AI app for legal · no coding needed
Signature packet assembly verifier
Catch mechanical packet inconsistencies before signing.
Made for: Transactional law firms

What it does for you
The problem
Execution packs contain inconsistent names and missing exhibits.
What it gives you
Reviewed execution packet checklist
What you give it
Counsel-approved final documentssigning instructions
How it works, step by step
- Compare party names
- Check signature blocks
- Match exhibit references
- Flag missing pages
- Track counsel approval
- Export execution checklist
What you see on screen
- Packet inventory
- Field comparison
- Release checklist
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 Signature packet assembly verifier 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 Signature packet assembly verifier 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 data195 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 transactional law firms, turn counsel-approved final documents and signing instructions into reviewed execution packet checklist. Address this specific problem: execution packs contain inconsistent names and missing exhibits. The aim: catch mechanical packet inconsistencies before signing. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies counsel-approved final documents and signing instructions, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final reviewed execution packet checklist before use. Retain source links and a version history for the next cycle.
How the AI works
Extract fields and explain exact mismatches. 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
Preserve matter confidentiality, access boundaries and original evidence. Qualified professionals review legal interpretations and final client documents. Mechanical checks; legal sufficiency remains with counsel. 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: Mechanical checks; legal sufficiency remains with counsel. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: compare party names; check signature blocks. Support the third task through an assisted review queue: match exhibit references. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed execution packet checklist. 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
Authorized matter files, firm templates and approved legal knowledge collections. 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 counsel-approved final documents and signing instructions. 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 packet inventory; move into field comparison for the detailed task; finish in release checklist for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





