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DistillerSR vs Universal SLR Assistant

DistillerSR

distillersr.com

Universal SLR Assistant

sadeghanisi.github.io

Side by side

FigureDistillerSRUniversal SLR Assistant
Price from$7.83/moFree
Free plan—Yes
Free trial——
Open source—Yes
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
APIYesYes
Self-hosted—Yes
Plans published61
Spec score · AI Literature Review Software7.9 (#16)8.2 (#10)
Spec score · Systematic Review Software7.8 (#20)8.2 (#6)
PurposeDistillerSR automates literature-based evidence synthesis and management for research and surveillance.Universal SLR Assistant supports systematic and scoping review workflows with AI-assisted screening, extraction, and reproducible outputs.
SupportThe platform page says support is available by phone, email, or chat, while the academic plan page lists unlimited email support from 8 am to 8 pm ET.The project directs users to the Complete User Guide, in-app Help panel, output logs, tests, and GitHub issues for support.

Plans

DistillerSR

  • Student$19.95 a month
  • Faculty$94 a year
  • Research Project$194 a year
  • Corporate & GovernmentPriced on request
  • DepartmentalPriced on request
  • InstitutionalPriced on request

DistillerSR pricing →

Universal SLR Assistant

  • Free and open sourceFree

Universal SLR Assistant pricing →

In their own words

DistillerSR

DistillerSR has no write-up on Specifiction yet; its figures are above.

Visit distillersr.com

Universal SLR Assistant

Universal SLR Assistant supports systematic and scoping reviews with AI-assisted screening, extraction, and reproducible outputs. It provides a desktop interface and a browser-based local Web App, and imports RIS, BibTeX, CSV, and plain-text references, including exports from PubMed, Scopus, and Web of Science. Its workflow covers abstract screening, full-text PDF processing, structured extraction, and export; likely duplicates are identified by DOI and fuzzy title matching. Workspace mode stores references, PDF metadata, review items, decisions, and audit events in a local SQLite workspace, separating AI suggestions from human decisions. Exports can include decision files and logs, exclusion reasons, PRISMA-ready counts, a methods disclosure draft, and a manifest. The project supports a range of cloud and local AI providers. Cloud providers receive submitted paper text, while Ollama or another local endpoint can keep it on the user's computer or local network. AI results are assistance only: people must verify them and make final review and reporting decisions. The software is free under the MIT License; cloud AI provider API fees may apply.

Visit sadeghanisi.github.io