Description
DistillerSR is an AI-enabled evidence management platform designed to automate the conduct and management of literature-based research and surveillance. Trusted by over 250 companies, including 80% of top pharmaceutical and medical device firms, DistillerSR transforms literature evidence into a shareable organizational asset, reducing costs and accelerating research delivery.
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The platform's core capabilities revolve around intelligent automation and AI, evidence reuse, data extraction and reporting, seamless data integration, and expert managed services. Intelligent Automation & AI features include AI-driven screening that reduces screening time by up to 70%, configurable workflows for standardization, and an audit-ready integrated trail for 100% traceable evidence. This AI automation lowers costs and eliminates repetitive tasks throughout the literature review pipeline.
Evidence Reuse is facilitated by eliminating costly rework and reducing evidence silos through centralized access. This allows organizations to reuse extracted data, preventing duplicate purchases and ensuring consistency. The Extract & Report capabilities offer up to 70% reduced data extraction time with AI-enabled forms and human-in-the-loop validation. A customizable reporting engine provides faster, more insightful analysis that can be shared across an organization.
Data Integration capabilities allow for direct connection to corporate libraries like RightFind and PubMed, as well as business intelligence tools such as Power BI and Tableau, for enhanced visualization and analysis. Evidence enrichment through third-party integrations further enhances the data. Expert Managed Services are available to reduce costs and accelerate time to value by leveraging DistillerSR's team expertise, freeing up internal teams to focus on core competencies.
DistillerSR's approach to AI emphasizes security, control, and compliance, with AI models hosted within a secure ecosystem and customer-led processing controls to respect publisher agreements. Data isolation, no training on customer references, and aggressive grounding with attribution ensure data privacy and transparency. This makes DistillerSR a powerful tool for global research organizations seeking to improve decision-making, accelerate project timelines, and mitigate risks associated with evidence synthesis.
- CategoryAI Research Assistants
- Pricing ModelContact Us
- DeploymentCloud
- PlatformsWeb
DistillerSR's Core Features
AI-enabled automation for literature reviews
Automates literature-based research and surveillance
Reduces costs and accelerates research delivery
Configurable and auditable workflows
Intelligent automation and AI for screening and data extraction
Evidence reuse to eliminate silos and rework
Centralized evidence access and management
AI-enabled forms and data standardization
Customizable reporting engine
Direct integration with corporate libraries and BI tools
Expert managed services for cost savings and faster time to value
100% traceable evidence with integrated audit trail
AI Rerank and AI Classifiers for intelligent screening
Purpose-built GenAI for Smart Evidence Extraction (SEE)
Agentic AI for drafting regulatory reports and synthesizing insights
How to use DistillerSR?
Configure: Customize workflows and AI settings to match your research needs.
Automate Screening: Utilize AI classifiers and reranking to prioritize relevant references.
Extract Data: Employ AI-enabled forms and human-in-the-loop validation for efficient data extraction.
Reuse Evidence: Leverage centralized data to prevent rework and ensure consistency.
Integrate Data: Connect digital libraries and BI tools for comprehensive analysis.
Report Findings: Utilize the customizable reporting engine for insightful analysis and sharing.
Manage Evidence: Maintain a centralized, auditable repository of literature evidence.
DistillerSR's Use Cases
- Automated Literature Reviews
- Evidence Management
- Regulatory Submissions
- Post-Market Surveillance
- Cost-Effectiveness Analysis
- Clinical Guideline Development
- Research Acceleration
- Data Extraction
- Evidence Synthesis
