AI app for science and research · no coding needed
Specialist literature monitoring
Narrow relevance criteria and inspectable inclusion decisions.
Made for: R&D teams tracking one applied research topic
What it does for you
The problem
Broad alerts overwhelm researchers with irrelevant papers.
What it gives you
Reviewed literature monitoring digest
What you give it
Licensed or openly accessible paperstopic criteria
How it works, step by step
- Screen topical relevance
- Identify duplicate versions
- Extract study context
- Summarize reported findings
- Preserve limitations
- Publish cited digests
What you see on screen
- Topic feed
- paper evidence
- digest review
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 Specialist literature monitoring 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 Specialist literature monitoring 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 links1 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare22 KB
- prompt-vps.mdThe same build on your own server (Docker)22 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data202 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
For r&D teams tracking one applied research topic, turn licensed or openly accessible papers and topic criteria into reviewed literature monitoring digest. Address the recurring problem: broad alerts overwhelm researchers with irrelevant papers. The pilot measures relevant inclusions and missed key papers against the buyer's current method, before the larger build.
Agree a narrow watchlist, confirm lawful source access, collect dated snapshots, detect candidate changes, review relevance and accuracy, deliver a concise digest, and refine the watchlist from buyer feedback. Start with licensed or openly accessible papers and topic criteria and finish with reviewed literature monitoring digest.
How the AI works
Classify source material, group related developments and summarize verified changes. Use deterministic snapshot comparison for factual changes where possible. Distinguish observed publication content from analyst interpretation and uncertain implications.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
What to build first
Begin with r&D teams tracking one applied research topic and one recurring use case. Build the first two modules: screen topical relevance; identify duplicate versions. Provide operator assistance for the third module: extract study context. Deliver reviewed literature monitoring digest through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
What it can connect to
Authorized datasets, papers, protocols, code and research records. Permitted feeds, published document sources, email digests and internal briefing channels. Verify collection rights and source reliability before selling coverage commitments. These are candidate integration categories, not verified supported connectors.
The screens in detail
Use a watchlist with source health and last-checked dates, a chronological change feed, and a reviewable briefing editor. Display original evidence beside each alert. Let users mute irrelevant topics and record whether a change led to action. In this product, the first view is topic feed, followed by paper evidence and digest review.
