<p>Octave, a provider of cloud-native quality management systems, has developed a specialized multi-tenant AI foundation to help enterprise customers analyze massive volumes of regulated data. Serving over 650 customers across the medical device, pharmaceutical, and food manufacturing sectors, Octave faced the critical requirement of ensuring that sensitive complaint records and audit findings remained strictly isolated between tenants.</p><p>Rather than deploying individual AI features in silos, the engineering team prioritized the construction of a horizontal platform designed to support future AI applications. This strategy, centered on Amazon Bedrock, reduced development cycles for new AI capabilities from months to weeks and cut deployment time per use case by approximately 50 percent, according to the company.</p><p>The architecture relies on a three-layer foundation. First, data preparation utilizes Cohere Embed v4 on Amazon Bedrock to index documents into per-tenant vector stores via Amazon OpenSearch Service. Second, the <em>AgentCore</em> runtime—a capability of Amazon Bedrock AgentCore—enforces hardware-level isolation by executing user requests in dedicated, sanitized microVMs. This approach ensures that data access remains tied to the specific user session rather than shared service roles. Finally, the system incorporates content protection through Amazon Bedrock Guardrails, which filter both input and output to mitigate risks such as prompt injection and unauthorized information disclosure.</p><p>To achieve the necessary throughput for onboarding large datasets, Octave implemented an event-driven pipeline capable of processing documents across diverse formats, including images, PDFs, and email threads. By leveraging the large context window of the embedding models and parallelizing processing via AWS Step Functions and Lambda, the company sustained a pipeline performance of 1.4 million documents per 24 hours per tenant.</p><p>The first production application built on this foundation, the Complaint Triage Assistant, has been in operation for three months. It automates the extraction and summarization of complex complaint records, allowing human quality handlers to focus on decision-making rather than manually searching through archives. Octave maintains that while the system surfaces context and institutional memory, human oversight remains a mandatory component of the quality management workflow.</p><p>The company plans to extend this infrastructure to support additional use cases, including audit readiness monitoring, incident narrative generation, and predictive quality risk scoring.</p>
Source: aws.amazon.com
Original article: https://aws.amazon.com/blogs/machine-learning/how-octave-built-a-secure-multi-tenant-ai-foundation-on-amazon-bedrock/
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