Academic PDFs turned into machine-readable research.
Citations that hold up, every time.

A PDF full of figures and equations means nothing to a machine.

Academic research documents are dense and inconsistent — figures, tables, citations and equations sit buried inside static PDFs with no structure a system can read. Without embedded metadata, there was no reliable way to anchor a citation to its source or verify it was correct.

That same lack of structure meant researchers couldn’t query the documents efficiently. Finding a specific data point, table or citation meant manually searching through papers rather than asking a system to retrieve it directly.

Three constraints were non-negotiable:

• Complex documents packed with figures, tables, citations and equations
• No embedded metadata to anchor accurate citation
• No efficient way to query papers through AI

Every paper broken down, tagged, and made queryable.

Generative AI and large language models decompose each academic paper into its key components — figures, tables, citations and equations — rather than treating it as a single block of text.

Vision models sharpen the metadata extraction on top of that decomposition, enabling precise querying and retrieval of academic data. The tools were then integrated with existing academic systems, so citation and metadata embedding happen seamlessly within workflows researchers already use.

The result is a PDF that carries its own structure: a system that knows what a citation, a figure and an equation are, not just where the text sits on the page.

Citations that resolve. Queries that return the right answer.

Citation accuracy and query precision now come from the metadata itself, not manual review. Researchers reference and retrieve academic data faster, and the publishing process runs on structure the system built in from the start.