AI Dev 26 x SF: Emma McGrattan: Engineering the Context Layer

At AI Dev 26 x San Francisco, Emma McGrattan, CTO at Actian, explored the critical role of the "context layer" in deploying enterprise-grade AI applications. She addresses how to engineer data layers that reliably ground Large Language Models (LLMs) in a company's unique business reality at scale.12 Key takeaways from the presentation include: The Necessity of RAG: LLMs are stateless and lack specific business knowledge; Retrieval-Augmented Generation (RAG) uses vector databases to provide the necessary semantic context for grounded AI responses. Architectural Pressures: Regulatory requirements, the need for sub-millisecond latency in real-time decisions, and "data gravity" from hundreds of internal sources often make cloud-only solutions insufficient. Choosing a Topology: Cloud: Offers elastic scale and global reach but faces challenges with latency and data egress costs. On-Premises: Necessary for industries like financial services, healthcare, and defense where data sovereignty and security regulations (e.g., HIPAA, PHI) are paramount. Edge: Vital for millisecond-level decision-making and environments with spotty or no connectivity. The Future is Hybrid: Enterprises should design for hybrid architectures that use intelligent query routing to send workloads to the most appropriate tier—cloud, on-prem, or edge—based on data sensitivity and latency needs. Coming Innovations: The next 12 to 18 months will see the rise of multimodal retrieval (audio, image, time series), AI-driven index management, and governance-aware retrieval. McGrattan concluded by emphasizing that the context layer is "load-bearing" for the modern enterprise and introduced Actian's new vector database designed for on-premises and edge deployment.