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

Why It Matters

Without a robust context layer, enterprises cannot trust LLM outputs for critical decisions, risking compliance breaches, latency failures, and lost competitive advantage.

Key Takeaways

• LLMs require a business-specific context layer for accurate answers.
• Regulatory, latency, and data gravity drive hybrid AI deployment choices.
• Cloud offers scale, but on‑prem and edge meet sovereignty and speed needs.
• Retrieval‑augmented generation relies on vector databases for contextual grounding.
• Future vector stores will support multimodal, governance‑aware retrieval at scale.

Summary

Emma McGrattan, CTO of Actian, explains that large language models (LLMs) lack any knowledge of an enterprise’s specific data, making a dedicated "context layer" essential for delivering business‑relevant answers. She frames the problem as engineering a data layer that can reliably feed LLMs with proprietary information, turning generic AI outputs into actionable insights.

The talk highlights three pressures reshaping AI architecture: regulatory mandates that force data to stay within sovereign or on‑premise boundaries; latency requirements that demand sub‑millisecond decisions, especially in fraud detection or autonomous systems; and data gravity, where enterprises juggle hundreds of disparate data sources across clouds, mainframes, and SaaS platforms. These forces push organizations toward hybrid deployments—cloud for elastic scale, on‑prem for compliance, and edge for ultra‑low latency.

McGrattan illustrates the concept with a retrieval‑augmented generation (RAG) example: an insurance customer asks why their premium rose, and a vector database pulls relevant policy, claim, and location data to ground the LLM’s response. She also cites edge scenarios like badge‑scanning devices that must operate offline during storms, and sovereign cloud offerings that keep data within regional borders. The choice of where the vector store lives directly impacts response time and cost.

The implication is clear: a one‑size‑fits‑all AI stack no longer works. Enterprises must design intelligent query routing—sending regulated data to on‑prem, latency‑critical queries to edge, and fresh, large‑scale analytics to the cloud. Actian’s new on‑prem/edge vector AI database and the upcoming shift to multimodal, governance‑aware retrieval signal that the context layer will become a core, load‑bearing component of future distributed AI systems.

Original Description

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.