PROJECT TITLE
ScaleWorks - AI Legal Agent
My Role
I was responsible for designing, developing, and deploying the entire AI-powered legal assistant platform for Scaleworks.
My role involved integrating multiple specialized AI models including a legal Q&A assistant, e-discovery engine, document automation pipeline, and real-time transcription service.
I built the frontend experience, architected the backend infrastructure, designed the database schema, and implemented secure file handling and scalable AI model orchestration. I also ensured seamless client-side performance, intuitive UX, and production-grade deployment.
What is ScaleWorks AI
Scaleworks is a custom AI-powered legal agent platform designed to help legal professionals, firms, and institutions automate critical workflows.
It integrates multiple domain-specific AI models to handle tasks such as legal document analysis, contract data extraction, e-discovery from uploaded files, document transformation into structured data, and real-time transcription of courtroom audio.
The platform aims to reduce manual workload, improve speed and accuracy in legal research, and unlock new efficiencies in document-driven legal operations.
1. The Problem
Legal professionals are overwhelmed by manual document review, research complexity, and repetitive data entry tasks. <br/><br/> Most legal tech tools lack the intelligence, scalability, or integrations needed to automate knowledge extraction and streamline workflows in a secure and usable way.
a. User Challenges: Lawyers and legal teams spend excessive time searching through documents, manually extracting data, and transcribing hearings—resulting in inefficiencies, missed insights, and burnout.
b. Business Challenges: The need to develop a reliable, domain-specific AI solution that respects legal data sensitivity, supports varied formats (PDFs, audio), and integrates seamlessly into legal operations—while maintaining high performance and compliance.
In-House Legal Team
Julia, a corporate legal counsel, needs to quickly extract insights from contracts and legal documents without relying entirely on manual paralegal review.
Litigation Specialist
David, a litigation attorney, uses Scaleworks to transcribe hearings in real-time and search through large sets of discovery documents more efficiently.
Frontend
Built with React and Tailwind for a clean, structured legal UI and responsive layout.
Backend
Node.js with Express for managing endpoints, file parsing, AI model coordination, and authentication.
Database
PostgreSQL and Supabase for storing structured document data, user sessions, and query logs.
AI Models
Integrated OpenAI models for Q&A and document processing, and Whisper for transcription.
OCR & Document Parsing
Used Google Cloud Vision API to extract text from both native and image-based PDFs, ensuring accurate and high-fidelity data capture from legal scans.
Document Processing
Used PDF.js and docx-parser to extract clean text from uploaded legal documents.
Deployment
Deployed on Render and Vercel with secure environment variables, route protection, and file system constraints.
a. Legal Assistant Integration
- Built a prompt-engineered OpenAI-based assistant tailored for legal language and citation referencing.
- Created a query refinement mechanism to improve accuracy of legal answers.
b. E-Discovery Document QA
- Developed a pipeline that accepts PDF, DOCX, and TXT files for context-specific Q&A from document contents.
- Embedded documents and used retrieval-augmented generation (RAG) for precise answer extraction.
c. Document Automation & Structuring
- Integrated Google Cloud Vision API to extract structured text from scanned PDFs, including image-based legal and financial documents.
- Built a post-processing layer to clean and categorize extracted data into labeled financial fields.
- Transformed the final parsed data into Excel-compatible tables downloadable by the end user.
d. Real-Time Audio Transcription
- Integrated a Whisper-based transcription model to handle live or uploaded courtroom audio.
- Enabled speaker tagging and timestamped transcriptions formatted for legal context.
- Used streaming generation for fast, progressive response rendering in legal Q&A.
- Implemented file chunking and memory-efficient document parsing to support large legal files.
- Secured file uploads and limited file retention in accordance with data sensitivity best practices.
- Optimized model calls with caching and fallback layers for increased uptime and failover handling.
- Used asynchronous batch processing in Google Vision to handle large PDF files and optimize processing time for document-heavy users.
