A Guide to Incorporating Multimodal AI into Your Business Workflow

Last evening, Meta launched Llama3.2 model, their first model with multimodal capabilities. This came just over a week after Mistral AI announced Pixtral-12B. Multimodal models are essentially large language models with image-understanding capabilities, and due to this, they have also been called large vision models (LVMs). In this article, however, we will continue to refer to them as multimodal models instead of LVMs to avoid confusing them with vision AI models (such as the YOLO series). 

It is increasingly clear that the future of all language models is multimodality. The question we want to answer in this article is how it affects your business workflow. What do you need to know as a leader about these models, and what general use cases can you expect to attack with them? 

Before we begin, let’s take a very quick look at the type of multimodal models currently in the market, and how they pit against each other. 

Top Multimodal AI Models

As of today, there are several key multimodal AI models that are already being used by businesses. These models integrate text, images, and can reason over data that’s spread over both. 

You can find the top contenders in the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark (MMMU benchmark), and track the top performing ones at any point. 

Let’s break down some of the leading contenders. We will break them down into two categories, the ones that are open weight or open source, and others that are platform models. 

Platform Models

The platform models are accessible through APIs and are charged per model request. You should evaluate them if data sovereignty is not a primary concern, and you don’t want the complexity of hosting models in your own infrastructure. You just need to keep an eye on the API costs, especially if you foresee heavy use of these models in your application layer. 

4o Models by OpenAI

GPT-4o, OpenAI's latest multimodal model, significantly enhances the capabilities of previous versions by integrating vision alongside text and audio. It can interpret and generate visual content, such as responding to questions based on images, generating descriptions, and even creating visual elements. This makes it highly suitable for a variety of applications that rely on image recognition, such as document processing, handwriting analysis, content creation, and customer service. The one key aspect of 4o models is that you can them to produce structured outputs (say, in JSON formats), with the assurance that it will adhere to the schema 100% of the time. This is very powerful, especially if you are planning to integrate it into your business workflow.

Claude 3.5 Sonnet by Anthropic

Claude 3.5 Sonnet is a powerful multimodal model from Anthropic, also known for its advanced vision capabilities. It excels in visual reasoning tasks such as interpreting charts, graphs, and transcribing text from imperfect images, making it useful for industries like retail, logistics, and financial services. The model is designed for high-speed performance, operating at twice the speed of previous models while maintaining cost efficiency.

Gemini 1.5 Pro

The Gemini 1.5 Pro by Google DeepMind offers advanced multimodal capabilities across text, images, audio, and video, supporting a groundbreaking context window of up to two million tokens. Its vision abilities enable sophisticated image and video understanding, helping in tasks like long-form video analysis and object recognition. The model also excels in reasoning, math benchmarks, and complex code generation. The most important thing to note about this model is its context window.

Open Weight Models

These models are openly accessible and deployable in your own infrastructure (on-premise or cloud). However, keep in mind that the inference speed will depend on the underlying GPU. The real cost here is the cost of your cloud provider’s GPU instance. We don’t recommend on-prem GPU infrastructure because GPU models are constantly evolving (eg H200 is already available in less than year of H100 launch) and investing in your own on-prem infrastructure doesn’t make sense. The GPU capability will decide your inference capability (or training time, if you plan to train the models). 

Llama3.2 by Meta

Meta’s recent Llama3.2 model with vision capabilities is available in two variants, the 90B and the 11B. You also get lightweight and smaller text-only variants 3B and 1B. The 90B model is meant for enterprise use cases, and should be the one you evaluate when planning to use Llama3.2. If you need simpler vision capabilities or simple reasoning over images, then 11B might suffice. We are already testing this model for its accuracy, especially in comparison for Pixtral-12B (described below) or 4o by GPT (which was the top choice, unless you need to build data sovereign applications). 

Qwen2-VL-72B by Alibaba

The Qwen2-VL-72B is a state-of-the-art multimodal model from Alibaba, designed to excel in both vision and language tasks. With 72 billion parameters, this model leverages advanced architectures such as a Vision Transformer (ViT) with 600 million parameters, allowing it to process images and videos seamlessly. Its standout feature is the Naive Dynamic Resolution mechanism, which enables the model to handle images of varying resolutions by converting them into dynamic visual tokens. This leads to highly efficient and accurate visual representation, closely mimicking human visual processing.

