Intelligent image tagging, automated by AISwiftask integrates with Imagior to automatically analyze and tag your visual assets. Stop sorting through thousands of images manually.Result:Drastically reduce file search time and optimize your digital asset management.The organizational chaos of manual taggingManaging an image library without an effective tagging system is a major time sink. When files are poorly indexed, your teams spend hours looking for the right visual instead of creating.Main negative impacts:Time wasted searching: Team members waste precious time navigating unstructured folders, lacking precise metadata.Database inconsistency: Every user uses their own terminology, making global searches inefficient and complex.Hidden operational costs: Time spent by skilled resources manually classifying images represents a huge cost for the company.Swiftask connects Imagior to a specialized AI agent that analyzes the content of each image and automatically applies relevant tags based on your taxonomy.BEFORE / AFTERWhat changes with SwiftaskWithout SwiftaskA designer uploads 500 photos from a shoot. They must open each image, analyze its content, and manually enter keywords. The process takes hours and input errors are common.With Swiftask + ImagiorAs soon as files are uploaded to Imagior, your Swiftask agent instantly analyzes the visuals. It identifies objects, colors, context, and applies exact tags in seconds.Start for free4 steps to automate your taggingSTEP 1 : Configure your agent in SwiftaskDefine the tagging rules and taxonomy specific to your business within the Swiftask interface.STEP 2 : Connect your Imagior accountLink your Imagior space to Swiftask via our secure connector to enable real-time analysis.STEP 3 : Set the automation flowChoose whether the agent should tag automatically upon upload or after human validation.STEP 4 : Activate and save timeThe agent processes every new addition. Your libraries are perfectly organized without effort.Start for freeKey AI tagging featuresThe AI analyzes visual properties (objects, text in image, colors) and business context provided by your reference documents.Target connector: The agent performs the right actions in imagior based on event context.Automated actions: Automatic tagging based on visual content. Normalization of tags according to your taxonomy. Multilingual tag support. Detection of sector-specific objects.Native governance: You keep full control: generated tags are editable and the AI learns from your corrections.Each action is contextualized and executed automatically at the right time.Each Swiftask agent uses a dedicated identity (e.g. agent-imagior@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.Start for freeWhy choose this automation?1. Instant searchFind any asset in seconds thanks to rigorous indexing.2. Consistent qualityAI doesn't get tired and applies the same tagging rules for every image.3. Increased productivityFree your teams from repetitive tasks so they can focus on creation.4. Unified taxonomyEnsure perfect consistency across your entire organization.5. ScalabilityManage millions of images as easily as a few hundred.Start for freeSecurity and privacySwiftask applies enterprise-grade security standards for your imagior automations.Protected data: Your images and metadata are processed securely and are never used to train public models.Access control: You define precisely who has access to automation configurations.To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.Start for freeRESULTSImpact on your productivityMetricBeforeAfterTagging time per image2-5 minutesLess than 2 secondsSearch timeSeveral minutesInstantIndexing error rateHigh (human)Almost zeroStart for freeTake action with imagiorDrastically reduce file search time and optimize your digital asset management.Book a demo7-day free trialDistribute your Imagior content with precision using AINext use case