Standard parsers couldn't read scientific charts. AI now extracts visuals into text, making hidden data searchable.
An AI-native biopharma intelligence platform enables teams to screen assets and benchmark competitors using data from clinical publications and regulatory filings.
Standard Python-based parsers could not interpret visual-heavy content like conference posters, charts, and scientific figures. Critical data...
“We could track those documents, but not truly interpret them. Critical information embedded in visuals was invisible to our models —the kind of data that drives real decisions in biopharma.”
AI platform for BioPharma knowledge work and clinical data analysis.
Data framework and agentic OCR platform for building LLM-powered applications.
Maven Bio's Scientific document processing is part of this use case:
Related implementations across industries and use cases
Legacy systems choked on 1,000-page medical files. AI now converts handwritten notes and 1970s scans into structured data.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Legacy systems choked on 1,000-page medical files. AI now converts handwritten notes and 1970s scans into structured data.
Abstractors spent 6 hours hunting details per complex case. Now, they validate AI findings in 90 mins, saving 6,000 annual labor hours.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Standard parsers couldn't read scientific charts. AI now extracts visuals into text, making hidden data searchable.
An AI-native biopharma intelligence platform enables teams to screen assets and benchmark competitors using data from clinical publications and regulatory filings.
Standard Python-based parsers could not interpret visual-heavy content like conference posters, charts, and scientific figures. Critical data...
“We could track those documents, but not truly interpret them. Critical information embedded in visuals was invisible to our models —the kind of data that drives real decisions in biopharma.”
AI platform for BioPharma knowledge work and clinical data analysis.
Data framework and agentic OCR platform for building LLM-powered applications.
Maven Bio's Scientific document processing is part of this use case:
Related implementations across industries and use cases
Legacy systems choked on 1,000-page medical files. AI now converts handwritten notes and 1970s scans into structured data.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Legacy systems choked on 1,000-page medical files. AI now converts handwritten notes and 1970s scans into structured data.
Abstractors spent 6 hours hunting details per complex case. Now, they validate AI findings in 90 mins, saving 6,000 annual labor hours.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.