# A comprehensive guide to transforming your business with multimodal AI

URL: https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai

> What multimodal AI is, how models combine text, images, audio and video, where businesses use it, and how to adopt it safely in regulated industries.

LLMs

What multimodal AI is, how models combine text, images, audio and video, where businesses use it, and how to adopt it safely in regulated industries.

Maria-Elena Tzanev · AI strategy · September 11, 2024 · 7 min read · Updated September 29, 2026

In short

Multimodal AI models work with several kinds of data in one model, such as text, images, audio and video, so they can read a scanned invoice, listen to a voice message and answer questions about both. Businesses use them for document processing, customer support and inspection work. Start with one workflow, keep sensitive data under control and measure accuracy on your own files.

Multimodal AI is AI that understands, and often produces, more than one type of data, such as text, images, audio and video, within a single model. Instead of chaining a speech recognizer, an OCR engine and a text model, a multimodal model can read a scanned contract, listen to a customer's voice message and answer questions about both. For businesses, that makes it practical to automate work that involves documents, screens and conversations, not just typed text.

## What is multimodal AI?

A modality is a type of data: text, images, audio, video or structured numbers. Traditional AI models handle one modality each. A multimodal model accepts several at once and reasons across them.

| Aspect | Single-modality AI | Multimodal AI |
| --- | --- | --- |
| Data handled | One type at a time, such as only text or only images | Several types together, such as text, images and audio |
| Context | Understands a situation from one kind of data | Builds a fuller picture by combining data types |
| Results | Based on one source | Cross-checked across sources |
| Best for | Simple tasks with one input type | Tasks that mix documents, images, speech or video |
| Example | Classifying the sentiment of survey responses | Checking an insurance claim's photos against the policy wording |

Take an insurance claim. The file holds a claim form, photos of the damage, a PDF of the policy and a recorded call with the customer. A multimodal model can read all four and flag where the photos don't match the description, work that used to need a person to switch between tools.

## How does multimodal AI work?

Most multimodal models follow the same pattern, even when the details differ:

1.  **Encode.** A dedicated encoder turns each input into a numerical representation: a vision encoder for images and document pages, an audio encoder for speech.
2.  **Align.** Training on paired data, such as images with captions or audio with transcripts, teaches the model to place related inputs close together, so a photo of a dented bumper and the words "dented bumper" mean the same thing to it.
3.  **Fuse.** The model combines the representations and uses attention to weigh the parts that matter for the question, such as the damaged area in a photo rather than the parking lot around it.
4.  **Generate.** A language model produces the output: an answer, extracted fields, a summary or, in models that support it, speech or images.

Some models combine separate encoders with a language model; others are trained on mixed data from the start. For a business user, the difference shows up in quality and cost, not in how you use the model.

Very few enterprises train multimodal models from scratch. The usual path is to start from a foundation model, ground it in your own documents with retrieval, and fine-tune only when evaluation shows a gap the other steps can't close.

## What types of multimodal models are there?

| Type | Inputs and outputs | Typical business uses |
| --- | --- | --- |
| Vision-language models | Text with images, document pages or screenshots | Document extraction, visual inspection, screen reading |
| Speech and audio models | Speech in, text out, or text in, speech out | Call transcription, voice agents, meeting summaries |
| Video understanding | Video frames and audio, with text | Training content, quality control, compliance review |
| Omni or any-to-any | Text, images and audio in and out | Assistants that see, listen and talk in one exchange |

The right type depends on the data your process already produces. A bank's back office mostly needs vision-language models for documents; a contact center needs speech.

## Where do businesses use multimodal AI?

The strongest use cases are the ones where people currently read, look at or listen to something before they type into a system:

-   **Document processing.** Extract fields from scanned forms, bank statements, invoices and contracts, including tables and charts, and check them against your rules. See [document review agents](https://www.getdynamiq.ai/use-cases/document-review).
-   **Customer support.** Understand a customer's screenshot, photo or voice message along with their account history, and resolve more requests without a handoff. See [customer service agents](https://www.getdynamiq.ai/use-cases/customer-service).
-   **Insurance and claims.** Compare damage photos with the claim description and the policy, and route mismatches to an adjuster.
-   **Healthcare administration.** Process referral documents and intake forms that mix typed text, handwriting and images, with clinicians reviewing anything flagged.
-   **Inspection and quality control.** Combine camera images with sensor readings to spot defects on a production line.
-   **Computer use.** Agents that read screens can operate web applications without an API, for example to collect data from a supplier portal.

