The request execution layer uses AgentCore Gateway for authentication and policy enforcement before launching an isolated microVM session. Permissions are intended to match the specific user and apply only during execution. Amazon Bedrock Guardrails filters both inputs and outputs.

According to the article, the system generated embeddings for 2 billion documents. Customer data isolation covers the vector store, search, and the context passed to the model. The records processed include complaints, corrective and preventive actions, and audit results. Details are available in the source article.

What changed

Why it matters

The case study offers a concrete architectural pattern for platform providers looking to add AI features that work with sensitive data from multiple customers. It shows where to enforce data isolation, user permissions, and content controls. The reported reduction in deployment time is a result of this implementation, rather than a proven guarantee for other projects.

Relevant practical impact

What this means

01

For a business

For development teams building platforms that serve multiple customers, the case study documents the use of shared infrastructure across AI features. According to the article, this approach reduced the deployment time for one use case on the Reliance platform by 50 %; it also consolidated data isolation and permission rules into a shared foundation.

Development
What to decide When designing a similar platform, verify that customer data isolation applies across the vector store, search, and model context.
More business impacts →
Amazon Bedrock Cohere Embed v4 Complaint Triage Assistant Octave Reliance

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Event sources

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AWS Machine Learning Blog primary source · first detected How Octave built a secure multi-tenant AI foundation on Amazon Bedrock