Divorce proceedings are emotionally exhausting. The last thing families and attorneys want is to wrestle with complex legal paperwork on top of everything else. One such document that consistently causes friction is the QDRO (Qualified Domestic Relations Order) — a legally sensitive, detail-heavy document that must be absolutely precise.
In this case study, we’ll walk you through how we helped LexyAlgo build a fully automated QDRO generation system using Docassemble, deployed securely on AWS, and designed to scale across multiple jurisdictions.
This isn’t a theoretical tutorial. It’s a real-world breakdown of legal automation, architecture decisions, interview logic, AWS setup, and lessons learned along the way.
The Problem LexyAlgo Faced
LexyAlgo works closely with family law attorneys handling retirement asset division during divorce. Their biggest challenges were:
- Manual QDRO drafting taking 2–4 hours per case
- High risk of errors due to inconsistent client inputs
- Repeated back-and-forth between attorneys and clients
- Difficulty scaling document preparation without hiring more staff
They needed a system that could:
- Ask the right legal questions in plain English
- Validate responses automatically
- Generate court-ready QDRO documents
- Work securely in the cloud
- Integrate into an existing legal workflow
That’s where Docassemble came in.
Why We Chose Docassemble for QDRO Automation
Docassemble is a powerful open-source platform purpose-built for legal automation. It allows developers to design guided interviews that convert structured answers into legally compliant documents.
For LexyAlgo, Docassemble was ideal because it supports:
- Conditional interview logic
- Python-based business rules
- DOCX and PDF generation
- Secure user authentication
- Cloud-friendly deployment
Combined with a robust docassemble AWS deployment, it became the backbone of the entire system.
The solution was hosted entirely on AWS to ensure security, scalability, and compliance.
Architecture Overview:
- AWS EC2 – Core Docassemble server
- PostgreSQL (RDS) – Interview data and session storage
- S3 Buckets – Generated QDRO documents
- Nginx + HTTPS – Secure traffic handling
- IAM Roles – Controlled access to resources
This setup allowed LexyAlgo to confidently host sensitive legal data while maintaining performance and uptime.
---
question: What type of retirement plan is being divided?
fields:
- no label: plan_type
datatype: radio
choices:
- Defined Benefit
- Defined Contribution
---
if: plan_type == "Defined Benefit"
question: What is the participant’s accrued benefit date?
fields:
- Accrued benefit date: accrued_date
datatype: date
This approach ensured:
- Users only saw relevant questions
- Attorneys didn’t need to re-explain legal concepts
- Error rates dropped significantly
This interview logic is a core principle we emphasize in every docassemble tutorial we build for legal professionals.
Automating Legal Logic with Python
Docassemble’s Python support allowed us to encode legal rules directly into the interview.
def marital_fraction(marriage_date, separation_date, plan_start_date):
total_service = separation_date - plan_start_date
marital_service = separation_date - marriage_date
return round(marital_service.days / total_service.days, 4)
This logic:
- Runs instantly
- Eliminates manual calculations
- Reduces human error
- Ensures consistency across cases
This is where legal automation truly shines — turning subjective legal workflows into deterministic, repeatable systems.
Document Assembly: From Answers to Court-Ready QDROs
Once the interview is complete, Docassemble merges responses into a professionally formatted DOCX template.
Key features we implemented:
- Dynamic clauses based on plan type
- Jurisdiction-specific language blocks
- Attorney branding and court formatting
- Automatic pagination and numbering
Docassemble’s document engine made it possible to produce ready-to-file QDROs in minutes instead of hours.
This is the power of document assembly on AWS — fast, reliable, and scalable.
Secure Hosting & Scaling on AWS
A critical requirement was ensuring the system could handle growth without re-architecture.
Our docassemble AWS deployment included:
- Auto-restart scripts for resilience
- Daily encrypted backups
- Environment-based configs (staging + production)
- Secure file access policies
This made the platform future-ready, allowing LexyAlgo to onboard new law firms without worrying about infrastructure limits.
If you’re looking to host Docassemble on AWS, this architecture is battle-tested and production-proven.
Lessons Learned from This Case Study
Building a QDRO automation system reinforced a few key truths:
- Legal automation must feel human
- Interview design matters more than UI
- AWS deployment should be simple, not over-engineered
- Docassemble is best used as a platform — not just a tool
Whether you’re building intake forms, court filings, or compliance documents, Docassemble offers unmatched flexibility when implemented correctly.
Final Thoughts
This LexyAlgo case study demonstrates how Docassemble, combined with AWS, can transform one of the most complex legal documents into a smooth, automated experience.
If you’re exploring docassemble deployment, legal automation, or document assembly on AWS, this approach can be adapted to virtually any legal workflow.
And if you want a partner who understands both law and technology, we’re here to help.
FAQ
1. What is a QDRO and why is it so hard to automate?
A Qualified Domestic Relations Order (QDRO) is a court-approved legal document that divides retirement benefits during a divorce. It’s difficult to automate because every QDRO depends on multiple variables—retirement plan type, jurisdiction, marital dates, survivor benefits, and plan-specific language. Even a small mistake can lead to rejection by the plan administrator. That’s why using Docassemble with carefully designed logic and validations is critical for accurate QDRO automation.
2. Why did you choose Docassemble for building the QDRO system?
Docassemble was chosen because it’s built specifically for legal automation. Unlike generic form builders, Docassemble allows conditional questioning, Python-based legal logic, and professional document assembly. This made it possible to convert complex QDRO rules into guided interviews that feel simple for users while remaining legally precise behind the scenes.
3. How does Docassemble reduce errors in QDRO preparation?
Docassemble reduces errors by asking only relevant questions, validating user inputs, and applying consistent legal calculations automatically. Instead of relying on manual drafting or copy-paste templates, the system uses structured interviews and embedded logic to ensure every QDRO is generated correctly, every time. This dramatically lowers the risk of rejected or non-compliant documents.
4. Is hosting Docassemble on AWS secure for sensitive legal documents?
Yes, when done properly, hosting Docassemble on AWS is highly secure. In the LexyAlgo case, the system was deployed using encrypted storage, secure authentication, controlled access roles, and HTTPS traffic. AWS provides enterprise-grade security, scalability, and reliability, making it well-suited for handling sensitive legal and financial data.
5. Can this QDRO automation approach be reused for other legal documents?
Absolutely. The same Docassemble framework used for QDROs can be adapted for many legal workflows—divorce filings, settlement agreements, estate planning documents, intake questionnaires, and more. Once the interview logic and deployment structure are in place, new document types can be added without rebuilding the system from scratch.
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