Back to Workflows
Visual Arts Archive Streamlining AI Metadata and RAID Storage
Company Situation
The company operates within the visual arts and archival services sector, specializing in managing extensive photographic and film archives. Their team services the art industry by preserving and organizing large-scale, sensitive media collections for both internal use and external companies. Their projects often involve decades’ worth of content, requiring meticulous databasing, serialization, and scanning workflows. The company handles high-resolution photography files and maintains local storage infrastructure designed for secure, reliable archiving.
Existing Workflow
Currently, the company relies on local RAID storage systems backed up redundantly to store tens of thousands of image files, predominantly in compressed JPEG format for metadata extraction purposes. They perform extensive manual databasing and organization but seek automation for metadata generation to improve efficiency. For company-facing workflows, they use Frame IO for collaborative media review and approval. The company uploads smaller JPEG files—not large TIFFs—to minimize storage and bandwidth demands. Metadata is primarily captured manually or semi-manually, focusing on physical attributes like film type rather than subjective content identification.
Issues with the Existing Workflow
Manual metadata entry is time-consuming and inconsistent, especially for large archives spanning decades.
Lack of scalable, automated metadata extraction creates bottlenecks during digitization and scanning phases.
Concerns about data privacy and intellectual property protection limit options for cloud-based AI tools.
Existing tools do not easily integrate or provide turnkey solutions for batch processing and metadata export.
Storage considerations are critical; the company does not want to offload or duplicate their entire archive to a cloud provider, only the necessary files for metadata extraction.
The company is cautious about AI training practices and wants assurances that their content will not be used to train third-party models.
How Shade Would Change Their Workflow
Shade offers a web-based platform that automates surface-level metadata extraction from image batches, significantly reducing manual effort. The company can upload compressed JPEG files for processing and export metadata in CSV format to integrate with existing database systems. Shade’s model respects strict data privacy and IP protections, ensuring the company retains full ownership and control over their media without it being used to train AI models. The platform aligns well with their existing use of Frame IO by complementing—not replacing—the collaborative review process. Shade’s scalable approach to storage and user management provides a flexible solution tailored to the company’s archival needs without requiring full cloud storage of all media files.
Benefits
- Automated, scalable metadata extraction reduces manual databasing workload.
- Strong data privacy and security protocols protect intellectual property and sensitive archives.
- Ability to upload only necessary image files (compressed JPEGs) minimizes storage overhead and bandwidth use.
- Metadata export in CSV format facilitates seamless integration with existing archival databases.
- Complements existing tools like Frame IO without disrupting established workflows.
- Access to enterprise-grade compliance standards including HIPAA, SOC2 Type 2, GDPR, and TPN certifications.
- Transparent AI usage policies provide confidence in data ownership and non-training assurances.
Technologies Used
Want to see how Shade can improve your workflow?
Book a Demo