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
Accelerate data extraction by using SEE and Human-in-the-Loop workflows to manage the oversight of answers, or use SEE Automation to complete an entire scoping review.
Contextual
Processing
SEE processes all form questions simultaneously, understanding relationships between variables in context for simplified prompting and improved accuracy.
Enhanced Accuracy
SEE delivered an 8.3 percentage point improvement on the standardized LitQA benchmark compared to its previous version, including precise extraction from complex tables.
Built-to-be Compliant
No customer data is shared outside the DistillerSR ecosystem or is used for AI-model training. Development is governed by the NIST AI Risk Management Framework.
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Purpose-built GenAI for Literature Reviews
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