Case Study: AI Metadata Embedding Boosts Citation Accuracy & Query Precision for Academic Research

Industry: Education, Research Technology

Client

Major Academic Research Collaboration

Goal

To develop tools that embed metadata into academic research PDFs, ensuring accurate citation and improved accessibility of academic resources.

Challenges

  • Developing a system capable of processing complex academic research documents.
  • Extracting and embedding metadata to ensure accurate citation.
  • Enabling efficient querying of documents through AI.

Solution

Built tools using generative AI and large language models (LLMs) to decompose academic papers into key components such as figures, tables, citations, and equations.

Applied vision models to enhance metadata extraction, enabling precise querying and retrieval of academic data.

Integrated the AI tools with existing academic systems to ensure seamless citation and metadata embedding.

Impact:

Developed a system that improved citation accuracy and query precision for academic research.

Enhanced academic workflows by enabling researchers to access and reference critical data more efficiently.

Facilitated more accurate referencing and information retrieval, streamlining academic publishing processes.

*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.