Boost processing performance by combining AI models

Leveraging the strengths of multiple AI models within a single application is a powerful strategy for optimizing performance, accuracy, and reliability in complex scenarios. By integrating different AI systems, businesses can enhance decision-making, automate workflows, and drive better outcomes.

Microsoft’s Model Catalog offers access to over 1,800 AI models, with even more available through Azure OpenAI Service and Azure AI Foundry. This extensive ecosystem ensures that organizations can select the best models to develop custom AI solutions tailored to their specific needs.

Let’s explore how a multi-model AI approach works and examine real-world scenarios where businesses have successfully implemented this strategy to boost performance and reduce costs. By leveraging multiple AI models, companies can enhance efficiency, improve accuracy, and drive smarter decision-making across various industries.

How the Multiple Model Approach Works

A multiple model approach enhances AI performance by combining specialized models to tackle complex tasks more effectively. Each model is designed for a specific function, such as language understanding, image recognition, or data analysis, allowing for greater accuracy and efficiency. These models can work in parallel, route data dynamically, or complement each other within an application.

For example, you might integrate a fine-tuned vision model with a large language model (LLM) to perform advanced image classification and natural language queries simultaneously. Alternatively, a small AI model fine-tuned to generate SQL queries could be paired with a larger AI model for broader tasks like information retrieval and research assistance. In both cases, this approach delivers flexibility and precision, enabling businesses to build custom AI solutions tailored to their needs.

Key Considerations Before Implementing a Multiple Model Strategy

To ensure a successful multi-model AI strategy, organizations must first define their objectives and evaluate the suitability of different models. Key factors to consider include:

• Intended purpose – What problem is the model solving?
• Application requirements – Does the model size align with system constraints?
• Training & management – How will specialized models be maintained?
• Accuracy needs – Do certain tasks require higher precision than others?
• Governance & compliance – Are there policies for responsible AI use?
• Security & bias mitigation – How will potential risks be addressed?
• Cost efficiency – What are the projected costs at scale?
• Programming language compatibility – (Check DevQualityEval for recommended languages for specific models.)

Prioritizing these factors will help organizations optimize AI model selection, integration, and deployment based on their tech stack, resources, and business goals.

Real-World Applications of the Multiple Model Approach

Let’s examine how leading organizations have successfully implemented multi-model AI strategies to enhance efficiency, reduce costs, and drive innovation.

Scenario 1: Routing

Routing is when AI and machine learning technologies optimize the most efficient paths for use cases such as call centers, logistics, and more.

Multimodal Routing for Diverse Data Processing

One application of multiple model processing is routing tasks through different multimodal models that specialize in processing specific data types, such as text, images, sound, and video. For example, an application can use a combination of a smaller model like GPT-3.5 Turbo with a multimodal large language model like GPT-4o, depending on the data modality.

This routing approach allows applications to process multiple modalities efficiently by directing each type of data to the model best suited for it, improving overall system performance and versatility.

Expert Routing for Specialized Domains

Another example of routing is expert routing, where prompts are directed to specialized models, or “experts,” based on the specific field referenced in the task. By implementing expert routing, companies ensure that different types of user queries are handled by the most suitable AI model or service.

For example, technical support queries might be routed to a model trained on technical documentation and support tickets, while general information requests could be handled by a general-purpose language model.

Expert routing is particularly valuable in specialized fields such as medicine, where models can be fine-tuned for specific topics or image analysis. Instead of relying on a single large model, multiple smaller models—such as Phi-3.5-mini-instruct for chat-based tasks and Phi-3.5-vision-instruct for vision-based tasks—can be used. Each model is optimized for its domain, ensuring greater accuracy, reduced processing costs, and improved relevance of AI-generated responses.

Real-World Examples of Expert Routing

Auto Manufacturer

A large auto manufacturer implemented a Phi model to efficiently process most basic tasks while routing more complex queries to a large language model (LLM) like GPT-4o. The Phi-3 offline model handles standard data processing locally, reducing costs and latency, while the GPT online model is leveraged for more business-critical, complex tasks. This hybrid approach maximizes the cost-effective performance of Phi-3, while ensuring that advanced queries are processed effectively.

