This Week's Focus ⤵️
Good morning and happy Friday.
Are you being watched by your favorite LLM?
Can you switch models without rewriting your workflows?
Many AI offerings still create avoidable risk: unclear retention terms, shared limits, and integrations that hardwire your workflows to a single vendor.
The more you embed one model into your operations, the harder it gets to switch.
The Quick Summary: The era of relying on a single AI provider is over. With the production rollout of Gemini and xAI, Sprinklenet Knowledge Spaces now offers a fully model-agnostic control plane, allowing you to route data to the best model for the job while maintaining absolute control over your proprietary information.
The Interface Layer 🖥️
I’ve written before about how Chat is becoming the Interface Layer for the modern enterprise. But for too long, that interface has been constrained by the limitations of single-model dependencies.
From day one, we architected Sprinklenet Knowledge Spaces to be fundamentally model-agnostic. We’ve always believed that your business logic shouldn't be hard-coded to a specific vendor's API. That’s why we’ve long supported OpenAI, Anthropic (Claude), and the high-speed inference of Groq.
Today, we’re expanding that ecosystem.
We’re announcing full production support for Google Gemini and xAI (Grok).
As the model landscape diversifies, our architecture ensures you’re ready to leverage the specific strengths of every major player without rebuilding your infrastructure.
Why Middleware Matters
(More Than the Model) 💡
The reason why middleware like Spaces is critical is simple: Separation of Concerns.
In a direct-to-model integration, you’re often handing your data and your prompt over to a black box. You surrender control over how it's processed.
Sprinklenet Spaces sits in the middle. We act as the governance layer—the "enforcement shim" that empowers you to control the interaction.
You control the context: The platform enables you to define exactly what data gets retrieved from your vector database.
You control the prompt: Spaces enforces your specific guardrails before the model ever sees the query.
You control the routing: You decide which model answers the question based on cost, speed, or capability.
This allows you to build Technical Bots—automated agents that don't just "chat" but trigger API calls, review invoices, or query legacy SQL databases—without being tied to a specific LLM's capabilities.
New Additions: Gemini + Grok, Plus How We Route Across Providers
You might ask if adding these models is worth the headline. In 2026, absolutely.
1. Google Gemini (The Multimodal Engine) We aren't just talking about text anymore. Google’s Gemini models have native multimodal capabilities that are stunning for technical use cases.
The Use Case: Imagine a Technical Bot for a field services team. A technician uploads a photo of a broken part or a video of a site inspection. Gemini doesn't just "see" it; it reasons about it. It can cross-reference that visual input against your PDF technical manuals stored in a Knowledge Space and output a repair protocol.
The Benefit: For organizations deep in the Google Workspace ecosystem, this integration offers latency and compatibility advantages that streamline workflows.
2. xAI / Grok (The Frontier Hedge) While the public focus is often on xAI’s consumer persona, the underlying model is a serious frontier-class reasoner.
The Use Case: Diversity of thought and "Sovereignty of Intelligence." We are seeing clients who want a "second opinion" on complex compliance questions. You can now configure a bot to run a query through GPT-5 mini for the primary answer and xAI for a red-team critique.
The Benefit: It prevents vendor lock-in. If one provider changes their safety policy or faces regulatory hurdles, you can switch your reasoning engine to xAI in the Sprinklenet dashboard instantly.
3. Groq (The Velocity of Thought) Groq isn't a model; it's a speed demon. By running open-source models (like Llama) on Groq's LPUs, we are seeing inference speeds that are measured in milliseconds, not seconds.
The Use Case: Real-time voice bots and high-frequency data processing. When you need to process 10,000 document snippets for a migration project, you don't need a heavy reasoning model; you need speed. Groq unlocks use cases that were previously too slow to be viable.
Who shall guard the guardians themselves?
"Bring Your Own Key" (BYOK):
Governance & Scale 🏛️
With this update, we’re doubling down on our BYOK approach. You plug your enterprise API keys (for OpenAI, Anthropic, Google, etc.) directly into your Knowledge Space settings.
Why is this the superior model for the enterprise?
Data Sovereignty & Policy Inheritance: This is the critical governance piece. When you use your own key, the data traffic flows under your specific enterprise agreement with the model provider. If you have negotiated a "Zero Data Retention" (ZDR) clause with OpenAI or Google, using your own key ensures that policy is strictly enforced. We act as the secure processor, but the legal standing regarding the "raw intelligence" remains between you and the provider.
Rate Limit Leverage: Large enterprises often negotiate higher rate limits and tiers with providers. BYOK allows the Sprinklenet middleware to inherit these negotiated benefits.
Cost and Chargeback Clarity: Usage stays aligned to the vendor agreement, billing, and internal allocation model.
Whether you prefer to leverage your existing strategic partnerships or tap into our pre-configured, enterprise-grade LLM defaults, we provide the infrastructure to get you scaling immediately.
Building the Builders:
Local Claude Code ⚙️
Finally, a quick note on how we’re building this.
We believe in "dogfooding" agentic workflows. Recently, our engineering team has shifted to running Claude Code locally on our development machines. This is an agentic coding assistant that lives in the terminal. I’m running Claude non-stop on Claude Max.
It’s allowing us to build the secure "overlay layers"—the custom API connectors that sit on top of your legacy applications—at a pace that feels like magic. By running it locally, we keep our IP secure while leveraging state-of-the-art reasoning to refactor complex codebases.
We’re essentially "building the machine that builds the machine," which is why we can now spin up a custom Knowledge Space MVP for a client in under 30 days.
Closer to Alignment 🤝🏼
This multi-model world is complex, but it offers unprecedented control if you have the right architecture.
We have just published a new non-technical whitepaper: "Sprinklenet Knowledge Spaces: Controlled, Auditable AI for the Enterprise."
It covers:
The business logic behind Technical Bots vs. Chatbots.
How to monetize your proprietary data securely.
The architectural diagram of a Zero Trust AI deployment.
Reply to this email with "whitepaper" if you want a copy, and I’ll send it over.
A Note From Jamie
We’re well underway into 2026, and I think this is the year AI middleware becomes a standard layer in enterprise systems. Governance and reliability matter, and LLM behavior is still unpredictable enough that you need a control plane. The single-model era is ending, not because any one model is bad, but because relying on one provider for mission-critical work creates avoidable risk. Pricing changes, policy shifts, outages, and regressions happen. If your workflows are hardwired to one model, every change turns into a disruption.
If you want a quick walkthrough of routing, BYOK, and audit logging inside Spaces, reply and I’ll send a short demo tailored to your use case.
- Jamie Thompson
