LeapSpace goes live: a research-grade AI workspace built on trusted science
Elsevier has launched LeapSpace, an AI-assisted workspace built on a vast base of peer-reviewed research. It's built for academic and corporate teams who need fast, defensible answers and a clear audit trail. Every output comes with sources, context, and evidence you can check.
Trust is still a sticking point with AI in research. Recent Elsevier studies report that only 22% of researchers trust existing AI tools, and 86% say these tools can cause critical errors. LeapSpace addresses that head-on with transparency, provenance, and content you can verify.
Why this matters for research teams
General AI tools often pull from unverified web content and hide how answers were formed. LeapSpace is different: it's grounded in peer-reviewed literature and shows you the steps used to generate each answer. That means less second-guessing and faster movement from question to protocol, report, or decision.
As one early industry user put it, the platform "advances rigor and transparency by providing traceable citations," with clickable sources and clearly structured tables that save time for regulatory readiness and broader research needs.
What's inside the content stack
LeapSpace brings a publisher-neutral approach and a growing collection of trusted sources. At launch, it includes:
- New licensing agreements with Emerald Publishing, IOP Publishing, NEJM Group, and Sage, with more on the way. Responses display referenced extracts with links back to the publisher's platform.
- 18+ million peer-reviewed articles and books from Elsevier, plus licensed subscription and open access content from other publishers and scholarly societies.
- The world's largest collection of abstracts from Scopus (100+ million records from 7,000+ publishers).
The result: high-quality answers grounded in structured, enriched, and linked data-not guesses.
Built for critical thinking, not black-box answers
LeapSpace shows the reasoning it uses to generate an answer in real time, so you can validate each step. Every insight is referenced back to the original source for provenance.
"Trust Cards" explain why sources were cited and flag contradictions, helping you judge the strength of the evidence. Researchers using LeapSpace report time savings in literature review, better study design, stronger collaboration, and new angles they might have missed.
One HCI researcher noted that it helps refine research directions and makes it easier to learn outside a primary domain-right down to running deep reports in the background and reviewing them on the go.
Model strategy, privacy, and security
LeapSpace uses a multi-model approach, selecting models per task to deliver the best outcome and flexibility as AI evolves. Data protection is handled at an enterprise level.
When third-party LLMs are used, sessions are private. Your data isn't stored to train public models, and information stays in a protected environment. Elsevier's responsible AI practices and privacy principles apply across the solution.
Practical use cases you can deploy now
- Evidence synthesis with traceable citations for grant writing, protocol design, and regulatory briefs.
- Comparative reviews that surface agreements and contradictions across studies.
- Cross-domain exploration to accelerate hypothesis generation and partner alignment.
- Shareable, structured outputs-tables, references, and links-built for team workflows.
Availability and next steps
LeapSpace is available now for institutions. Individual academics and students will be able to purchase in February 2026.
Learn more on Elsevier's product page: Introducing LeapSpace.
If you're building team capability
For researchers formalizing AI skills for literature review, analysis, or workflows, explore curated learning by role: AI courses by job.
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