AI app for science and research · no coding needed
Shared research instrument queue optimizer
Increase useful instrument access without extending operating hours.
Made for: University core facilities
What it does for you
The problem
Instrument capacity is lost to incompatible setup sequences.
What it gives you
Facility-professional-approved scheduling experiment
What you give it
Approved booking needstechnician-defined constraints
How it works, step by step
- Group compatible runs
- Simulate queue policies
- Compare setup and access tradeoffs
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned facility-professional-approved scheduling experiment 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 Shared research instrument queue optimizer 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 Shared research instrument queue optimizer 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 build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare28 KB
- prompt-vps.mdThe same build on your own server (Docker)28 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria15 KB
- demo/index.htmlThe working demo on sample data197 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
Increase useful instrument access without extending operating hours
Confirm the buyer's problem and scope, collect approved booking needs and technician-defined constraints, then follow this sequence: 1. Group compatible runs. 2. Simulate queue policies. 3. Compare setup and access tradeoffs. Resolve uncertain cases with qualified reviewers, approve facility-professional-approved scheduling experiment, and measure accepted instrument hours gained minus coordination and setup costs against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. No autonomous instrument operation or safety decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. No autonomous instrument operation or safety 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: No autonomous instrument operation or safety decisions. Implement one approved input format, a bounded representative case set and the first two task modules: group compatible runs; simulate queue policies. Support the third module with operator review: compare setup and access tradeoffs. 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
Authorized datasets, papers, protocols, code and research records. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Constraint and input setup, Scenario comparison, Decision and pilot tracker. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Make the task-specific outcome facility-professional-approved scheduling experiment visible beside its evidence, review state and value baseline.