Pixtral-12B by Mistral AI

Pixtral-12B has already gained reputation for its high precision in complex visual tasks. It excels in areas requiring deep understanding of complex image data, such as invoice parsing, OCR, deriving data from infographics or graphs and charts and other such scenarios. This model not only translates images into rich, descriptive text but also enhances the accuracy of text-based outputs with contextual image data. Mistral AI has tailored Pixtral-12B for industries where image detail and accuracy are paramount.

Business Use-Cases for Multimodal AI Models

While there are hundreds of use-cases that multimodal AI models can tackle, the ones you should ideally start with are the ones where your team currently spends significant time on manual data entry. We have been approached by businesses regularly looking to solve for such scenarios, as it can drastically improve the productivity and efficiency of their team. 

Here are some such use-cases.

Invoice Parsing

If your business deals with hundreds of unstructured or handwritten invoices regularly, multimodal AI models like 4o, or open models like Pixtral-12B or Qwen2-VL-72B can automate invoice processing for you. These models recognize and extract key fields from documents, such as amounts, dates, and vendor details, turning unstructured documents like PDFs or scanned images into structured formats like JSON. This means that you can drastically reduce the effort it takes to perform manual data entry, and streamline parts of your operations that were hard to automate before. We have shared an example of invoice parsing with Pixtral-12B on our blog.

Document Understanding

Handling large volumes of contracts, reports, or forms can be overwhelming, but multimodal models like Claude 3.5 Sonnet or Gemini 1.5 Pro can assist you by automatically parsing these documents, highlighting key sections, and summarizing them. This means that if you're in legal services, banking, or insurance, these models will help you quickly extract essential information, and reduce manual workload. Your choice of model would depend on the number of documents you want to analyze and the context window you would need.

The use-cases we are seeing around this revolve around claims processing, building legal AI assistants, or contract handling. Reach out to us for a free consultation if you want to understand how this can be done.

AI Assistant Creation

You can create smarter AI assistants for your team with multimodal AI models that understand both text and images. For example, with GPT-4o and Qwen2-VL-72B you can enable your team to query codebase, company knowledge base, contracts, support tickets and more. In this scenario, you need to create a system where the multimodal model has access to a repository of documents through a retrieval system (using vector databases or knowledge graphs).

Typically, retrieval-augmented generation (RAG) systems are the ones to use here. Your choice of retrieval model will depend on your document type and their structure.

Product Description or Catalog Creation

If you’re running an e-commerce platform, multimodal AI can help you automate the process of generating product descriptions. Models like Gemini 1.5 Pro or Qwen2-VL-72B can take product images and generate detailed descriptions along with SEO tags. This reduces the time your sellers spends on manual updates while maintaining a consistent style across your catalog.

We have analyzed the capabilities of a number of models when a large retail client reached out to us. We used 4o model to create an API microservice for them, that has automated a number of steps that sellers used to have to manually perform in the past.

Automating Product Catalog Creation with Tabular Data

Similar to above, one of the regular manual tasks for e-commerce sellers is updating product catalogs on multiple marketplaces, which usually involves creating descriptions, pricing, and specifications based on product images. Multimodal AI, like Qwen2-VL-72B or Pixtral-12B, can automate this process for you by converting product images into structured tabular data, extracting details such as dimensions, color, material, and other attributes directly from the image. 

This makes it easier for you to update multiple product listings across marketplaces in a consistent and efficient manner, without manually filling in details for each product entry. Reach out to us if you are an e-commerce seller, and want to streamline your catalog creation or updation using AI.

Report Generation

A multimodal AI-based RAG system can streamline report generation for your team by retrieving and analyzing data from a range of sources like spreadsheets, PDFs, and images, then generating content based on a pre-defined structure. For example, when producing a financial report, the system can pull data from balance sheets and market trend graphs, interpret both text and visual content using models like Qwen2-VL-72B or GPT-4o. You can then use this for automatic generation of sections such as executive summaries, data insights, and market analysis, reducing the need for manual input and ensuring consistency across reports. 

Additionally, such a system can automate regular report updates. If a quarterly report is required, the AI can retrieve the latest data, run the same analysis, and regenerate the report, making it highly efficient for recurring reporting needs. 

Choice of the AI Model

How do you choose which model to use? There are various factors that go into it. Here are some. 

