Pricing

Automatically index your visual assets with Azure AI Vision and Swiftask

Swiftask connects Azure AI Vision to your workflows. Your images are analyzed, tagged, and categorized automatically, with no human intervention.

Result:

Turn your unstructured image libraries into actionable, instantly searchable databases.

Manual image tagging is a major bottleneck

For companies handling large volumes of visual content, manual tagging is slow, expensive, and error-prone. Files get lost in folders, making search impossible and asset management a nightmare.

Main negative impacts:

Operational time loss: Teams spend hours manually renaming and tagging thousands of images instead of creating.
Inefficient search: Without standardized tags, finding a specific asset is a daily challenge, slowing down projects.
Data inconsistency: Each collaborator uses their own tagging logic, creating informational chaos.

Swiftask automates auto-tagging by sending your images to Azure AI Vision. The AI extracts relevant metadata and Swiftask automatically applies it to your files.

BEFORE / AFTER

What changes with Swiftask

Without Swiftask

A designer uploads 500 images for a campaign. They have to open each file, identify the content, and manually enter keywords into your DAM or CRM. This takes days and is prone to human error.

With Swiftask + Azure AI Vision

As soon as an image is added to a monitored folder, Swiftask sends it to Azure AI Vision. The AI detects objects, colors, and context. Swiftask automatically updates your metadata. Your files are ready to use in seconds.

Set up intelligent auto-tagging in 4 steps

STEP 1 : Create your workflow in Swiftask

Define a trigger (new file in a folder) to start the analysis.

STEP 2 : Connect Azure AI Vision

Configure the Azure integration to analyze incoming images.

STEP 3 : Define tagging rules

Choose the types of tags to extract (objects, text, celebrities, colors).

STEP 4 : Automate storage

Swiftask writes tags back to your file management tool or database.

Advanced analysis capabilities

Azure AI Vision recognizes thousands of objects, scenes, and visual concepts.

Target connector: The agent performs the right actions in azure ai vision based on event context.
Automated actions: Keyword extraction, Optical Character Recognition (OCR), brand identification, dominant color analysis, and content moderation.
Native governance: Swiftask centralizes analysis results for complete control over your assets.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-azure-ai-vision@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

Benefits for your productivity

1. Massive time savings

Automate 100% of repetitive tagging tasks.

2. Instant search

All your images become searchable by keyword.

3. Standardization

Ensure uniform nomenclature across all assets.

4. Scalability

Manage millions of images without adding headcount.

5. Seamless integration

Connect to your current tools via Swiftask.

Security and compliance

Swiftask applies enterprise-grade security standards for your azure ai vision automations.

Data encryption: All communications with Azure are fully secured.
Access control: Manage who can access analysis results.
GDPR compliance: Handling your visual data with privacy in mind.
Full audit trail: History of every image processed.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Measurable impact

Metric	Before	After
Indexing time	Minutes per image	Milliseconds per image
Tag accuracy	Variable (human)	High (Azure AI)
Volume handled	Effort-limited	Unlimited

Take action with azure ai vision

Turn your unstructured image libraries into actionable, instantly searchable databases.

Automatically extract text from documents with Azure AI Vision

Next use case