Backend for Embedded Academic Resources (BEAR)¶

Open-source framework for semantic expert search, digital twin interactions, and easy academic data integration.

Why BEAR?¶

Finding experts is slow and fragmented. BEAR solves this with:

• Semantic search using plain language to find domain experts.
• Digital twins – AI-powered avatars built from an expert’s papers, talks, and datasets, enabling chat with their work for early engagement.
• One-click deployment for universities.

Features¶

• Quick Setup: Launch a proof-of-concept in minutes.
• Semantic Search: Plain language search with advanced embeddings, at resource or author level.
• Digital Twins: Chat with papers or expert-like avatars.
• AI Profiles: Auto-generated author profiles.
• Custom Data Integration: Integrate with your institution's internal data, or other data source.

Value¶

• Makes academic data accessible and conversational.
• Accelerates collaboration and discovery.

Quick Start¶

See Getting Started.