Read the New Aviary Insights Post "Enabling Description of Under-Described Archival Video Collections through AI"

[Kevin Glick]

The "discoverability gap" is real: thousands of hours of rich archival video are currently invisible due to sparse, manual metadata. We're changing that.

Our latest deep-dive explores how Aviary is strategically implementing AI to create scalable, high-quality descriptive metadata. We're talking about combining Named Entity Recognition (NER), LLM Audio Analysis, and LLM Video Analysis to build a powerful metadata layer.

This isn't about replacing archivists; it's about augmenting their professional practice. AI breaks the backlog, allowing experts to focus on crucial human review and high-level interpretation.

Ready to see how this responsible AI approach can transform your collections?

👉 Read the full blog post and explore the AI services in the Aviary platform