Multimodal

What it is

In the context of artificial intelligence, being multimodal means having the ability to interpret and generate information through multiple communication channels (modes) simultaneously. Until recently, most AI models were unimodal: a chatbot processed only text, a computer vision system analyzed only images, and transcription software handled only audio. These systems operated in silos.

A multimodal model breaks these barriers. It can "see" a graph in a PDF file, "read" the accompanying text, and "understand" the relationship between the numerical data and the visual description. For an SME, this means that AI stops being just an advanced text editor and becomes an assistant that understands the full context of real documents, invoices, stock photos, or training videos.

How it works

The magic of multimodality lies in how information is represented internally. Instead of treating words and pixels as entirely distinct things, the model uses a common vector space (embeddings).

When a multimodal model processes a photo of a broken engine and a written description of the error, it converts both into a mathematical representation where the visual concept of an "oil leak" is close to the textual description "dark viscous stain." The model isn't just labeling the image; it is aligning meanings.

There are two main types of architectures:

1. Early Fusion: Where data from different sources are combined at the very beginning of processing.
2. Late Fusion: Where each type of data is processed independently and the results are combined in the final stage to make a decision.

The modern approach, popularized by models like GPT-4o or Gemini, integrates this capability natively from initial training, allowing much greater fluidity between input types.

When to use

The multimodal approach is indispensable when business-critical information is spread across unstructured formats. If your workflow depends on humans looking at screens to extract data for the computer, multimodality is the solution.

• Digitization of physical processes: Interpreting handwritten invoices, receipts, or delivery notes where the visual layout matters as much as the text.
• Visual quality control: Analyzing photos of products on the assembly line and generating automatic text reports about defects.
• Advanced customer support: Allowing a customer to send a photo of a broken product and receive automatic repair instructions based on the technical manual.
• Complex data analysis: Crossing PowerPoint presentations (which use many visual elements) with supporting spreadsheets.

Common errors

1. Underestimating inference cost: Processing images and video requires much more computing power (and tokens) than processing simple text. Many companies try to use multimodality for tasks that a simple OCR (Optical Character Recognition) would solve for a fraction of the price.
2. Assuming perfect spatial understanding: While multimodal models "see," they may fail at millimeter-precision spatial tasks or the exact counting of many small objects in a confined space.
3. Blindly trusting complex tables: Reading a table in a PDF is one of the biggest challenges in AI. Without a validation strategy, the model may align them incorrectly.
4. Ignoring privacy: When sending photos or videos of internal processes to cloud models, SMEs often forget that this data may contain sensitive customer information or industrial secrets.

Practical example for an SME

Imagine a small condominium management company. Traditionally, when a resident reports a leak, an employee has to read the email, look at the attached photos, decide if it's urgent, and contact a plumber.

With a multimodal system:

1. The resident sends photos of the ceiling stain via WhatsApp.
2. The multimodal model analyzes the photo and identifies the severity (e.g., "active leak with mold") and the likely location (e.g., "bathroom").
3. The system crosses the photo with the building floor plan (image) and the maintenance history (text).
4. The AI automatically generates a service order already classified by urgency, attaching relevant technical notes for the technician.

This reduces response time from hours to seconds, without anyone having to manually open the attachment for initial screening.

Frequently asked questions

Q: Is Multimodal the same as OCR? A: No. OCR only transcribes characters. Multimodal understands context. An OCR reads "$20.00," but a multimodal model understands that this value is the VAT rate because it is positioned in a specific field of a specific form.

Q: Do I need special cameras to use multimodal AI in my factory? A: Generally no. Current models are very robust and can process mobile phone photos or standard CCTV images, provided the lighting allows the main elements to be distinguished.

Q: Can I use multimodal models locally to maintain privacy? A: Yes, there are already smaller and optimized versions of multimodal models that can run on your own servers or powerful workstations, ensuring that images do not leave the company.

Q: Is video processed as a continuous stream? A: In most current cases, the model extracts "frames" from the video at regular intervals and analyzes them in sequence to understand movement or state changes.

• 01Analyzing a photo of a worn-out part to identify the correct reference in the parts catalog.
• 02Creating automatic audio descriptions for products on an e-commerce site from photographs.
• 03Extracting structured data from scanned invoices that have different and complex layouts.
• 04Translating a technical manual from German to Portuguese while keeping all visual diagrams in their original place.

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