LetterMate, Legal documents
Twenty guided workflows that turn facts into formal letters
A guided document product built around the facts a user needs to provide before generating a letter.
Built for: Consumer legal-letter product
- letter types
- 20
- letter types
- letters generated
- 12,000+
- letters generated
- lower cost vs lawyer-led creation
- 70%
- lower cost vs lawyer-led creation
First agent live in one week. A short call. Your first agent, a fixed quote, and the timeline. No obligation.
Sound familiar?
- “People know what happened but not how to write it as a formal letter.”
- “A blank chat box leaves users guessing which details matter.”
- “Paying a lawyer for a standard dispute letter is slow and expensive.”
How the system works.
- Step 1
Twenty letter workflows
Rent disputes, deposit returns, parking appeals, refund requests, subscription cancellations, employment grievances, and more, each with its own form.
- Step 2
Questions specific to each letter
A rent-increase dispute asks for the notice date, current rent, proposed rent, and reason, so nothing important is missed.
- Step 3
Structured drafts, not chat replies
OpenAI returns body paragraphs as JSON, and the application formats and cleans them into a proper document.
- Step 4
Export, history, and billing
Users save letters, export PDFs, and manage subscriptions and checkout in the same product.
What changed.
- 20 letter types, each with its own guided questions
- 12,000+ letters generated
- 70% lower cost than lawyer-led letter creation
- Every user moves from a specific issue to a reviewable, exportable letter
The AI inside.
OpenAI GPT-4.1
Drafts letter-specific body paragraphs as structured JSON from the facts the user provides
The full stack.
Frontend
- Next.js 15
- React 19
- React Hook Form
- Zod
Backend
- Node.js
- JWT
- bcrypt
- Email integrations
Data
- MongoDB
- Mongoose
AI and automation
- OpenAI GPT-4.1
- jsPDF document export
Integrations
- Stripe
This fits you if...
- Your team writes the same kinds of letters or documents again and again
- Drafts need specific facts that people forget to include
- You want generated documents in a consistent, reviewable format
Start with a free Agent Audit. First agent live in one week
The Document Intake Agent brings this to your team.
Every contract, invoice, and bid pack becomes a checked record, with the source page attached.
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Project overview
Twenty letter workflows built around the facts a user needs to provide
Services: Document AI ; AI Workflow Automation
Delivery: Guided legal-letter product, document generation, subscriptions, and administration.
Project Snapshot
| Figure | What it represents |
|---|---|
| 20 letter types | Guided workflows for different letter needs |
| 12,000+ letters | Letters generated in the reported project period |
| 70% lower cost | Reported comparison with lawyer-led letter creation |
The project reports 12,000+ generated letters and 70% lower cost compared with lawyer-led letter creation.
The Problem
The product serves users who know what happened but do not know how to turn it into a formal letter. A rent dispute needs different information from a refund request or an employment grievance.
An unrestricted chat interface would leave users responsible for choosing the right prompt and remembering the required facts. LetterMate needed guided forms and letter-specific generation.
What We Delivered
The generator covers twenty defined workflows, including rent disputes, deposit returns, parking appeals, refund requests, subscription cancellation, and employment grievances.
Each workflow collects its own fields. For a rent-increase dispute, the generator uses details such as the notice date, current rent, proposed rent, and reason for the dispute. It requests structured body paragraphs rather than returning an unformatted chat response.
The application adds user accounts, saved-letter history, PDF export, subscriptions, checkout, support, and administrative views.
Engineering Decisions
Write prompts around a letter type. The required inputs and output instructions change with the user's task.
Return a document structure. The generator requests JSON containing body paragraphs. Application code parses and formats the response for the document view.
Keep formatting out of the user's hands. Cleanup removes unwanted subject lines and placeholders from the generated body.
Support repeat use. Letter history, billing, and account management are part of the product rather than a separate manual service.
Results
The delivered product gives users a path from a specific issue through a questionnaire to a reviewable letter and export, with 12,000+ letters generated and a reported 70% lower cost compared with lawyer-led letter creation.
The code supports the twenty-workflow scope. It uses prompt-based OpenAI generation; it does not establish proprietary fine-tuning. Generated drafts still require user review and are not a guarantee of a legal outcome.
Technology stack
Next.js 15, React 19, MongoDB, Mongoose; OpenAI with a configurable model defaulting to GPT-4.1 in the generator; React Hook Form, Zod, jsPDF, formatting utilities; Stripe; JWT, bcrypt, and email integrations.
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Want this for your team?
Book a short audit call and show us the workflow. You leave with the first agent we would build, a fixed quote, and the timeline.
