AI app for sales · no coding needed
Sales-to-implementation promise bridge
Expose promise-to-delivery gaps before customer kickoff.
Made for: B2B implementation team leads

What it does for you
The problem
Customers expect features promised during sales but absent from delivery scope.
What it gives you
Reviewed implementation expectation brief
What you give it
Approved contractsconsented sales notes
How it works, step by step
- Extract capability promises
- Compare signed scope
- Flag unsupported dates
- Link approved exceptions
- Assign clarification owners
- Export onboarding brief
What you see on screen
- Promise evidence
- Delivery scope
- Handoff 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 Sales-to-implementation promise bridge 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 Sales-to-implementation promise bridge 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 data196 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 b2B implementation team leads, turn approved contracts and consented sales notes into reviewed implementation expectation brief. Address this specific problem: customers expect features promised during sales but absent from delivery scope. The aim: expose promise-to-delivery gaps before customer kickoff. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies approved contracts and consented sales notes, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final reviewed implementation expectation brief before use. Retain source links and a version history for the next cycle.
How the AI works
Compare stated promises with documented product capabilities. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
Safeguards
Keep product capabilities and commercial terms verified. Use authorized customer records and require review before outreach, promises or pricing exceptions. Internal preparation; contract meaning reviewed by authorized staff. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
What to build first
Costed pilot: Internal preparation; contract meaning reviewed by authorized staff. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract capability promises; compare signed scope. Support the third task through an assisted review queue: flag unsupported dates. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed implementation expectation brief. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
What it can connect to
Approved sales collateral, CRM records and product or pricing information. Source repositories, task trackers and report exports. Keep findings as review proposals until authorized owners accept the resulting actions. Begin with uploads and exports of approved contracts and consented sales notes. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
The screens in detail
Open on a review queue ordered by reviewer-selected priorities. Show each finding beside the original evidence and applicable rule. Provide accept, dismiss and needs-information controls with reasons. A separate report view summarizes confirmed findings and unresolved items, not raw AI flags. Open with promise evidence; move into delivery scope for the detailed task; finish in handoff review for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





