Beyond ETL: Why scalable AI systems need live operational data

Live data access is the only scalable way to support production-grade AI systems.

Enterprise AI systems are built on architectures that prevent timely, accurate responses: vector stores that rely on stale embeddings, static snapshots that fail to reflect real-time activity, and brittle pipelines that are costly to maintain.

On Episode 261 of The Data Exchange and in a recent edition of the Gradient Flow newsletter, Snow Leopard founder and CEO Deepti Srivastava joins Ben Lorica to unpack this growing problem and share why live, structured data is essential for next-gen AI—and the approach that Snow Leopard is taking to solve the problem.

Here’s what the discussion covered:

[01:07] Connecting AI to business value
Without real-time access to sources like CRM, finance, and product systems, LLMs are disconnected from the workflows they’re meant to support.

[04:29] From Spanner to Snow Leopard
Centralized architectures and batch-based pipelines create brittle systems. Drawing on her experience building Spanner at Google, Deepti explains why live access—rather than ETL—is the only scalable way to support production-grade AI systems.

[08:07] The limits of RAG
RAG systems degrade under real-world use. Once data is embedded, it’s already stale. Structured data gets flattened, freshness is lost, and fuzzy matching introduces critical inaccuracies, particularly in high-trust domains like finance and operations.

[15:02] What Snow Leopard does differently
Snow Leopard eliminates pipelines entirely. It sits between your agents and data systems, performs intelligent query parsing and routing, and retrieves only the precise rows and fields needed—on demand, from the original source.

[25:07] Why building it yourself doesn’t scale
Deepti explains why teams quickly hit a wall trying to DIY what Snow Leopard handles by design.

Check out what we’re building and sign up for early access @ snowleopard.ai.