AI app for science and research · no coding needed
Reviewed research evidence and writing workspace
Reduce manual search, extraction and citation work while keeping every claim traceable to a source.
Made for: Researchers, research groups and academic writers producing literature reviews and papers
What it does for you
The problem
Papers, notes, extracted data and draft text sit in separate tools, so search, reading, extraction and writing are repeated by hand and citations drift out of sync.
What it gives you
Reviewer-approved evidence set with linked citations
What you give it
Licensed database resultsuploaded PDFsreading notesdraft text
Build your own version of Elicit, LitRevu and more
One app with what these 10 AI tools do, yours to keep and change: Elicit, LitRevu, Deep Review by SciSpace, ResearchGPT, Paperguide, OpenRead, Enago Read, Sourcely, SciSpace AI Academic Writer, Undermind (YC S24).
Everything these tools do, in one app
- Paper Search Finds relevant academic papers from large databases or the web.Found in Elicit, Deep Review by SciSpace, ResearchGPT and 4 more
- Paper Summarization Generates concise summaries of academic papers to speed up understanding.Found in Elicit, LitRevu, ResearchGPT and 4 more
- Literature Review Generation Automatically creates literature review paragraphs or sections with citations.Found in Elicit, ResearchGPT, OpenRead and 1 more
- Data Extraction Pulls specific data points, including from tables, out of papers.Found in Elicit
- Citation Export Exports citations in formats suitable for reference managers or bibliographies.Found in Sourcely
- Reference Management Organizes citations, notes, and references in a central place.Found in Paperguide, Enago Read
- PDF Upload and Q&A Allows users to upload PDFs and ask questions to get answers from the document.Found in ResearchGPT, OpenRead
- AI Chat Interface Provides a conversational interface to ask questions and get explanations about papers.Found in Paperguide, Enago Read
- Academic Writing Assistance Helps draft and improve academic content with suggestions and corrections.Found in Paperguide, SciSpace AI Academic Writer
- Citation Suggestions Suggests relevant citations and references while writing.Found in SciSpace AI Academic Writer
- Grammar and Style Correction Checks and corrects grammar and style for formal academic tone.Found in SciSpace AI Academic Writer
- Note-taking System Provides tools to take and organize notes, including backlinks and outgoing links.Found in OpenRead
- Collaboration Tools Enables sharing drafts, workspaces, and feedback among researchers.Found in Elicit, Enago Read, SciSpace AI Academic Writer
- Personalized Recommendations Suggests relevant papers based on user interests or previous activity.Found in Enago Read
- Transparent Sourcing Shows supporting quotes or explanations for how information was extracted or selected.Found in Elicit, Deep Review by SciSpace
- Journal Templates Offers pre-built templates for writing journal papers.Found in OpenRead
- Research Community Provides seminars and a community for researchers to connect and collaborate.Found in OpenRead
How it works, step by step
- Search licensed academic databases and the open web for relevant papers
- Screen results against stated inclusion and exclusion criteria
- Summarize each paper with source-linked quotes
- Extract specified data points, including from tables
- Answer questions against uploaded PDFs
- Provide a conversational interface for explanations about a paper
- Organize citations, notes and references in one library
- Take notes with backlinks and outgoing links
- Draft literature review sections with citations
- Suggest relevant citations while writing
- Check grammar and formal academic style
- Apply journal templates to drafts
- Export citations in reference-manager and bibliography formats
- Share drafts and workspaces for feedback
- Recommend papers from stated interests and prior activity
- Show supporting quotes and explanations for extracted or selected information
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved evidence set with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Reviewed research evidence and writing workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Reviewed research evidence and writing workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data198 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual search, extraction and citation work while keeping every claim traceable to a source. For researchers, research groups and academic writers producing literature reviews and papers, convert licensed database results, uploaded PDFs, reading notes and draft text into a reviewer-approved evidence set with linked citations. The benefit is a testable hypothesis, measured through accepted review sections per research hour and citation corrections after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect licensed database results, uploaded PDFs, reading notes and draft text, then follow this sequence: 1. Search licensed academic databases and the open web for relevant papers. 2. Screen results against stated inclusion and exclusion criteria. 3. Summarize each paper with source-linked quotes. 4. Extract specified data points, including from tables. 5. Draft literature review sections with citations. Resolve uncertain cases with qualified reviewers, approve the reviewer-approved evidence set with linked citations, and measure accepted review sections per research hour and citation corrections after review against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve author voice, source attribution, quotation accuracy and usage permissions. Researchers approve substantive changes and publication scope. One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One defined review protocol and citation style; final screening, extraction and scholarly claims remain researcher decisions. Implement one approved input format, a bounded representative case set and the first two task modules: search licensed academic databases and the open web for relevant papers; screen results against stated inclusion and exclusion criteria. Support the remaining modules with operator review: summarize each paper with source-linked quotes; extract specified data points, including from tables; draft literature review sections with citations. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Author-owned manuscripts, licensed database APIs, reference managers and journal submission destinations. Start with file exchange and validate destination specifications before promising direct submission. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Search and screening, Evidence and extraction, Draft and citation review. Use a thumbnail gallery for projects, a large central reading and extraction canvas, and a right-hand panel for sources, notes and comments. Let users compare paper versions and draft versions side by side. Display screened, extracted, drafted and approved states. Provide a shared workspace link with comments anchored to the relevant paper, table or paragraph. Make the task-specific outcome reviewer-approved evidence set with linked citations visible beside its evidence, review state and value baseline.
