Ben Kus, CTO of Box, delves into the complexities of the company's three-layer infrastructure: managing millions of interactions, navigating multi-tenant security challenges, and ensuring AI adheres to intricate content permissions. He discusses the impact of vector embeddings on file sizes and emphasizes the continued relevance of RAG despite advancements in context windows. The conversation also highlights Box's development of AI agents aimed at streamlining cumbersome enterprise processes, creating a path to improved productivity in the workplace.
Box's immense scale necessitates a robust infrastructure capable of handling millions of interactions per second while ensuring data reliability and security.
The unique multi-tenant system at Box requires innovative solutions for secure content sharing across enterprises, challenging traditional security models in SaaS platforms.
AI agents are essential to streamline enterprise processes like RFP responses, enhancing user productivity while maintaining strict adherence to security protocols.
Deep dives
Box's Scale and Infrastructure Challenges
Box operates at an enormous scale, serving over 120,000 enterprises and handling data in the exabyte range. This immense volume presents significant challenges in maintaining efficient storage, retrieval, and organization of unstructured content. The infrastructure must support millions of interactions per second while ensuring reliability and security. Box has developed advanced capabilities and architectures to manage this growth while allowing users to access and collaborate on specific documents seamlessly.
The Complexity of Multi-Tenant Systems
A significant challenge for Box lies in its multi-tenant system, where users from different enterprises may need to share documents securely. The need for individualized permissions and classification policies complicates how documents are shared and retrieved across different accounts. Unlike many SaaS products that can simply segregate content per tenant, Box's design must account for complex sharing scenarios. This requires innovative solutions that balance user access with strict security measures to properly manage shared content.
AI Integration and Security Considerations
Box aims to leverage the latest developments in AI while ensuring that user permissions and security protocols are respected. As new AI models emerge regularly, it is essential for Box to integrate these capabilities into their platform without compromising data security. Implementing AI securely at scale presents its own set of challenges, necessitating a robust framework that accommodates rapid advancements in AI technology. The priority is to maintain a reliable system that empowers users to maximize AI without risking data integrity.
The Evolution of AI Agents
AI agents are becoming integral to Box's operations, enabling users to automate repetitive tasks, efficiently query data, and generate content. These agents can streamline complex processes, such as RFPs, by fetching relevant information, drafting responses, and editing documents based on user preferences. As enterprises recognize the value of AI agents, their capabilities are expected to evolve, transforming from simple query assistants into complex collaborators that facilitate a wide range of tasks. The ultimate goal is to create a seamless interaction between users and AI, allowing them to focus on higher-value work.
Optimizing Embeddings for Cost and Efficiency
Storing embeddings efficiently is crucial for managing costs at Box's scale, as each embedding can significantly increase storage requirements. The approach involves calculating embeddings selectively, focusing on the most relevant documents and utilizing intelligent systems to minimize costs without sacrificing performance. Techniques such as chunking documents into meaningful segments help ensure that retrieval processes yield high-quality results without overwhelming the infrastructure. Optimizing these systems will not only enhance performance but also keep expenditures manageable as data continues to grow.
Ben walks us through Box's three-layer infrastructure puzzle: First, the mind-boggling base infrastructure (think millions of interactions per second and trillions of files). Second, their unique multi-tenant security challenge - unlike most SaaS platforms, Box users share content across company boundaries, making traditional tenant isolation impossible. And third, ensuring AI respects all these complex permissions while still delivering value. The podcast then dives further into how vector embeddings can balloon file sizes - a few hundred bytes of text can require 4-6KB of vector data storage! We also dig into why RAG remains essential despite growing context windows, and how Box is developing AI agents that transform painful enterprise processes like RFP responses.
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