DistillerSR Launches First-of-Its-Kind Agentic AI Capabilities to Eliminate Evidence Silos and Accelerate Time to Market

New Capabilities Place Peer-reviewed Literature Review Evidence at the Fingertips of Research and Commercial Teams to Align Decision-making and Improve Productivity

OTTAWA, ON / ACCESS Newswire / September 17, 2025 / DistillerSR, the market leader in AI-enabled literature review automation and evidence management, today announced the launch of DistillerSR Agentic AI. The first-of-its-kind capability combines DistillerSR trusted, expertly curated literature evidence with Agentic AI, enabling research teams and business leaders to access a validated, organization-wide source of evidence. With improved time to insight and more aligned decision-making, pharmaceutical and medical device companies can develop and commercialize new treatments faster and more cost-effectively.

Unlike standalone large language models, which can generate inaccurate or non-compliant content, DistillerSR Agentic AI safeguards research accuracy by grounding outputs in expertly validated and peer-reviewed literature evidence. This ensures that any generated summaries, reports, or insights meet the highest standards of reliability, reproducibility, and traceability-critical for regulatory submissions, post-market surveillance, health technology assessments, and scientific communications, among other use cases.

“DistillerSR Agentic AI represents a giant leap forward in how life science organizations can manage, access, and act on evidence from literature reviews,” said Peter O’Blenis, CEO at DistillerSR. “For the first time, organizations have a secure, trusted way to access their expertly curated literature evidence and make it available across other teams, enabling faster, more reliable decision-making for regulatory, clinical, medical affairs, and commercial teams.”

Accessible, Trusted Evidence Company-wide

DistillerSR Agentic AI dramatically reduces the time and manual effort required for research-intensive tasks. Teams can now accelerate the drafting of regulatory reports, summarize complex findings, uncover new insights with unprecedented speed, and even configure their DistillerSR’s workflows. The new capability applies DistillerSR’s existing permission controls and copyright considerations, allowing validated data to be made available with appropriate access controls to an entire organization with confidence.

Recognized AI Market Leadership

DistillerSR has been leading the market with the application of AI-enabled technologies to literature review workflows for more than 15 years and DistillerSR team member have had their research on AI and literature-review automation cited in peer-reviewed publications more than 670 times. Recent innovations have included:

  • November, 2024: Smart Evidence Extraction (SEE), a purpose-built GenAI expert-in-the-loop solution for literature evidence extraction.

  • September, 2024: The adoption of the National Institute of Standards and Technology (NIST) AI RMF for all future AI-related product releases as an operating and governing foundation.

  • December, 2023: Successful funding by five national governments under the Eureka-ITEA collaboration, the world’s largest international research and development network representing 45 countries, to fully automate literature reviews through composite or blended AI methods. The consortium is led by DistillerSR, and includes Philips Medical Systems, University of Leiden Medical Center, Bosonit,Dataseen BV, Össur, Adelphi Group, Biotronik, and DrugBank.

DistillerSR is trusted by global research organizations, including more than 80% of the largest pharmaceutical and medical device companies, contract research organizations, governments, NGOs, and academic institutions.

About DistillerSR
For more information, visit www.distillersr.com.

Contact Information

Vivian MacAdden
Director, Industry Stakeholder Programs
vivian.macadden@distillersr.com
(613) 212-0051

SOURCE: DistillerSR Inc.

View the original press release on ACCESS Newswire

Staff

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