Small Language Models: The Future of Business-Ready AI

There’s no shortage of hype around large, monolithic AI models, but the real future of AI in business lies in agility, scalability, and cost-effectiveness. Enter Small Language Models (SLMs)—the powerhouse behind building a Composable Enterprise.

Tomorrow, I’ll be diving into how businesses can achieve end-to-end solutions using Composable Language Models (CLMs), seamlessly combined with classic AI and analytics components, all deployed as data products.

Composable Language Models: Modular AI for Real Business Needs

The challenge with large language models (LLMs) is their significant computational cost, latency issues, and reliance on expensive infrastructure. In contrast, SLMs are lightweight, faster, and can solve specific parts of business problems when deployed in a composable architecture.

At Dataception Ltd, we’ve pioneered a modular approach to AI with SLMs deployed as data products. These models can:

• Decompose complex workflows into smaller, manageable parts.
• Run efficiently on on-demand CPU and GPU instances, eliminating the need for massive, always-on infrastructure.
• Integrate with other components like classic AI, graph analytics, and business intelligence tools to deliver holistic solutions.

Data Mesh Infrastructure: The Foundation for Composability

To bring composable solutions to life, we leverage Data Mesh architecture as the infrastructure backbone. Key highlights include:

1. SLMs with Function Calling: Small models orchestrate business processes, calling APIs or other components dynamically to complete workflows.
2. Knowledge Graph Integration: By combining SLMs with knowledge graphs, we enable contextual understanding and enriched data exploration across decentralized domains.
3. On-Demand Resources: Deploying SLMs and analytics as discrete data products on CPU/GPU instances ensures scalability and cost control without sacrificing performance.

Why SLMs and Data Products Matter

SLMs aren’t just efficient—they are the key to building AI solutions that are modular, scalable, and focused on delivering business value. By deploying AI models as data products in a composable architecture, businesses can:

• Solve specific business problems faster and more effectively.
• Integrate seamlessly with existing workflows and systems.
• Optimize costs by scaling individual components on demand.
• Enhance flexibility by combining SLMs with classic AI and other analytics approaches.

This is how we move beyond the hype of single large AI models and enable organizations to implement AI-driven business flows tailored to their needs.

What to Expect Next

At Dataception Ltd, we specialize in creating business-oriented solutions using SLMs and data products. Tomorrow, we’ll be sharing real-world use cases, frameworks, and techniques that demonstrate how to:

• Use Composable Language Models to orchestrate workflows.
• Deploy AI solutions incrementally with Data Mesh infrastructure.
• Build modular, scalable, and efficient business flows using small, function-calling SLMs.

If you’re curious about how Composable AI solutions can drive business transformation, reduce costs, and improve efficiency, ping us to know more. Let’s explore the future of AI—small, smart, and composable! 🚀