Smart Evidence Extraction
SEE the Difference. Trust the Evidence
Instantly Find, Suggest, Explain, Extract and Link Supporting Literature Evidence.
Designed for research professionals faced with time-consuming and error-prone data extraction processes, Smart Evidence Extraction (SEE) works in a fully automated or a human-in-the-loop workflow, using purpose-built GenAI, to reduce the time to extract data and improve the auditability of their reviews with linked evidence.
Increased Reviewer Productivity
Streamline the extraction process by finding, suggesting, explaining, extracting, and linking evidence, in a fully automated or a human in the loop workflow.
Intelligent Evidence
Synthesis
Leverage GenAI capabilities to extract data from tables, provide sentiment analysis and text summarization of reference sources.
Context-aware Responses
Get more accurate answers and suggestions you need through a AI model that has a contextual understanding of all the questions in an extraction form.
Responsible AI Development
Adherence to the NIST AI Risk Management Framework ensures that all AI models used by DistillerSR are trustworthy, reliable, and meet the highest ethical standards.
Related Resource
Purpose-built GenAI for Literature Reviews
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