Case Study
AI-Assisted Contract Generation
An AI-assisted legal workflow that transforms structured client intake information into jurisdiction-appropriate draft contracts for attorney review.
Outcome
Created a repeatable drafting process that reduces repetitive first-draft work while preserving attorney control over legal judgment and final content.
- Project Type
- Legal AI and workflow automation
- My Role
- AI workflow design, prompt engineering, system integration, testing, and product thinking
- Users
- Attorneys and legal staff
- Status
- Active development / internal implementation
- Confidentiality
- Some details have been generalized
System at a Glance
- 1Client Intake
- 2Structured Data
- 3Validation and Normalization
- 4Prompt and Template Assembly
- 5LLM Draft Generation
- 6Attorney Review
- 7Revision and Approval
- 8Final Contract
Overview
AI-Assisted Contract Generation is a legal AI workflow for converting structured client intake information into jurisdiction-appropriate draft contracts for attorney review.
The system is designed as a drafting assistant, not a replacement for legal professionals. Attorneys remain responsible for review, validation, legal judgment, approval, and final edits.
Business Problem
Attorneys often begin contract drafting using information collected during client intake. Re-entering information, organizing clauses, and producing a consistent first draft can require significant manual effort.
The opportunity was to create a structured AI-assisted workflow that could generate a reliable starting draft while preserving attorney oversight.
Goals
- Reduce repetitive manual drafting
- Improve consistency across contract drafts
- Convert intake data into structured legal documents
- Keep attorneys involved at critical decision points
- Create a workflow that is reviewable, traceable, and appropriate for legal work
Solution
Structured intake information is processed, normalized, and passed into a controlled AI generation workflow.
The system creates a first draft based on client information, matter type, jurisdiction, required clauses, approved language or templates, and a defined output structure.
The generated document is then sent to an attorney for review, revision, and approval.
Human-in-the-Loop Design
The workflow intentionally keeps legal professionals involved. AI generates a draft, not a final legal document.
- Attorneys validate legal accuracy and jurisdictional fit
- High-impact decisions remain human-controlled
- Outputs can be revised before approval
- The workflow prioritizes speed without sacrificing oversight
My Contributions
- Translated the legal drafting process into a structured AI workflow
- Designed prompts and output structures
- Connected intake data to document generation
- Developed validation and review steps
- Considered incomplete inputs, failure cases, and inconsistent outputs
- Helped make the system understandable and usable for non-technical stakeholders
Challenges and Design Decisions
- Balanced speed with accuracy
- Prevented incomplete intake data from producing unreliable drafts
- Maintained output consistency
- Kept the system flexible across contract types
- Designed appropriate attorney review checkpoints
- Avoided over-automation in a high-responsibility environment
Technologies and Capabilities
Outcome
- Reduced the work required to create an initial draft
- Created a repeatable and structured drafting process
- Improved consistency in how intake information is incorporated
- Preserved attorney control over final legal content
- Demonstrated how generative AI can support practical legal operations