The Promise of Image embedding model
AI chatbots are changing how businesses work.
They answer questions fast. They pull data from company files.
But there’s a problem:
Most AI chats with business data rely on text alone. This limits their power.
Enter the image embedding model. It’s a gAMecHaNGer. This post explores why.
Why Image Embedding Models Matter
Most company AI chats use text embedding models.
These turn words into numbers.
The numbers help find relevant text.
But business data isn’t just text. Think PDFs, Word docs, or presentations.
They have tables, charts, and images; columns, text boxes, and arrows.
Extracting information with text only from these is messy. Often it’s incomplete.
An image embedding model fixes this. It turns images into numbers too. It captures meaning from visuals. No need to extract text first.
This makes setup easier. It also boosts answer quality.
The AI sees the full picture—literally!
- Easier setup: Skip tricky text extraction.
- Better answers: Visual data adds context.
- More accurate: No lost details from poor text extraction.
How AI chats with business data works today
Current AI chats lean on text. But businesses store data in rich documents. To use it, they extract text.
Text extraction can be done with:
- Programmatically
- libraries such as pdf2text, pandoc, kreuzberg etc.
- OCR (optical character recognition)
- Machine Learning Models such as EasyOCR, GOT OCR 2.0 etc.
- Vision LLMs (Large Language Models that understand images)
- Sonnet3.7, Minicpm-o-2.6 etc.
Then, the extracted text is passed into the AI prompt.
If the extracted text is very long, it won’t fit into the AI prompt.
To solve this, companies use a RAG (Retrieval-Augmented Generation) pipeline.
Here’s how it works:
1. Split extracted text into small text chunks.
2. Turn chunks into numbers using a text embedding model.
3. Store numbers and text in a database.
4. When a user asks a question, turn it also into numbers.
5. Find the closest matching text chunks to the user question.
6. Feed those chunks to the AI prompt for an answer.
This works for simple text.
But this method struggles with complex content.
Imagine a manual. It has text, images, and diagrams. The layout matters!
Text extraction often loses this.
You get jumbled words. No context. No images. No diagrams.
Image Embedding model explained
Let’s break it down, shall we?
A text embedding model turns words into numbers. Similar words get similar numbers.
For example, “sunny day” and “bright day” are close. “Rainy day” is farther.
An image embedding model does the same for pictures. It turns images into numbers.
A sunny beach photo gets numbers close to “sunny day.” A rainy city photo? Not so much.
The magic is in multimodal / image embedding models. They handle both text and images!
They let you compare them. Ask about revenue. The model finds a chart image, not just text.
It’s like giving the AI eyes.
- Text embedding: Words to numbers.
- Multimodal / Image embedding: Text and images, together.
The future of company AI chat with Image Embedding Models
Why is the image embedding model the future?
It’s simple.
It cuts steps.
It keeps data whole.
No more losing context from bad text extraction. A picture is worth a thousand words.
With image embedding models, the AI sees all the content!
Here’s the process:
1. Turn each document page into an image.
2. Create an image embedding vector (the “numbers”) for each.
3. Store images and vectors in a database.
4. User asks a question? Turn the question into a vector too and find the most relevant images.
5. Feed images to a vision AI model (like Grok or Gemini).
6. Get a precise answer 🎉
This shines with complex documents.
Think installation guides. Or financial reports with charts.
The AI sees the full layout. It understands connections.
A red modem on top of another connected with a cable?
It’s in the image. No need to describe it.
- Simpler: No text extraction headaches.
- Richer: Visuals add depth to answers.
- Flexible: Works for any document style.
*Image suggestion: Show a complex document page (e.g., an instruction manual with text, arrows, and diagrams). Next to it, an AI chat answering a question about the manual.*
How to find the best image embedding model?
Researchers behind ColPali made image embedding models with document data popular.
They also created a leaderboard for open source models: https://huggingface.co/spaces/vidore/vidore-leaderboard
There are also models that aren’t part of this leaderboard. Proprietary models from Cohere for example.
If you know a leaderboard that compares both open source and closed source image embedding models, please let us know per Email or on X 🙏🏻
Real-World Impact
Image embedding models aren’t just theory.
They’re proving themselves.
Companies with messy, visual-heavy documents see big wins.
Setup is faster. Answers are sharper.
It’s not always the best choice – simple text often works.
But for enterprises with evolving, complex files? Image embedding are the way to go.
We just started using image embedding models in new projects.
Results are promising. It’s becoming our go-to approach.
The future of company AI chat is visual. And it’s here!