Sage

Another example of expert routing comes from Sage, a leader in accounting, finance, HR, and payroll technology for small and medium-sized businesses (SMBs). Sage aimed to enhance AI-powered automation to improve productivity and efficiency in accounting processes.

To achieve this, Sage deployed Mistral, a commercially available large language model (LLM), and fine-tuned it with accounting-specific data. This customization addressed gaps in GPT-4 and improved Sage Copilot’s ability to categorize and respond to accounting-related queries.

For example, while a standard Mistral LLM might struggle with cash-flow forecasting questions, the fine-tuned version could accurately route queries through Sage-specific and domain-specific data, ensuring precise and relevant responses for users.

Scenario 2: Online and Offline AI Use

Online and offline AI models provide a hybrid approach that balances local processing with global data access. In this setup, an organization can use an offline AI model for specific on-device tasks (such as a customer service chatbot) while also leveraging an online AI model to retrieve real-time, context-aware information.

Hybrid Model Deployment for Healthcare Diagnostics

In the healthcare sector, AI models can be deployed in a hybrid manner to support both online and offline capabilities. For example, a hospital could use an offline AI model to handle initial diagnostics and data processing locally via IoT devices. Simultaneously, an online AI model could access the latest medical research from cloud-based databases and medical journals.

• The offline model processes patient data on-site, ensuring fast and secure analysis.
• The online model retrieves globally available medical insights, helping healthcare professionals stay updated with the latest advancements.

This dual approach enables medical staff to conduct accurate patient assessments while benefiting from real-time medical knowledge.

Smart-Home Systems with Local and Cloud AI

In smart-home systems, AI models can be used for both local and cloud-based tasks.

• An offline AI model embedded within the home network controls essential functions such as lighting, temperature, and security systems, ensuring quick responses and functionality during internet outages.
• An online AI model is utilized for cloud-based services like voice recognition, smart-device integration, and software updates, enabling continuous feature enhancements.

This online-offline combination allows smart-home systems to maintain reliable, real-time control over critical functions while leveraging cloud intelligence for advanced capabilities and long-term optimization.

Scenario 3: Combining Task-Specific and Larger Models

Companies aiming to optimize costs and efficiency can benefit from combining a small, task-specific language model (SLM) like Phi-3 with a larger, more powerful model like GPT.

One effective strategy is to deploy Phi-3, part of Microsoft’s high-performance, cost-efficient small language models, in edge computing scenarios or applications with stricter latency requirements. Phi-3 can handle fast, low-cost processing, while larger models like GPT provide additional computational power for more complex queries.

Another approach is to use Phi-3 as an initial filter or triage system, managing straightforward queries and escalating only more complex or nuanced requests to GPT models. This tiered strategy helps companies streamline workflows, reduce unnecessary processing costs, and improve overall efficiency.

By strategically integrating small and large models, businesses can achieve a cost-effective AI setup tailored to their unique needs.

Capacity: AI-Powered Answer Engine®

Capacity, an AI-powered Answer Engine®, provides organizations with a personalized AI research assistant that scales across teams and departments. Their goal was to unify diverse datasets and improve information accessibility for customers.

By integrating Phi, Capacity developed an AI knowledge-management solution that enhances:

• Information retrieval
• Security
• Operational efficiency

This implementation has helped customers save time and effort while ensuring streamlined access to critical data. After the successful deployment of Phi-3-Medium, Capacity is now testing Phi-3.5-MOE for potential production use.

Unlock the Power of Multi-Model AI with Digital Bricks

Integrating multiple AI models into a single workflow can enhance efficiency, reduce costs, and optimize performance across industries. Whether you're implementing task-specific models, expert routing, or a hybrid online-offline approach, the right AI strategy can transform the way your business operates.

At Digital Bricks, we specialize in AI implementation and education, helping organizations navigate the complexities of multi-model AI deployment. Our expertise spans:

AI Literacy & Training – Gain a deep understanding of AI models, from general-purpose tools to specialized solutions like Copilot Studio.
Strategic AI Implementation – Optimize multi-model AI integration tailored to your business needs.
Responsible AI & Governance – Ensure compliance, security, and ethical AI usage across your workflows.

Whether you're building AI-powered automation, fine-tuning AI models, or upskilling your workforce, Digital Bricks provides the guidance and expertise to help you succeed.

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