AI app for product development · no coding needed
Concept testing coordination
Real customer evidence with participant relevance and purchase signals documented.
Made for: Product teams considering a new offering
What it does for you
The problem
Early feedback comes from convenient rather than relevant participants.
What it gives you
Concept testing report
What you give it
Concept materialsparticipant criteriaconsent records
How it works, step by step
- Define participant criteria
- Prepare neutral questions
- Coordinate real sessions
- Collect reactions
- Distinguish interest from commitment
- Synthesize findings
What you see on screen
- Recruitment brief
- study sessions
- evidence synthesis
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 Concept testing coordination 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 Concept testing coordination 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 criteria11 KB
- demo/index.htmlThe working demo on sample data199 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 product teams considering a new offering, turn concept materials, participant criteria and consent records into concept testing report. Address the recurring problem: early feedback comes from convenient rather than relevant participants. The pilot measures participant relevance and validated learning against the buyer's current method, before the larger build.
Agree the decision and research questions, define permitted sources or participants, collect evidence, code findings, compare supporting and contradictory material, review interpretations, and deliver a cited brief with next questions. Start with concept materials, participant criteria and consent records and finish with concept testing report.
How the AI works
Assist with retrieval, transcription, structured extraction and thematic synthesis. Preserve source passages and methodological context. Human researchers validate inclusion, quotations and conclusions. Use real participants when customer research is required.
Safeguards
Use consented research and preserve contradictory evidence. Separate observed user behavior, proposed explanations and untested product assumptions. 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 product teams considering a new offering and one recurring use case. Build the first two modules: define participant criteria; prepare neutral questions. Provide operator assistance for the third module: coordinate real sessions. Deliver concept testing report 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
Product feedback, authorized interviews, usage exports and requirement records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. These are candidate integration categories, not verified supported connectors.
The screens in detail
Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. In this product, the first view is recruitment brief, followed by study sessions and evidence synthesis.
