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.
Related implementations across industries and use cases
Basic PDF readers failed on messy scans. A new engine now turns complex bank reports into clean, structured tables for analysis.
Malformed PDFs forced engineers to manually patch pipelines. AI agents now parse complex files into clean markdown, ending manual fixes.
Protein alignment took 20 mins per result. Vector search now scans 5 billion sequences in <1 minute, linking DNA to cellular images.
Basic PDF readers failed on messy scans. A new engine now turns complex bank reports into clean, structured tables for analysis.
Malformed PDFs forced engineers to manually patch pipelines. AI agents now parse complex files into clean markdown, ending manual fixes.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
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.
Related implementations across industries and use cases
Basic PDF readers failed on messy scans. A new engine now turns complex bank reports into clean, structured tables for analysis.
Malformed PDFs forced engineers to manually patch pipelines. AI agents now parse complex files into clean markdown, ending manual fixes.
Protein alignment took 20 mins per result. Vector search now scans 5 billion sequences in <1 minute, linking DNA to cellular images.
Basic PDF readers failed on messy scans. A new engine now turns complex bank reports into clean, structured tables for analysis.
Malformed PDFs forced engineers to manually patch pipelines. AI agents now parse complex files into clean markdown, ending manual fixes.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.