Faster literature reviews with research-learning AI and citation context
Scholarly is an AI-powered research-learning assistant that accelerates literature review, synthesis, and evidence mapping across disciplines. It ingests PDFs and journal metadata to extract methods, results tables, and citation contexts, producing structured summaries and reproducible notebooks. Scholarly's key differentiator is paragraph-level provenance: every claim comes with a DOI-linked excerpt and confidence score, helping teams maintain audit trails. It serves PhD students, academic groups, and corporate R&D teams who need traceable literature synthesis. Scholarly includes a free tier with limited credits and affordable paid plans to keep the research-learning workflow accessible.
Scholarly launched in 2020 as a startup built by former academic engineers to solve the slow, error-prone process of triaging and synthesizing scholarly literature. Positioned between reference managers and general-purpose LLM tools, Scholarly combines semantic indexing over academic corpora with a robust PDF parser to produce evidence-backed summaries and exportable audit trails. Its core value proposition is reducing discovery-to-draft time while preserving source fidelity: every synthesized claim links back to a specific paragraph, DOI, and confidence metric so teams can reproduce or contest findings reliably.
Under the hood Scholarly exposes four tightly integrated capabilities. The PDF Extractor parses sections, tables, inline citations, and figures, returning structured JSON with paragraph-level confidence scores and normalized reference metadata. The Synthesizer produces context-aware summaries at user-chosen granularities (sentence, paragraph, or structured abstract) and highlights the exact source snippets that support each assertion. The Search Lens enables semantic search across integrated indexes such as PubMed, arXiv, and CrossRef while allowing boolean filters, date ranges, and methodology-specific masks. Finally, the Notebook export bundles source snippets, DOIs, the prompts used, and provenance tags into reproducible Jupyter or Quarto notebooks for audit and handoff.
Scholarly's pricing begins with a free tier that offers 20 full-paper ingestions and 50 semantic searches per month plus basic summaries and Zotero export. The Pro plan is $18/month billed annually and unlocks 500 ingestions, batch summarization, notebook exports, and priority API access. Research Plus is $49/month and adds team sharing, CrossRef bulk queries, higher API rate limits and SSO. The platform includes a 14-day Pro trial for new users. Enterprise options include on-prem or VPC deployment, volume licensing, custom connectors, seat-based billing, and a dedicated account manager with custom pricing and academic discounts.
Users range from graduate students compiling systematic reviews to corporate R&D teams preparing decision briefs. For example, a biomedical data scientist trimmed literature triage time by roughly 70% when preparing a systematic review using Scholarly's extractor and notebook exports. A policy analyst cut briefing prep time by two days per report through the Synthesizer and reproducible notebooks. University libraries and research groups adopt Scholarly to standardize intake workflows. Compared with tools like Elicit, Scholarly emphasizes PDF parsing, citation provenance, and exportable audit trails rather than being solely question-answer focused.
Paragraph-level provenance with DOI-linked excerpts made my systematic review auditable - saved hours verifying claims.
Parses methods and results tables from PDFs in ~30s and exports reproducible notebooks - cut our handoff time by days.
Free tier's 20 ingestions/month is too restrictive when working on full systematic reviews - otherwise extraction is excellent.