AI app for legal · no coding needed
Legal matter staffing economics planner
Improve matter economics through explicit task allocation assumptions.
Made for: Law firm practice leaders
What it does for you
The problem
Matter staffing ignores task complexity and supervision costs.
What it gives you
Partner-approved delivery plan
What you give it
Approved work breakdownsprofessional staffing constraints
How it works, step by step
- Match declared task requirements
- Compare staffing scenarios
- Preserve supervisory obligations
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned partner-approved delivery plan 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 Legal matter staffing economics planner 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 Legal matter staffing economics planner 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 Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria15 KB
- demo/index.htmlThe working demo on sample data197 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
Improve matter economics through explicit task allocation assumptions
Confirm the buyer's problem and scope, collect approved work breakdowns and professional staffing constraints, then follow this sequence: 1. Match declared task requirements. 2. Compare staffing scenarios. 3. Preserve supervisory obligations. Resolve uncertain cases with qualified reviewers, approve partner-approved delivery plan, and measure matter contribution improvement minus supervision and planning 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 automated employment decisions or assessment of lawyer worth. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve matter confidentiality, access boundaries and original evidence. Qualified professionals review legal interpretations and final client documents. No automated employment decisions or assessment of lawyer worth. 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 automated employment decisions or assessment of lawyer worth. Implement one approved input format, a bounded representative case set and the first two task modules: match declared task requirements; compare staffing scenarios. Support the third module with operator review: preserve supervisory obligations. 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
Authorized matter files, firm templates and approved legal knowledge collections. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Constraint and input setup, Scenario comparison, Decision and pilot tracker. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Make the task-specific outcome partner-approved delivery plan visible beside its evidence, review state and value baseline.
