AI app for marketing · no coding needed
Customer onboarding content revenue lab
Connect content investment to observable customer progress.
Made for: B2B marketing and success teams
What it does for you
The problem
Educational content is produced without evidence of task completion.
What it gives you
Reviewed content activation experiment
What you give it
Consented task researchapproved product guidance
How it works, step by step
- Identify activation barriers
- Prototype task-specific content
- Test completion against baseline
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed content activation 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 Customer onboarding content revenue lab 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 Customer onboarding content revenue lab 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 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
Connect content investment to observable customer progress
Confirm the buyer's problem and scope, collect consented task research and approved product guidance, then follow this sequence: 1. Identify activation barriers. 2. Prototype task-specific content. 3. Test completion against baseline. Resolve uncertain cases with qualified reviewers, approve reviewed content activation experiment, and measure verified activation contribution minus content and assistance cost 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 unsupported retention attribution; one onboarding task. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Verify product claims and permissions. Distinguish observed campaign results from causal explanations and keep customer data collection authorized. No unsupported retention attribution; one onboarding task. 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 unsupported retention attribution; one onboarding task. Implement one approved input format, a bounded representative case set and the first two task modules: identify activation barriers; prototype task-specific content. Support the third module with operator review: test completion against baseline. 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
Approved brand material, campaign exports and authorized customer research. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Research question and consent, Evidence comparison, Reviewed findings and experiment. 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. Make the task-specific outcome reviewed content activation experiment visible beside its evidence, review state and value baseline.
