OpenAI Vision API

Let customers send images and get AI-powered visual answers instantly

Customers share product photos, screenshots, or documents in chat. Your AI agent analyzes them with GPT-4o vision, extracts relevant details, and responds with accurate visual understanding. Support becomes visual, not just textual.

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Visual intelligence inside every conversation

Your AI agent gains the ability to see and interpret images customers share, turning photos into actionable answers using GPT-4o multimodal understanding.

OpenAI Vision API

Use Cases

Visual AI support scenarios

From product identification to document verification, see how image-understanding transforms the conversations your AI agent handles daily.

Instant Visual Damage Assessment

A customer photographs a broken item and sends it through chat. Your AI Agent forwards the image to GPT-4o Vision, receives a description of the visible damage, and automatically determines whether it qualifies for replacement under your policy. The customer gets a resolution path in seconds. Your support team handles only the exceptions that need human judgment.

Screenshot-Based Technical Troubleshooting

A user shares a screenshot of an error message they cannot describe in words. Your AI Agent reads the image with OpenAI Vision, identifies the error code and context, and walks the customer through the fix step by step. No more asking customers to type out error messages. Resolution happens visually, the way the problem was reported.

Photo-Powered Product Discovery

A shopper photographs an item they saw in a magazine and asks if you carry something similar. Your AI Agent analyzes the image, identifies the style and category, and returns matching products from your store. Browsers convert to buyers because they found exactly what they pictured, literally. Visual search drives revenue your text-only chatbot never could.

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OpenAI Vision API

OpenAI Vision API

FAQs

Frequently Asked Questions

Which OpenAI model does the Vision API integration use?

The integration uses the GPT-4o-mini model by default via OpenAI's Responses API. This model supports multimodal input, meaning it can process both text prompts and image URLs in a single request. You can configure the model parameter to use gpt-4o or other vision-capable models depending on your accuracy and cost requirements.

What image formats can customers send for the agent to analyze?

The agent accepts any publicly accessible image URL, including JPEG, PNG, GIF, and WebP formats. When a customer uploads a photo through your chat widget, it gets hosted and the URL is passed to GPT-4o Vision. The model handles most standard image formats and resolutions that web browsers support.

Does Tars store the images customers share during conversations?

Tars processes image URLs in real-time and passes them to OpenAI's API for analysis. The image data is not permanently stored by Tars after the conversation. OpenAI's data retention policies apply for the API call itself. For sensitive image data, review OpenAI's enterprise data processing terms.

Can the agent analyze multiple images in a single conversation turn?

Yes. The OpenAI Responses API accepts an array of content items, so the agent can include multiple input_image objects alongside text prompts in one request. A customer can share several photos, and the agent processes them together for comparison or comprehensive analysis.

How accurate is the image analysis for product identification?

GPT-4o Vision is strong at identifying objects, reading text, and describing visual content. It works well for product categories, brand logos, and general item recognition. For highly specialized domains like medical imaging or industrial inspection, accuracy depends on the specificity of your prompts. Custom instructions improve results significantly.

What is the latency for processing an image through the agent?

Typical response times range from 2 to 8 seconds depending on image complexity, model selected, and prompt length. GPT-4o-mini is faster and cheaper, while GPT-4o provides more detailed analysis. For most customer support scenarios, the response feels near-instant within a chat conversation.

How is this different from using OpenAI's chat interface directly?

The direct OpenAI interface requires users to have an account and navigate a separate platform. With Tars, the vision capability is embedded inside your customer-facing chat agent on your website or WhatsApp. Customers never leave your channel. The agent combines visual analysis with your business context, product data, and support workflows.

Can I control what types of images the agent will process?

Yes. Through your agent's gambit configuration, you can set rules for when the vision tool activates. For example, only process images when the conversation involves product support or document verification. You can also add pre-processing prompts that guide the model to focus on specific visual elements relevant to your business.

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