Considerations for Choosing Right Multimodal AI

1. Data Privacy and Sovereignty: If you need full control over your data for privacy reasons, opt for open-weight models like Llama3.2 or Qwen2-VL-72B, which allow you to host on your own infrastructure. These models, however, require strong computational resources (e.g., GPU instances), so ensure your infrastructure can support them. For businesses that prefer not to handle infrastructure or data hosting, platform models like GPT-4o and Claude 3.5 Sonnet are available via APIs and offer ease of use at the cost of entrusting data to third-party providers.
2. Cost Efficiency: For low-frequency or light AI usage, platform models are an excellent option due to their pay-per-use model. However, if you anticipate high usage, the cumulative API costs can outweigh the initial setup costs of hosting an open-weight model. Thus, for long-term, high-volume applications, self-hosted models can become more cost-efficient.
3. Context Window and Task Complexity: Models like Gemini 1.5 Pro offer extended context windows, up to two million tokens, making them ideal for large-scale tasks like long-form document analysis or video processing. For report generation or complex data analysis requiring multiple documents, having a model that can handle large context windows is crucial to maintain coherence and accuracy across sections.
4. Type of Input Data: Multimodal models vary in their ability to handle specific data types. For businesses dealing with both text and images, models like Qwen2-VL-72B or Pixtral-12B excel in processing diverse data such as scanned documents, charts, and product images. If your operations involve complex document processing, invoice parsing, or catalog generation, selecting a model with advanced visual understanding is essential.
5. Industry-Specific Use Cases: Your industry’s requirements will also shape your choice. If precision in visual data analysis is crucial—like in retail or logistics—Pixtral-12B offers specialized capabilities for product catalog creation and inventory management. In contrast, Claude 3.5 Sonnet is better suited for visual reasoning tasks like financial chart analysis or legal document processing.

Building RAG and Retrieval Considerations

If your workflow involves Retrieval-Augmented Generation (RAG)—where the AI retrieves documents or data before generating content—you may need to build a RAG system that complements the AI model. This is commonly the case, as in most cases, your dataset size will be big enough that you won’t be able to pass the entire data to your AI model in the prompt. 

In such a scenario, additional factors you need to consider include:

1. Retriever Choice: Selecting the right retriever for your RAG setup depends on the nature of your data. Vector search with tools like Qdrant or PGVector is often recommended for unstructured data, as it can efficiently retrieve semantically similar documents. If your data is more structured or domain-specific, you may need a custom retriever or knowledge graphs.
2. Chunk Size: The size of the data chunks you feed into the model is critical for optimizing retrieval and generation. Smaller chunk sizes (around 300–500 tokens) help maintain context and ensure that the model accurately retrieves relevant sections without overwhelming the context window. Larger chunks can cause loss of detail or make it harder for the AI to locate specific information.
3. Embedding Models: Depending on the task, embedding models for your retriever must align with the model you're using. For instance, if you're using Qwen2-VL-72B, embeddings from similar multimodal models ensure consistent retrieval performance.
4. Evaluation of RAG: You should also evaluate the RAG system once its built. For this, you need to create a Q&A dataset, and use a framework like RAGAS, which helps evaluate the RAG responses based on the benchmark dataset you have provided. This is highly recommended. 
5. Integration: Once your retriever is set up, integrating it with the chosen multimodal AI can streamline workflows. For example, in a report generation task, the retriever can pull financial data or visual elements from various sources, and the AI can generate coherent sections based on this input.

By combining the right multimodal AI with a well-architected RAG system, you can significantly improve automation in tasks like report generation, document understanding, and content creation.

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Conclusion

At Superteams.ai, we specialize in helping businesses navigate the complexities of choosing the right multimodal AI model or building powerful RAG systems tailored to your data. Whether you’re looking to streamline invoice processing, automate product catalog updates, or generate detailed reports, we can guide you through the process of selecting models like Llama3.2, Qwen2-VL-72B, or Gemini 1.5 Pro based on your specific needs. We also have extensive experience in designing and deploying RAG systems that integrate seamlessly with your workflows, ensuring efficient data retrieval and generation for tasks like report creation, document analysis, and more.

If you’re ready to take the next step in optimizing your business with AI, we’re here to help. Reach out to us for a free consultation to explore how we can support your business with cutting-edge AI solutions tailored to your operations.

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