AWS Data & AI Stories #01: Multimodal AI

In traditional AI systems, text was usually the main input.

But to solve real life problem, text is not enough by alone.

Today, many workloads include documents, images, audio, and video at the same time. A user may upload a PDF report, attach a photo, send a voice note, or provide a short video clip. If our solution only understands text, we miss a big part of the context.

This is where multimodal AI becomes important.

On AWS, multimodal AI is now becoming more practical. Amazon Bedrock Knowledge Bases supports multimodal content such as images, audio, and video, and AWS now provides different processing approaches depending on whether the goal is retrieval or structured extraction.

What is Multimodal AI?

Multimodal AI means an AI system can work with more than one type of data.

For example:

text
images
scanned documents
audio
video

Instead of focusing on only one format, the system can process and combine multiple data types to produce better results.

This is useful because enterprise data is rarely pure text. A lot of business value sits inside screenshots, scanned forms, call recordings, diagrams, inspection videos, and media-rich documents. AWS’s current multimodal stack is built around exactly that problem.

Why does it matter?

Because real systems are multimodal by nature.

Think about a few examples:

A support team receives images and voice notes from the field
A finance team works with reports, charts, and scanned documents
A healthcare team uses forms, reports, and medical images
An industrial operation stores inspection photos, maintenance PDFs, and recorded operator observations

In all of these cases, text-only AI is limited.

A multimodal approach helps us move from isolated files to connected understanding.

Multimodal AI on AWS

When I look at AWS from a practical point of view, I see multimodal AI as a workflow, not just a model.

A simple logical flow looks like this:

Collect multimodal data
Process and extract useful information
Store or index the result
Retrieve relevant context
Generate answers, summaries, or actions

AWS already has building blocks for this. Amazon Bedrock Data Automation is designed to extract structured insights from documents, images, audio, and video. Amazon Bedrock Knowledge Bases supports multimodal retrieval. AWS also supports Nova Multimodal Embeddings for visual and cross-modal retrieval scenarios.

Main AWS services to know

1. Amazon Bedrock Data Automation

This is useful when the main challenge is understanding raw input.

For example:

extracting information from documents
analyzing images
processing audio
turning video into structured output

So if the input is messy and unstructured, this is a strong starting point. AWS positions Bedrock Data Automation specifically for automating insight generation from unstructured multimodal content.

2. Amazon Bedrock Knowledge Bases

This is useful when the goal is retrieval.

If you want your AI application to search your content and answer questions based on it, Knowledge Bases becomes important. AWS documentation now states that Bedrock Knowledge Bases supports images, audio, and video in addition to traditional text-based content.

3. Amazon Nova Multimodal Embeddings

This is useful when the goal is similarity and cross-modal search.

For example:

finding images similar to another image
searching media with text
creating a shared semantic space across different content types

Amazon Nova Multimodal Embeddings supports text, documents, images, video, and audio in a single embedding space, which makes cross-modal retrieval possible.

A simple architecture view

At a high level, a multimodal AI architecture on AWS can look like this:

Data comes from users, applications, devices, or storage
Files are stored in Amazon S3
Bedrock Data Automation extracts useful content and structure
Bedrock Knowledge Bases indexes or connects relevant knowledge
Nova Multimodal Embeddings can support semantic and visual retrieval
A Bedrock-based application or assistant generates the final output

This approach is also reflected in AWS guidance and recent AWS machine learning posts around multimodal retrieval and multimodal assistants.

Retrieval or extraction?

This is one of the first design questions I would ask.

Do I want to extract information from the content?

Or do I want to retrieve relevant content across multiple modalities?

These are not exactly the same problem.

If the main need is converting raw media into structured output, Bedrock Data Automation is usually the right starting point.

If the main need is visual similarity or cross-modal search, Nova Multimodal Embeddings is often the better fit.

AWS explicitly separates these two approaches in its multimodal guidance, which is useful because many teams try to solve all multimodal problems in the same way.

Where can this be used?

There are many practical scenarios.

A few examples:

intelligent document processing
visual search
media search
support case analysis
industrial inspection workflows
knowledge assistants
report summarization from mixed content

For me, the important point is this: multimodal AI is not only about chat. It is also about turning different content types into usable business knowledge.

Common mistake

A common mistake is to think multimodal AI means only “upload file and ask question.”

That is too simple.

A real solution usually needs:

ingestion
extraction
indexing
retrieval
generation
governance

The model is only one part of the story.

Final thoughts

Multimodal AI is becoming a practical architecture topic on AWS.

Instead of treating text, images, audio, and video as separate worlds, we can now build workflows that connect them. AWS already provides managed building blocks for processing, retrieval, and generation across these content types, which makes multimodal design much more realistic than before.

For me, the first step is not choosing the model.

The first step is asking: