Multimodal AI: Why Your Agent Should Understand Images and Videos

For the past few years, AI agents could only understand text.

Customer uploads a screenshot of an error? Too bad — the agent can't see it.

Employee shares a product photo and asks a question? The agent can't help.

Sales rep wants to analyze a competitor's landing page? Not possible.

That changes with multimodal AI.

Multimodal AI agents can understand:

Images (screenshots, photos, diagrams)
Videos (product demos, tutorials, recordings)
Documents (PDFs, presentations, invoices)
Audio (voice messages, call recordings)

And they can combine these inputs with text to provide richer, more helpful responses.

This isn't a futuristic concept. It's available today — and it's transforming how businesses use AI agents.

What Is Multimodal AI?

Multimodal AI refers to AI systems that can process and understand multiple types of input (modalities):

Text (the traditional modality)
Images (photos, screenshots, diagrams)
Video (recordings, demos, presentations)
Audio (voice, music, sound)

Why "multimodal"? Because the AI model can reason across multiple modes of input simultaneously.

Example:

User uploads screenshot + asks: "Why isn't this button working?"

Text-only AI: "I can't see the image. Please describe the issue."

Multimodal AI: "I can see the error message in your screenshot. The 'Submit'
button is greyed out because the 'Email' field is empty. Please enter your
email address and the button will become active."

Why Multimodal AI Matters

1. Customers Naturally Communicate Visually

When something goes wrong, customers take a screenshot or record a video. It's faster and clearer than typing a description.

Traditional text-only agents:

Customer: "The login button isn't working"
Agent: "Can you describe what you're seeing?"
Customer: "It's just... not working. The button is there but nothing happens."
Agent: "Are you seeing an error message?"
Customer: "No, it just doesn't do anything."
[10 back-and-forth messages later, still no resolution]

Multimodal agents:

Customer: [uploads screenshot] "The login button isn't working"
Agent: "I can see the issue. The login button is being blocked by an ad blocker
extension. Please disable it for this site or try opening the page in an
incognito window."
[Issue resolved in 1 message]

2. Use Cases That Were Impossible Before

Multimodal AI unlocks entirely new applications:

Customer Support:

Analyze error screenshots instantly
Understand product photos for troubleshooting
Watch video recordings to diagnose issues

E-Commerce:

"Find me shoes that look like this" [uploads photo]
"What's this product called?" [uploads image from Instagram]
Visual search for similar products

Education & Training:

Upload diagrams and ask questions
Analyze student work (drawings, essays, projects)
Generate explanations from textbook pages

Internal Operations:

Scan invoices and extract data
Analyze design mockups and provide feedback
Review presentation slides for branding consistency

Sales & Marketing:

Analyze competitor landing pages
Generate descriptions from product photos
Review ad creatives for compliance

3. Faster, More Accurate Resolutions

Problem with text-only:

Customers struggle to describe visual issues
Back-and-forth clarification wastes time
Miscommunication leads to wrong solutions

Advantage of multimodal:

Agents see exactly what the customer sees
Instant understanding, no guessing
Faster resolution, higher satisfaction

4. Reduced Friction

Text-only:

"Please describe your issue in detail, including any error messages, button
labels, and what you've already tried."

Most customers won't do this. They'll give up or email support (increasing ticket volume).

Multimodal:

"Upload a screenshot and I'll help you right away."

Much lower friction. Higher engagement.

Real-World Use Cases

Use Case 1: Technical Support

Scenario: A user encounters an error and doesn't know how to describe it.

Without multimodal:

User: "I'm getting an error when I try to log in."
Agent: "What does the error say?"
User: "Uh... something about authentication?"
Agent: "Can you copy the exact error message?"
User: "I don't know how to copy it."
[User gives up, emails support, waits 24 hours for human response]

With multimodal:

User: [uploads screenshot of error page]
Agent: "I can see the error: 'Authentication token expired. Please log out and
log back in.' This happens when your session expires. Click the 'Log Out'
button in the top right, then log in again with your credentials. Your data
will be preserved."
User: "That worked, thanks!"
[Issue resolved in 1 minute]

Use Case 2: Product Identification

Scenario: A customer sees a product in the wild and wants to buy it.

Without multimodal:

Customer: "I saw this cool lamp at a friend's house. Do you sell it?"
Agent: "Can you describe the lamp?"
Customer: "It's like... modern, with a curved base."
Agent: "We have many lamps. Can you be more specific about the color, size, material?"
[Customer gives up]

With multimodal:

Customer: [uploads photo of lamp]
Agent: "That's our Arc Floor Lamp in matte black! It's currently in stock for
$149. I can add it to your cart or send you a link to learn more."
Customer: "Add to cart!"
[Sale completed]

Use Case 3: Internal Documentation

Scenario: An employee is following a process guide and gets stuck on a diagram.

Without multimodal:

Employee: "I don't understand step 3 in the deployment guide."
Agent: "Step 3 says: 'Configure the load balancer as shown in Figure 2.'"
Employee: "Yeah, but I don't understand the diagram."
Agent: "Please refer to the deployment documentation."
[Employee asks a coworker instead, distracting them from work]

With multimodal:

Employee: [uploads screenshot of diagram] "I don't understand this part."
Agent: "In Figure 2, the arrows represent traffic flow from the load balancer
to backend servers. Each server in the pool receives requests round-robin.
The health check (green checkmark) indicates the load balancer only routes to
healthy servers. Does that clarify?"
Employee: "Yes, thanks!"
[Issue resolved without interrupting coworkers]

Use Case 4: Design Feedback

Scenario: Marketing team needs to ensure ad creatives meet brand guidelines.

Without multimodal:

Marketer: "Can you review this ad creative for brand compliance?"
Agent: "Please describe the ad and I'll check our brand guidelines."
[Too time-consuming — marketer emails brand team instead, waits 2 days]

With multimodal:

Marketer: [uploads ad creative image]
Agent: "I've reviewed the ad creative. Here's what I found:
✅ Logo placement: Correct (top right, correct size)
✅ Color palette: Matches brand colors (#0066CC, #FFFFFF)
❌ Font: Should be 'Gotham Bold' but this appears to be 'Arial Black'
⚠️ Tagline: Missing the ™ symbol after 'Acme'

Please correct the font and add the ™ symbol before publishing."
[Marketer fixes issues in 5 minutes, no approval bottleneck]

Use Case 5: Competitive Analysis

Scenario: Sales rep is on a call and competitor's website comes up.

Without multimodal:

Rep: "How do we compare to Competitor X?"
Agent: "Competitor X offers similar features but our pricing is more competitive."
Rep: "But they're saying they have AI-powered analytics. Do we have that?"
Agent: "I don't have information about their specific feature set."
[Rep loses deal due to lack of competitive intel]

With multimodal:

Rep: [shares screenshot of competitor's pricing page]
Agent: "Here's how we compare:

| Feature | Competitor X | Herm.Chat |
|---------|--------------|-----------|
| AI Agents | 5 | 10 |
| Price | $149/mo | $79/mo |
| RAG Support | ✅ | ✅ |
| Slack Integration | ❌ | ✅ |
| Multimodal | ❌ | ✅ |

We offer more agents, better integrations, and multimodal AI at nearly half
the price. Key differentiators: Slack integration and multimodal support."
[Rep wins deal with data-driven comparison]

How Multimodal AI Works

Step 1: Vision Models Encode Images

Modern LLMs include vision encoders that convert images into embeddings (similar to how text is embedded).

Example:

Input: Screenshot of an error page
Vision encoder output: Vector representation capturing visual elements (text, buttons, colors, layout)

Step 2: Combined Understanding

The AI model combines the image embedding with the text query to understand the full context.

Example:

Image: [Screenshot showing red error message]
Text: "Why am I seeing this?"

Combined understanding: The user is asking about the error message visible in
the screenshot. The error text says "Authentication failed." The model can now
explain the error and provide a solution.

Step 3: Reasoning Across Modalities

The model reasons across both text and image to generate a response.

Example:

User uploads invoice PDF + asks: "What's the total amount due?"

Model:
1. Uses vision to read the PDF
2. Identifies the "Total Amount Due" line
3. Extracts the value: $1,245.00
4. Generates response: "The total amount due is $1,245.00, with a due date of April 15, 2026."

Types of Multimodal Inputs

1. Screenshots

Use cases:

Error messages and troubleshooting
UI/UX feedback
Bug reports
Competitor analysis

What AI can extract:

Text (OCR)
UI elements (buttons, forms)
Layout and design
Error messages and states

2. Photos

Use cases:

Product identification
Visual search
Quality control
Inventory management

What AI can extract:

Object recognition
Brand logos
Text on packaging
Visual attributes (color, shape, size)

3. Documents (PDFs, Presentations)

Use cases:

Invoice processing
Contract review
Report analysis
Presentation feedback

What AI can extract:

Full text content
Tables and data
Charts and graphs
Document structure

4. Videos

Use cases:

Tutorial analysis
Product demos
Screen recordings
User feedback

What AI can extract:

Keyframes (images at specific timestamps)
Spoken words (audio transcription)
On-screen text
Visual actions and events

5. Diagrams and Charts

Use cases:

Data analysis
Architecture review
Process documentation
Technical explanations

What AI can extract:

Chart data and trends
Relationships between elements
Labels and annotations
Visual patterns

Getting Started with Multimodal AI Agents

Step 1: Identify Use Cases

Where would visual input be helpful?

Customer-facing:

Support agents (screenshot troubleshooting)
Product search (image-based search)
Visual FAQs (upload diagram, get explanation)

Internal:

IT helpdesk (screenshot diagnostics)
Design review (logo, ad, mockup feedback)
Document processing (invoices, receipts, contracts)

Step 2: Choose a Multimodal-Capable Platform

Not all AI agents support multimodal input. Look for:

Herm.Chat (supports GPT-4 Vision, Claude 3+ with vision)
OpenAI GPT-4 Vision (via API)
Anthropic Claude 3+ Sonnet/Opus (via API)
Google Gemini Pro Vision (via API)

Note: Multimodal features are typically available on Growth, Scale, or Enterprise plans due to higher computational costs.

Step 3: Update Your System Prompt

Tell your agent how to handle image inputs.

Example:

You are a customer support agent for Acme Corp.

When users upload screenshots or images:
1. Carefully analyze the visual content
2. Identify any error messages, UI elements, or relevant details
3. Reference specific elements from the image in your response
4. Provide step-by-step solutions based on what you see

Always describe what you observe in the image before answering, so the user
knows you understand their issue.

Step 4: Enable Image Upload in Your Interface

For website widgets:

Add an image upload button to the chat interface
Support drag-and-drop for images
Display uploaded images in the conversation

For Slack integrations:

Users can share images directly in Slack
Agent processes images automatically

For internal tools:

Enable file upload (images, PDFs, videos)
Display previews in conversation history

Step 5: Test with Real Scenarios

Create test cases:

Upload screenshots of errors
Share product photos
Send diagrams with questions
Submit invoices or receipts

Evaluate:

Does the agent correctly identify visual elements?
Are responses helpful and accurate?
Does it cite specific details from the image?

Step 6: Monitor and Optimize

Track:

What % of conversations include images?
Resolution rate for image-based queries
User satisfaction with image-based support

Optimize:

Refine system prompt based on failures
Add visual examples to documentation
Train team on how to encourage image uploads

Advanced Multimodal Techniques

1. Image + Document RAG

Combine image understanding with document retrieval.

Example:

User: [uploads diagram] "How does this relate to our architecture?"

Agent:
1. Analyzes the diagram
2. Searches internal architecture docs
3. Compares diagram to documented architecture
4. Provides contextualized answer

Response: "The diagram shows a microservices architecture with API Gateway,
which aligns with our documented architecture (see Architecture Guide v2.3,
Section 4). However, the database replication setup differs — we use active-
active replication, while this diagram shows active-passive."

2. Video Frame Analysis

Extract keyframes from videos and analyze them individually.

Example:

User: [uploads product demo video] "Can you summarize this demo?"

Agent:
1. Extracts keyframes every 5 seconds
2. Analyzes each frame
3. Transcribes audio
4. Generates summary

Response: "The demo shows 5 main features:
1. Dashboard overview (0:10)
2. Report generation (0:45)
3. Real-time collaboration (1:20)
4. Export options (2:05)
5. Integration setup (2:40)

Key highlights: Real-time collaboration supports up to 10 users, and exports
are available in PDF, CSV, and Excel formats."

3. Comparative Analysis

Compare multiple images side-by-side.

Example:

User: [uploads 2 ad creatives] "Which one follows brand guidelines better?"

