AI agents need live business data, not more intelligence
Simply put, for AI agents to be part of critical / user-facing workflows, they need real-time access to the “crown jewels” of a business...structured operational business data.
Everyone’s talking about AI agents. But in spite of all the hype, not many AI agents have made it out of POC and into production, especially for critical business workflows.
Simply put, for AI agents to be part of critical / user-facing workflows, they need real-time access to the “crown jewels” of a business – structured operational business data. In most enterprises, this data is locked away in data silos, and often stuck behind brittle pipelines, stale snapshots, or vector stores that strip away business logic.
On Episode 099 of The Great Things with Great Tech podcast, Snow Leopard founder and CEO Deepti Srivastava sits down with Anthony Spiteri to unpack what AI agents really need in order to move beyond POCs—and how Snow Leopard enables them to do just that through live data retrieval, natively from any source.
Here’s a tactical summary of the discussion:
[15:30] The spark behind Snow Leopard
Even as companies race to prototype AI tools, few can deploy them in production. One of the big challenges is access to the right information at the right time to make the right decisions. Snow Leopard was built to solve this, making it possible for AI agents to have real-time access to structured, operational data.
[20:59] Why AI agents must live in the critical path
To drive business outcomes, AI agents need to be incorporated into critical business workflows, not sitting to the side or bolted on after-the-fact. This integration requires trustworthy infrastructure that connects them to systems of record.
[22:32] The hard part isn’t access…it’s relevance
Pulling data from a pre-defined lake or warehouse that aggregates information is hard but achievable. However, pulling the right data, that reflects current reality and isn’t stale, is a huge challenge. Snow Leopard dynamically fetches live data natively, by constructing native queries to get exactly what’s needed when it’s needed.
[27:11] Three truths Snow Leopard is built on
Data silos are a fact of life.
AI agents need access to live operational business data to be able to handle critical workflows.
Data consolidation in data-lakes with pre-defined pipelines isn’t the right solution.
[31:50] The limits of vector search
Vector databases are useful for semantic search, but are notoriously inaccurate for structured data and precise lookups. Snow Leopard is building intelligence around the data itself. It preserves precision by querying data sources directly and natively.
[34:34] How Snow Leopard works
Snow Leopard performs real-time, federated queries across operational systems—no ETL, no duplication. It builds native SQL or API calls at query time, tailored to each data source.
[36:20] The platform
Snow Leopard uses deterministic programming to generate native queries on the fly. Snow Leopard handles the logic, securely and at scale.
[37:50] Built for enterprise reality
Snow Leopard adapts to every customer’s real-world stack without requiring centralized warehouses or re-platforming.
[39:00] What’s ahead
Our team at Snow Leopard is only getting started. The goal: to power the next generation of AI-native enterprise apps by making business data instantly accessible, accurate, and actionable for LLM-powered agents.
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