Multimodal LLMs are Revolutionizing Data Discovery

November 30, 2022, is a date that will live in history for many in the world of AI and tech. On that day, a new chatbot solution called ChatGPT was launched, and the rest, as they say, is history. Fast forward to our modern world, and the chatbot has become a household name. 

Fast forward to today, and enterprise artificial intelligence has only continued to evolve at breakneck speeds. While this first wave of Large Language Models (LLMs) wowed the world with their ability to generate human-like text, today’s most impactful enterprise applications are going beyond words. They are going multimodal.

Multimodal LLMs represent the next frontier of enterprise intelligence, enabling AI systems to understand and generate insights not only from text, but from images, videos, meetings, presentations, and more. For modern organizations dealing with a flood of diverse content types, this shift is not just impressive—it’s essential.

Why Multimodal Matters

Most enterprise data doesn’t live in clean, structured databases. According to several reports, the average estimate is around 80-90% of corporate data is stored as unstructured. This means said data is living in file shares, scanned documents, marketing decks, customer support tickets, meeting transcripts, as well as image and video files. Traditional LLMs are fundamentally limited since they can only "understand" plain text. Critical business context is left out of the equation, and not having access to over three-quarters of vital data will only get worse as businesses grow and compile more data.

Multimodal LLMs offer a solution to this increasingly vital problem. These models are trained to process and interpret multiple data types simultaneously. That means they can read a PDF, extract the chart from page four, interpret the table on page five, and relate it back to a product description in a separate email thread—all in one unified response.

In practice, this capability unlocks significant value across the business. Customer service teams can resolve cases faster by combining knowledge base articles and screenshots. Legal teams can analyze contract language alongside scanned annexes. R&D groups can synthesize findings from research papers, technical diagrams, and prior patent filings.

Turning Multimodal Potential into Enterprise Results

While the potential of multimodal LLMs is enormous, deploying them in an enterprise environment is easier said than done. Companies must ensure that the multimodal LLM in question has governance, explainability, and seamless integration with existing systems. That is where Mindbreeze makes the difference.

Mindbreeze’s latest release merges multimodal LLM capabilities directly with our secure insight engine, enabling organizations to unlock value from all their data types—texts, images, videos, presentations, and more. This enables faster answers, deeper insights, and more confident decision-making across departments.

And unlike public AI tools, Mindbreeze processes data within your trusted environment. Each AI-powered response includes full source attribution, so users can validate where insights are coming from and act on them with confidence.

Enterprise-Ready, Right Now

Multimodal LLMs aren’t a future capability—they are here, and Mindbreeze is already putting them to work inside enterprises across industries. As AI evolves, organizations that embrace multimodal intelligence will be best positioned to innovate, compete, and lead.

Chat with your key account manager to learn how multimodal LLMs and Retrieval-Augmented Generation (RAG) are shaping the next chapter of enterprise AI.