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When the first large language models (LLMs) emerged from developers’ sandboxes into the wild, the business world’s response ranged from fear to euphoria, and both emotions lingered for quite a while. It didn’t take too long before all sorts of models began flooding the landscape: Small LLMs (yes, they are a thing; that’s not a typo), retrieval-augmented generative (RAG) models, bespoke, internal, fine-tuned, public, private, open-source—and the list continues to grow. But at least reality has finally settled in, calming things down and allowing organizations to identify their needs and determine which models will best meet them. 

Before deciding on and deploying any generative AI (GenAI) models, organizations must develop a proactive, flexible approach and fully understand the features, tools, and add-ins that will best support their business use cases, and then prioritize their in-house situations and the solutions to be adopted. For instance,  leadership committees must determine how many models will be needed, what types, and who will have access to them. They must identify the person or team who will determine how the new AI tools fit into the company’s broader digital infrastructure. They will also need to address questions such as are off-the-shelf solutions enough, or is the ability to customize features and parameters to meet organizational, and even departmental, use cases necessary? Is an audit trail for prompts and responses important? What about verifying the accuracy of the model-provided information?  Observability? Convenience? Ease of use? 

When facing such questions, one of the easy decisions to make early in the process is to employ a model-agnostic solution that enables flexibility and adaptability, which are essential product characteristics for organizations seeking to harness the full potential of AI. A model-agnostic approach to AI deployment involves using solutions that are compatible with a wide range of AI models, regardless of the models’ specific architecture or design. Such flexibility allows organizations to adapt quickly to new developments, tools, and innovations without being tethered to a single model or provider. A model-agnostic solution can also sharpen a firm’s competitive edge, enabling it to pivot quickly and efficiently when responding to new challenges and opportunities.

At CalypsoAI, we created the first model-agnostic model orchestration solution to enhance security and enablement for GenAI deployments. Our platform is designed to integrate seamlessly with a wide variety of AI models and offers:

  • Universal Compatibility: Our CalypsoAI platform works across different AI models, providing a consistent trust layer regardless of the underlying technology. A single query can be run on all models to compare and assess the depth and accuracy of responses. 
  • Adaptive Security Measures: With its ability to interface with multiple models, the CalypsoAI platform ensures robust security and observability, adapting to the unique needs of each model. Customizable, policy-based access controls allow admins to determine who has access to each model and under what circumstances.
  • Ease of Integration: CalypsoAI’s API-driven, SaaS-enabled design means integration is reduced to a few lines of code, diminishing the time and complexity typically required to secure AI deployments.

In practical terms, a model-agnostic approach like that of the CalypsoAI platform allows organizations to explore a broader range of AI applications. Whether it’s for predictive analytics, customer service enhancements, or advanced research, the ability to seamlessly switch or combine AI models without compromising security or functionality is invaluable.

As AI continues to advance, the model-agnostic approach will become increasingly necessary to ensure organizations are keeping up with AI developments and are positioned to lead and innovate. With CalypsoAI’s security, enablement, and model orchestration platform, organizations can navigate this evolving ecosystem while enjoying enhanced productivity and increased competitive advantage.

 

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