Paperguide

An AI-native platform for scientific research and evidence synthesis integrates literature discovery, reference management, cross-paper comparison, data extraction, citation writing, and auditable systematic reviews into a single research workflow.

AI Learning Tools#Literature Review#Evidence Extraction#Scientific Research Writing

Updated: 2026-10-26

What is Paperguide?

Paperguide is an AI-native platform for scientific research workflows and evidence synthesis, covering the entire process from discovering papers and organizing reference lists to synthesizing evidence and writing citations. It particularly emphasizes auditable systematic reviews: researchers not only receive summaries but can also record searches, screenings, and data extractions, with the review process generating corresponding reports to make it easier to verify conclusions against the source materials.

Who is it for?

College students, teachers and academic researchers who need to conduct literature reviews, graduation theses and cross-paper comparisons.
Medical, life sciences, and evidence synthesis teams conducting systematic reviews who are required to document the basis of literature searches and study selection.
Research institutions and consulting teams that need to extract fields from a large number of studies and generate structured evidence tables.
People who already use reference managers and want to connect data management with AI search and writing.

Key features

Discovery

Formulate research questions in natural language, and AI search generates comprehensive answers from papers while linking each conclusion to its source, reducing the barrier of having to guess the right keywords in advance.

Organization

The reference manager can import from Zotero or Mendeley, or add papers by Digital Object Identifier. Saved content can be further used for searching, systematic review, data extraction and writing.

Synthesis

The research assistant can compare multiple studies, provide an overview of the field, and organize the viewpoints scattered across individual papers into evidence that supports further reasoning.

Verifiable Data Extraction

Extract specified details from multiple papers and compile them into structured tables with citations, making it easy to check where each field originates.

Systematic Review

Supports pre-defining protocols, retaining retrieval and screening decisions, and emphasizes dual review and evidence for each criterion to form an auditable process.

Generate PRISMA from the Review Itself

Generate reports based on the actual review process, avoiding manually reconstructing the number and process of screenings by memory at the end of the project.

How to use Paperguide

First, formulate the research questions, inclusion and exclusion criteria, and extraction fields before initiating a search.
if the criteria are unclear, the faster the AI acts, the sooner it will accumulate inconsistent results.
After importing from a reference manager, perform deduplication first and verify metadata.
Errors in titles, authors, years, or identifiers will affect subsequent citation and screening.
Automated synthesis is suitable for identifying themes and discrepancies, but formal conclusions must be reviewed against the source document item by item, with particular attention paid to whether samples, methods, and scope of application have been omitted.
For systematic reviews, dual review and a complete audit trail should be used, and artificial intelligence should not replace independent judgment.
disagreements should be handled in accordance with pre-established protocols and explanations should be retained.
Full-text papers, data and citations involve licensing and privacy.
confirm authorization before import.
when the scope, price and reporting capabilities of the database change, refer to the latest instructions on the official website.