Agent: "Comparing both creatives:

Creative A:
✅ Logo correct
✅ Brand colors
❌ Wrong font (Arial instead of Gotham)

Creative B:
✅ Logo correct
✅ Brand colors
✅ Correct font

Creative B is fully compliant with brand guidelines."

4. OCR + Data Extraction

Extract structured data from images and documents.

Example:

User: [uploads invoice] "Log this invoice in our system."

Agent:
1. Extracts invoice data via OCR
2. Structures the data
3. Returns structured output or creates entry

Response: "Invoice extracted:
- Invoice #: 2026-03478
- Vendor: Acme Supplies
- Amount: $1,245.00
- Due Date: 2026-04-15
- Items: Office supplies (5), printer toner (2)

Should I create an entry in your accounting system?"

Multimodal AI Pricing and Costs

Multimodal AI is more expensive than text-only due to higher computational requirements.

Typical cost structure:

Text-only queries: $0.01 - $0.03 per 1,000 tokens
Image queries: $0.10 - $0.50 per image (varies by resolution and model)
Video queries: $1 - $5 per minute of video

On Herm.Chat:

Free & Starter plans: Text-only
Growth plan: Multimodal available (limited images/month)
Scale plan: Multimodal with higher limits
Enterprise plan: Unlimited multimodal

Why the cost difference? Vision models require significantly more computational power than text-only models.

Best Practices for Multimodal AI Agents

1. Encourage Image Uploads Proactively

Don't wait for users to figure it out.

In the chat interface:

"Having trouble? Upload a screenshot and I'll help you faster!"

In error states:

"Upload a screenshot of the error and I'll diagnose it for you."

2. Describe What You See

Build trust by confirming you understand the image.

Bad:

"Try restarting the app."

Good:

"I can see the error message 'Network timeout' in your screenshot. This usually
happens when the connection is unstable. Try restarting the app, and if the
issue persists, check your internet connection."

3. Set Clear Boundaries

Tell users what types of images you can and can't help with.

Example:

System prompt: "I can analyze screenshots, product photos, diagrams, and
documents. I cannot:
- Identify people in photos (privacy)
- Provide medical advice based on images
- Authenticate items (e.g., 'Is this real or fake?')

4. Optimize for Mobile

Many users will upload images from their phones.

Best practices:

Support drag-and-drop and file upload
Optimize image display for small screens
Handle various image formats (JPG, PNG, HEIC)

5. Privacy and Security

Be mindful of sensitive images.

Best practices:

Don't log images containing PII without consent
Redact sensitive data before sharing with team
Comply with GDPR, CCPA, and other privacy laws
Allow users to delete uploaded images

The Future of Multimodal AI

Multimodal AI is evolving fast. Here's what's coming:

1. Real-Time Video Analysis

Instead of uploading pre-recorded videos, agents will analyze live video streams.

Use cases:

Virtual troubleshooting ("Show me your setup via webcam")
Live quality control on manufacturing lines
Real-time training and coaching

2. Audio + Visual + Text

Combining all three modalities simultaneously.

Use cases:

Analyze video calls and provide real-time feedback
Transcribe and summarize meetings with visual aids
Voice + visual commands for hands-free workflows

3. Generative Multimodal Outputs

Not just understanding images, but generating them.

Use cases:

"Generate a diagram of this architecture based on my description"
"Create a mockup based on this sketch"
"Turn this data into a chart"

4. Cross-Modal Search

Search using one modality and retrieve in another.

Use cases:

Upload an image, get related documents
Ask a question, get back a video explanation
Share a diagram, get a written summary

Getting Started Checklist

Ready to add multimodal capabilities to your AI agent?

Final Thoughts

Multimodal AI isn't just a nice-to-have feature. It's a fundamental shift in how customers and teams communicate with AI.

Why it matters:

Customers naturally communicate visually
Faster, more accurate resolutions
Unlocks entirely new use cases
Reduces friction and increases engagement

The companies that adopt multimodal AI early will have a significant competitive advantage — better customer experiences, more efficient teams, and faster problem resolution.

The question isn't whether to add multimodal capabilities.

It's how quickly you can deploy them before your competitors do.

Ready to deploy a multimodal AI agent?

Start Free — Try text-based agents on the Free plan. Upgrade to Growth or Scale to unlock multimodal capabilities. See the difference for yourself.