Standard tools failed on tables and equations. Intelligent parsing extracted 4M pages of scientific PDFs for model training.
An enterprise AI platform needed to build a comprehensive training dataset from every NLP research paper published since 2017, totaling approximately 4 million pages of PDF content.
Standard open-source tools struggled to accurately extract complex elements like tables, charts, and equations from scientific documents. These...
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Standard parsers couldn't read scientific charts. AI now extracts visuals into text, making hidden data searchable.
Malformed PDFs forced engineers to manually patch pipelines. AI agents now parse complex files into clean markdown, ending manual fixes.
Building models took months. Now, experts annotate docs to guide the AI, delivering functional solutions 90% faster.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.