Large Language Models (LLMs) have sparked a revolution in recent years. What began as an academic curiosity has now become a cornerstone technology, seamlessly integrated into the daily routine of countless businesses.

Today, LLMs are almost as ubiquitous as commodities, with a wide array of options — both open-source and proprietary — available for sophisticated natural language processing. But here’s the catch: the real differentiator isn’t just the choice of LLM, but how you orchestrate the entire ecosystem of models, agents, security, and governance.

The evolution of LLMs

Tech giants like OpenAI, Google, Microsoft, and Anthropic have made their Large Language Models accessible through well-documented APIs. Meanwhile, the open source community has been bustling with initiatives like the LLaMA family of models and derivatives on Hugging Face. This explosion of options has led to greater diversity and competitiveness in terms of processing power, speed, and accuracy.

According to a Vertesia report, 90% of senior tech professionals believe LLMs would bring significant value to their organizations.

However, while generic LLMs are great for broad tasks, they might not always be the best fit for specific challenges. Industries like healthcare, telecommunications, and financial services (FSI) face stricter requirements—think encryption in transit, anonymization of sensitive data, content classification, prompt sanitization, and placeholder management. This is where a mix of solutions comes into play.

Multi-LLM and SLM Orchestration

Not all LLMs are created equal. Some excel in certain areas while others fall short. That’s where Small Language Models (SLMs) come in. These models are trained for specific domains, like law and medicine, and can handle niche tasks—like summarizing patient records—with ease.

SLMs are leaner and faster, often with millions or a few billion parameters, compared to the hundreds of billions (or even trillions) in larger models. So, why settle for one generic LLM when you can tap into a suite of specialized models, each tailored for a specific need?

This is where platforms like StackSpot AI shine. Instead of replacing LLMs, StackSpot AI integrates them with SLMs, combining their strengths in a unified, intelligent way. The platform acts as an orchestrator, dynamically selecting the best model for each subtask—all while ensuring security and compliance with regulations like LGPD.

The Importance of Solid Security and Governance Processes for Orchestrating LLMs and SLMs

AI adoption isn’t just about capabilities — it’s also about trust. Data security and vulnerability management are non-negotiable. Every LLM and SLM must be reliable to prevent leaks or breaches. Here’s what you need to consider:

• Encryption in transit and at rest: Protect critical information during transmission and storage.
• Anonymization and pseudonymization: Mask sensitive data, exposing only what’s necessary.
• Data classification: Label and categorize information based on confidentiality levels, applying appropriate access policies.
• Prompt sanitization: Prevent malformed or malicious prompts from leaking private data or causing unwanted model behavior.
• Smart placeholders: Replace sensitive terms with controlled placeholders, maintaining context without compromising security.

A robust governance system is equally crucial. It should manage logs, authorizations, model versions, and data, especially in complex scenarios like those in banking or healthcare.

Governance and LLMs

Beyond integration and security, governance ensures traceability, granular access controls, and compliance with industry-specific regulations. Again, this is particularly critical in sectors like finance and healthcare, where AI adoption is tightly regulated.

The challenge is significant, but so is the payoff. A well-governed AI system can unlock new levels of efficiency while keeping risks in check.

Multiagents: Virtual Specialists

Another exciting trend is the rise of multiagent systems (AI multiagents). These platforms allow you to create multiple AI agents, each specialized in a specific task—like optimizing code, sorting data, or analyzing legal documents.

Instead of relying on a single LLM to handle everything, multiagent systems distribute tasks to the most suitable models. This approach ensures optimal efficiency for each use case.

According to Gartner, by 2028, at least 15% of decisions will be made by AI agents. Today, the number is 0%.

With StackSpot AI Multiagents, businesses can orchestrate these agents in a way that’s tailored for their specific needs. The platform offers detailed logs, permission checks, and multiple layers of security, making it a flexible and scalable solution for large enterprises.

Want to see it in action? Check out this video on our YouTube channel:

Conclusion

LLMs and SLMs are becoming more abundant and accessible. But the real game-changer lies in orchestration — how you integrate, manage, and protect these models to create a competitive edge.

In the future, we’ll see even more initiatives tackling the complexity of using multiple models. The winners will be those with the expertise to deliver reliable orchestration, ensuring quality security, and governance.

Orchestration: The Ultimate Differentiator

At StackSpot AI, we believe in the power of intelligent orchestration. Our platform, developed by Zup Innovation, empowers businesses to harness the full potential of LLMs and SLMs without being tied to a single model or compromising on security.

For companies in regulated sectors, the choice of AI models goes beyond the latest trend. The future will be a mix of LLMs and SLMs, integrated seamlessly and transparently, with robust security and governance.

So, why limit yourself to one LLM when you can have a repertoire of specialized models at your fingertips?