AI app for sales · no coding needed
Contract expansion usage economics lab
Offer growth only where documented customer needs justify it.
Made for: B2B account management teams

What it does for you
The problem
Expansion offers ignore whether customers receive value from existing usage.
What it gives you
Buyer-reviewed expansion case
What you give it
Customer-authorized aggregate task outcomesapproved price rules
How it works, step by step
- Identify verified capacity constraints
- Compare expansion scenarios
- Calculate customer-side economics
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned buyer-reviewed expansion case 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 Contract expansion usage economics 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 Contract expansion usage economics 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 build3 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
Offer growth only where documented customer needs justify it
Confirm the buyer's problem and scope, collect customer-authorized aggregate task outcomes and approved price rules, then follow this sequence: 1. Identify verified capacity constraints. 2. Compare expansion scenarios. 3. Calculate customer-side economics. Resolve uncertain cases with qualified reviewers, approve buyer-reviewed expansion case, and measure incremental customer value and seller contribution minus delivery 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 assumed willingness-to-pay from personal data; human review. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Keep product capabilities and commercial terms verified. Use authorized customer records and require review before outreach, promises or pricing exceptions. No assumed willingness-to-pay from personal data; human review. 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 assumed willingness-to-pay from personal data; human review. Implement one approved input format, a bounded representative case set and the first two task modules: identify verified capacity constraints; compare expansion scenarios. Support the third module with operator review: calculate customer-side economics. 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 sales collateral, CRM records and product or pricing information. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Data and definitions, Pattern investigation, Action and value review. Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. Make the task-specific outcome buyer-reviewed expansion case visible beside its evidence, review state and value baseline.





