Aureka

The platform applies artificial intelligence to the challenge of making large collections of audio, video, and document content searchable, analyzable, and actionable transforming passive content archives into dynamic knowledge resources that can be queried, indexed, and analyzed at scale.

Its academic origins reflect a focus on rigorous, research-grade information management rather than consumer-oriented content tools.

Automated transcription with speaker recognition is a foundational capability, enabling organizations to convert audio and video recordings into searchable text without manual transcription labor.

The system identifies and labels different speakers throughout a recording, making it possible to filter and search by speaker rather than only by keyword.

This is particularly valuable for interview archives, oral history collections, panel discussions, conference recordings, and any content type where attribution matters as much as the content itself.

Aureka's AI-powered semantic search goes beyond keyword matching to enable search by meaning finding content that is conceptually related to a query even when it uses different terminology than the search terms.

This semantic capability is essential for research use cases where the vocabulary of a question may differ from the vocabulary used in historical or technical source material.