AI’s true platform shift + 3 spicy takes
AI keeps promising to deliver autonomous reasoning systems...that's not the reality though
AI keeps promising to deliver autonomous reasoning systems.
That’s not the reality though.
While the platform shift is clearly there, LLM-powered agents still don’t get the right information at the right time from the crown jewels of your business i.e. your operational, structured data systems—warehouse, lake, ocean, whatever.
Bolting AI onto the side of your existing technology stack isn’t a long-term success strategy.
Also, RAG doesn’t cut it for these types of real-time decision-making agents. When data is transformed and offloaded into a vector store, it breaks the logic those systems were built to preserve. Structure disappears. Context dissolves. Precision is lost.
Snow Leopard solves this with real-time, deterministic access to live data, queried directly from source systems at the moment an agent needs it to make progress. That’s what makes the reasoning system accurate and reliable.
Snow Leopard founder and CEO Deepti Srivastava explains more in episode 325 of the MLOps podcast.
Here’s what Deepti and host Demetrios Brinkmann discussed:
[02:40] Connecting LLMs to operational data
To deliver on their platform-shift promise, LLMs need access to live, operational business data—structured data from SQL, NoSQL, and APIs.
[04:50] The AI/data disconnect
There’s still a massive chasm between operational data and LLM-based apps. Most agents are being built to chat with your PDF or your notion docs etc. not your databases in real-time.
[08:43] Context is key
Your tech stack operates in an ecosystem. Any new technology has to exist within that ecosystem. Bolting it on as a side-car isn’t going to get you the fundamental platform shift that everyone is talking about.
[11:00] Spicy take #1
Putting all your data in a data warehouse, lakehouse, ocean etc. doesn’t actually solve the problem. That data is stale, transformed thereby losing original context, and doesn’t help AI Agents make real-time and accurate decisions.
[11:46] Spicy take #2
Even the most intelligent machines today—human beings—don’t make the right decisions without the right information. Intelligence and enhanced reasoning on their own aren’t enough. Why would we expect AI to be different in that regard?
No AI system will make the right decisions without the right data, at the right time. Better reasoning won’t fix stale and broken inputs.
[12:30] Model hallucinations 201
Hallucinations happen when LLMs have the wrong data or too much data where they can’t figure out what to focus on. When the system can’t distinguish what matters, it guesses.
[16:00] LLM strengths and limitations
LLMs are useful for fuzzy interpretation, summarization, and classification. But they fail at precision tasks like point lookups or definitive yes/no answers.
[17:00] Data needs context
Your business’s crown jewels – data in systems like Postgres, Snowflake, Google BigQuery Salesforce, HubSpot, and other APIs–lose meaning when extracted. Stripped from context, the story gets lost.
[24:00] Shifting engineering time from clean-up to value creation
Today, data engineers spend 70–80% of their time maintaining brittle pipelines. What if you could drop a box between your operational systems and your LLMs and just draw straight lines? That’s the shift: from complexity to creative innovation.
[28:00] What if you didn’t move the data?
What if you could skip the pipelines entirely and just query data directly from the source, in real time? No movement. No duplication. Just a live connection that fetches the data when the question is asked.
[32:00] No pipeline required
Snow Leopard will pull from multiple systems and return an answer without having to build a new pipeline or engineering a custom solution to accommodate every new use case or workflow. Ad hoc retrieval for ad hoc use cases.
[36:44] Spicy take #3: MCP doesn’t solve it
MCP is amazing. It’s a great open-source start to the connector problem. But MCP isn't a magical solution. It’s not tackling the hardest part of the problem, which is intelligent routing and, more importantly, understanding the semantic context around the data.
[39:00] SQL ≠ SQL
Not all SQL is the same. Mixing queries across dialects, like MySQL and Snowflake, breaks systems and confuses LLMs. That’s one reason text-to-SQL doesn’t work in practice. Generating a SQL query in the correct dialect requires additional effort and an inherent understanding of the underlying data systems.
[46:00] Ad hoc questions need ad hoc retrieval
In a world of ad hoc questions and on-demand information needs, why shouldn’t data retrieval be ad hoc too? Using pre-defined pipelines to solve every use case just doesn’t work and won’t scale.
[54:00] The real challenge with AI: POC to production
The hard part is still getting from proof of concept to production. Teams build cool demos, but can’t deploy them because of reliability and accuracy issues. Performance isn’t even the blocker yet. It’s just about making it work consistently and correctly.
That’s what we’re focussed on at Snow Leopard–accurate consistent data for alI!
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