Visual and Text Combined: What is a Multi-Modal LLM?

CONVERSATIONAL AI

Artificial intelligence has long been positioned merely as a “text-understanding” technology. Chatbots responded to conversations, language models summarized texts and performed translations. However, the real world isn’t just made up of words. We live in a multi-modal world that includes visuals, tables, and charts. This is exactly where the Multi-Modal LLM (Large Multi-Modal Language Model) comes into play. These models can understand not only text but also images, audio, video, and even document formatting, enabling AI to reach a level of contextual understanding we’ve never seen before.

Why was such a technology needed, how capable are these models, and in what fields do they create tangible benefits? At CBOT, we often encounter these questions. The answer is clear: Multi-modal AI systems have the potential to transform enterprise operations. In a world where visual and textual knowledge is processed together, decision-making processes become faster, more accurate, and smarter.

Why Multi-Modal?

The success threshold of AI systems is directly related to how diverse the data sources are and how contextually the system can interpret them. Today, many large organizations possess millions of documents, PDFs, charts, and visual data. Text-focused traditional language models can only work with this kind of data in a limited way. For instance, a bank may want to analyze performance charts from its branches. A model limited to text would not understand this data, while a multi-modal model could read the chart, connect it with the text, and draw conclusions.

The Architecture of Multi-Modal AI

These systems are trained on large datasets, just like traditional language models. The difference is: the training data includes not only text, but also visuals, audio, and sometimes even videos. The model architecture includes both visual processing layers (e.g., a CNN or Vision Transformer) and language processing layers (LLM). Special “alignment” techniques are used to bridge these two domains.

One of the most striking examples is OpenAI’s GPT-4o model. This model can analyze an image and not only give the correct answer to a question, but also recognize complex relationships in the image. It can understand a presentation slide, interpret what a chart is conveying, and even suggest improvements based on screen layout.

Which Industries Can It Transform?

The potential of multi-modal LLMs spans almost every sector. However, the impact is particularly significant in certain fields:

  • Finance: Analyzing documents where graphs, tables, and text are intertwined. For example, analyzing financial risk reports with visual elements.

  • Retail: Interpreting shelf images, reading product labels, identifying patterns from stock images.

  • Insurance: Joint interpretation of photos and reports submitted for damage assessment.

  • Healthcare: Patient data analyzed together with medical reports, X-rays, and MRI images.

  • Public Sector: Handling various document formats such as maps, graphs, and petitions together.

Multi-modal LLMs break the limitation of text in AI, allowing it to connect more closely with the real world. With systems that can make sense not only of written data but also visuals, audio, and complex document collections, institutions can make decisions faster, work with fewer human errors, and offer richer customer experiences.

At CBOT, we are at the heart of this transformation. The systems we develop are accelerating the digital transformation of many leading organizations in Turkey and the region. With GenAI systems capable of processing not only text but also images and thinking in a more contextual way, we are redefining how business is done.

Even today, the majority of data that most companies possess consists of visual and multi-modal content. For institutions aiming to keep up with the future, Multi-Modal LLMs are not just an option, but an inevitable necessity.