## What are the benefits of multimodal AI?

-   **Fewer handoffs.** One model replaces a chain of OCR, speech-to-text and text models, with fewer points of failure.
-   **Richer context.** Each data type can confirm or contradict the others, which catches errors a single source would miss.
-   **More natural interaction.** Customers and employees can show and say what they mean instead of describing it in text.
-   **New automation.** Work that depended on reading documents or looking at images becomes automatable for the first time.

## What are the challenges, and how do you manage them?

| Challenge | Why it matters | How to manage it |
| --- | --- | --- |
| Accuracy on your data | Models can misread handwriting, stamps and low-quality scans | Test on a representative sample of your own files before launch |
| Personal and biometric data | Images and audio carry faces, ID numbers and voices | Keep processing in your perimeter and detect PII in extracted text |
| Cost and latency | Images, audio and video use far more tokens than text | Route simple tasks to smaller models and compress inputs |
| Explainability | Reviewers need to see why a field was extracted or a claim flagged | Keep traces and link each answer to its source page or frame |

Regulation adds weight to the second row. Under the GDPR, biometric data used to identify a person is a special category of data, and sector rules such as HIPAA apply to images and recordings as much as to text.

## How do you get started with multimodal AI?

1.  **Pick one workflow** where people spend time reading documents or listening to calls, and the outcome is easy to check.
2.  **Collect a representative sample** of real inputs, including the messy ones: poor scans, handwriting, background noise.
3.  **Compare two or three models** on that sample, scoring accuracy, latency and cost.
4.  **Build the workflow with a review step,** so a person approves low-confidence results while you learn where the model fails.
5.  **Measure in production** and turn failures into test cases before you expand to the next workflow.

## How does Dynamiq support multimodal AI?

Dynamiq lets teams build multimodal agents on the models they choose and run them inside their own infrastructure:

-   **Agents that see documents and images.** Agents accept images and files alongside text, and document parsing and extraction feed knowledge bases for retrieval; see [Knowledge](https://www.getdynamiq.ai/product/knowledge-rag).
-   **Voice agents for inbound calls,** with 15 voice model vendors to choose from; see [Voice Agents](https://www.getdynamiq.ai/product/voice-agents).
-   **A choice of models.** Use any of 29 model providers from the SDK, call the most-used models through one AI Gateway endpoint, or run open models yourself; see [models](https://www.getdynamiq.ai/product/models).
-   **Controls for sensitive data.** Detect PII and prompt injection, pause tool steps for human approval and trace every run; see [guardrails and approvals](https://www.getdynamiq.ai/product/guardrails).

The whole platform can run in Dynamiq's cloud or self-hosted in your own cloud account or data center; see [how we deploy](https://www.getdynamiq.ai/deployment).

## FAQ

### What is multimodal AI in simple terms?

Multimodal AI is AI that can work with several kinds of information at once, such as text, pictures, sound and video. It can look at a photo, read a document and listen to a recording, then answer a question that needs all three.

### What is the difference between multimodal AI and generative AI?

Generative AI describes what a model does: it creates new content. Multimodal describes what a model can take in or produce: more than one type of data. Many current generative models are multimodal, such as a model that reads an image and writes a description of it.

### What are examples of multimodal AI in business?

Common examples include extracting data from scanned documents, customer support agents that understand screenshots and voice messages, insurance claims review that compares photos with policy text, and quality inspection that combines camera images with sensor data.

### Is multimodal AI safe for sensitive documents?

It can be, with the right controls: process documents in infrastructure you control, detect personal data in extracted text, limit who can access the results, keep a person in the loop for decisions, and keep a trace of every run for audit.

### Do I need to train my own multimodal model?

Usually not. Most businesses start from an existing foundation model, add their own documents through retrieval and carefully written instructions, and fine-tune only when evaluation on their own data shows a gap the other steps can't close.

Maria-Elena Tzanev

AI strategy

Writes on agentic AI strategy and enterprise adoption at Dynamiq.

On this page

1.  [What is multimodal AI?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#what-is-multimodal-ai)
2.  [How does multimodal AI work?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#how-does-multimodal-ai-work)
3.  [What types of multimodal models are there?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#what-types-of-multimodal-models-are-there)
4.  [Where do businesses use multimodal AI?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#where-do-businesses-use-multimodal-ai)
5.  [What are the benefits of multimodal AI?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#what-are-the-benefits-of-multimodal-ai)
6.  [What are the challenges, and how do you manage them?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#what-are-the-challenges-and-how-do-you-manage-them)
7.  [How do you get started with multimodal AI?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#how-do-you-get-started-with-multimodal-ai)
8.  [How does Dynamiq support multimodal AI?](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#how-does-dynamiq-support-multimodal-ai)
9.  [FAQ](https://www.getdynamiq.ai/post/a-comprehensive-guide-to-transforming-your-business-with-multimodal-ai#faq)

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