Enterprise AI’s systems architecture problem
Instead of unlocking data, most AI initiatives recreate old problems: brittle pipelines, static snapshots, and expensive ETL jobs.
Most AI systems aren’t failing because of model quality. They’re failing because of the underlying architecture.
Instead of unlocking data, most AI initiatives recreate old problems: brittle pipelines, static snapshots, and expensive ETL jobs that are difficult to scale and harder to govern.
Snow Leopard founder and CEO Deepti Srivastava discusses this challenge—and her view for how to solve the problem definitively—on Episode 20 (Season 1) of Straight Data Talk:
Respect system diversity
Eliminate unnecessary complexity
Fetch live operational data, on-demand and in real-time
Drawing on two decades building distributed data systems, Deepti unpacks how Snow Leopard bridges the gap between AI and live, trustworthy business data without forcing enterprises to rewire their entire stacks. Discussion summary:
[01:50] Enterprise AI is an infrastructure problem, not just a model problem
AI systems are failing to deliver business value. It’s not because the models aren't good enough—it’s because the underlying architecture wasn’t designed for dynamic, real-time access to operational systems.
[07:30] Data silos are a fact of life—not a bug to eliminate
Centralized warehouses are important, but not sufficient. Operational data will always live across fragmented systems, and expecting complete centralization creates more friction than flexibility.
[15:25] Pipelines recreate the same complexity AI was supposed to fix
Most RAG pipelines simply replicate traditional ETL problems—introducing latency, brittleness, and missing context into AI systems that need up-to-date and semantic information to move forward.
[22:40] Snow Leopard’s query-first architecture for GenAI
Snow Leopard eliminates unnecessary data movement by building real-time queries natively using intelligent routing and query generation at runtime.
[29:18] Usability and governance must be solved together
Rather than centralizing everything and losing data lineage and fine-grained controls, Snow Leopard’s architecture is built to enforce access, permissions, and auditability directly at the data source—preserving security and governance without stifling AI agility.
[38:02] Reducing unnecessary complexity unlocks developer focus
Simplifying the AI-data integration layer frees teams to focus on building high-value, differentiated products—not endlessly maintaining brittle glue code.
Be sure to sign up for early access to Snow Leopard.
