Announcing Snow Leopard on Discord: See Snowy in action!

We’re excited to announce the launch of Snow Leopard AI’s Discord server! 🎉

Now you can see Snow Leopard in action on Discord and play around with open-source SQL datasets to get a sense of how Snow Leopard can help you build business-critical enterprise AI agents!

Try Snow Leopard on Discord!

So many AI demos out there stop short of the real challenge: using structured operational data reliably, securely, and in real time in agentic applications and workflows. With our Discord server, you’ll see firsthand how Snowy (Snow Leopard’s Discord Agent) addresses these issues.

LLMs and Structured Data Don’t Mix (Well)

LLMs are great for unstructured text — summarizing documents, answering questions, chatting with PDFs, etc. But, as I shared in an earlier post, they are notoriously bad at working with structured data [eg: 1, 2].

And yet, the crown jewels of a business — customer data, product insights, inventory, financials — live in these structured data systems like SQL databases, data warehouses, and APIs (Salesforce, HubSpot, Stripe, etc).

Today, developers building AI agents that need this data have to deal with:

• Accuracy – Decision-making workflows need precise, specific, accurate information. LLM hallucinations and probabilistic responses just don’t cut it, so most AI agents don’t make it to production.

• Reliability – Business-critical workflows need deterministic, consistent outputs. This includes saying no, when the information doesn’t exist or isn’t available. It is horribly complex for developers to deal with LLM-induced non-determinism when building agents that are meant to run business workflows reliably.

• Freshness – Making real-time decisions requires real-time information. Stale data (from data dumps or RAG pipelines) can lead to bad calls, missed opportunities, or compliance issues.

In addition, none of the current state-of-the-art solutions (MCP, RAG, etc.) solve the problem fully.

If you’ve ever tried to use production databases in an AI application, you know that MCP + Text2SQL just doesn’t cut it for critical workflows. Building an MCP server and hooking it up to an LLM’s tool-calling workflow is easy. The hard part is getting that setup to actually give you correct, consistent and timely answers. It requires weeks or even months of iteration just to get to 60-70% accuracy.

RAG-based solutions can’t help with accuracy and real-time data needs either. After all, they’re similar to ETL systems – stale snapshots of data that has been transformed to fit in a vectorized format, losing context and accuracy in the process.

A fundamentally different approach is required.

Data dumps and ETL to data lakes means not only is the data stale, sometimes it doesn’t even exist in the data lake to answer questions

Why Snow Leopard Is Different

At Snow Leopard, we go beyond plumbing together connectors and MCP, and focus on building intelligence about the data itself — the relationships within and across data sources, the business context, and the rules that make the data meaningful. We call this semantic business logic.

This means Snow Leopard doesn’t just know how to query a database. It knows what data to fetch, from which database, and when.

We do this by combining:

• Decades of experience building large-scale enterprise infrastructure.

• Deep expertise in structured data and Applied AI.

• Relentless focus on simplicity and usability.

As infrastructure engineers ourselves, we believe strongly that solving hard problems isn’t enough. The solution has to reduce developer burden and be easy to use.

What This Means for Agent Builders

Snow Leopard is designed for agent builders who need to integrate structured data into decision-making workflows without drowning in complexity.

Here’s what Snow Leopard provides:

• Simple APIs – Use databases directly with your decision-making agents without worrying about brittle pipelines or complex, continuous ETL.

• Live queries – Queries are built in real-time and data is retrieved directly from the source system, meaning it's never stale.

• Governance and security – Only fetches data it has access to, and never stores / caches data. Data access is managed directly from source systems, on demand.

• Deterministic behavior – Explicitly denies requests when the data being asked for doesn’t exist or isn’t accessible. Agents can be built in a simple, reliable way instead of dealing with the complexity of non-deterministic LLM behavior.
  

Most AI agents stall at the POC stage primarily because they’re built on inconsistent and inaccurate foundations. Snow Leopard makes it feasible to build production-grade agentic software.

Snow Leopard gives clear deterministic answers vs. current approaches that give a range of probabilistic responses, which leads to ambiguity rather than clear information for decision making

See It in Action on Discord

To try Snow Leopard for yourself, join our Discord server!

You’ll notice that Snowy focuses on three things:

• Consistency — deterministic, repeatable responses.

• Accuracy — says no when the data isn’t there or the question doesn’t fit.

• Freshness — data is always fetched at query time.

We believe these are key attributes for anyone building enterprise AI agents.

Note that Discord is just the interface we’re using to showcase Snow Leopard’s capabilities. Snow Leopard itself is a conversational API that specifically does the heavy lifting of on-demand structured data retrieval.

Snow Leopard gives builders the confidence to ship AI agents that make real-time, data-driven decisions.

If you’re ready to bring Snow Leopard into your business, we’d love to help you get the most out of your operational data for your AI agents!

Contact us: hello@snowleopard.ai

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