What it will take for AI agents to address real-time customer concerns
Real-world questions—i.e. “Where’s my order?”—require real-time answers pulled from live operational systems.
Real-world questions—i.e. “Where’s my order?”—require real-time answers pulled from live operational systems. But most teams are stuck wiring agents into fragmented APIs, exporting data into lakes, or building syncs with vector stores that quickly go stale.
On Episode 031 of Generationship, Snow Leopard founder and CEO Deepti Srivastava sits down with host Rachel Chalmers to discuss what’s missing—and how to bridge the gap.
Here’s what Deepti and Rachel discussed:
[02:20] Enterprise data realities
Every enterprise has a unique stack shaped by time, org structure, and tooling—making centralized, one-size-fits-all data solutions unworkable in practice.
[08:30] What Snow Leopard enables
Snow Leopard connects LLMs to live operational systems (e.g. Postgres, Salesforce, Stripe) via native queries, enabling accurate, real-time answers without data duplication or delay.
[10:23] The catalyst for Snow Leopard’s creation
Deepti shares an experience interacting with a customer support chatbot that couldn’t access her order status, forcing a 45-minute support call for a simple confirmation. This broken support workflow underscored the cost of disconnected systems and the need for on-demand data retrieval.
[14:19] The transition from PM to founder
Deepti shares how being a solo founder demands unwavering conviction, especially in early-stage ambiguity, and why product focus depends on saying no to distractions. She speaks to the importance of staying grounded in customers’ pain points.
[21:10] What’s next for enterprise AI
The three vectors that Deepti is watching closely: Language models for non-English languages, unstructured-to-structured transformation, and agentic reasoning over graph data.
[26:06] Critical thinking is the missing interface
From misinformation to youth mental health risks, Deepti argues that AI’s greatest societal threats require not just technical guardrails, but renewed emphasis on critical thinking.
[34:00] Making AI useful means making data usable
Deepti lays out a vision for generative AI as ambient, decision-support infrastructure—and why getting there requires systems like Snow Leopard that deliver timely, trustworthy data on demand.
Follow our journey by signing up for early access and connecting with Deepti on LinkedIn.
