CASE STUDY
AI-Powered Medical Transcription
Generate AI-powered testimony transcriptions with unmatched accuracy and speed, tailored for Social Security disability hearings.LLM Evaluation
Speaker Identification
Legal AI
The Challenge
Social Security disability hearings generate hours of audio testimony featuring medical terminology, legal jargon, and multiple speakers. Traditional transcription services struggle with accuracy—especially on medical terms and condition names critical to case outcomes.Disability lawyers need transcripts that are fast, accurate, and speaker-identified. Generic AI transcription gets medical terms wrong. Human transcription takes days and costs hundreds per hearing. The market needed something better.Building production-grade AI transcription required more than just calling OpenAI's Whisper API. It required custom evaluation frameworks, domain-specific accuracy measurement, and LLM agents that could handle the messiness of real hearing audio.Our Approach
We partnered with attorney Nick Coleman to build Lexmed AI from the ground up—combining his domain expertise in SSD law with our technical chops in production AI systems.The breakthrough wasn't just better transcription. It was building custom evaluation frameworks to measure accuracy on legal and medical terminology, LLM agents that handle real hearing audio, and speaker identification that actually works in multi-party disability hearings.Custom Evaluation Framework
Built domain-specific evaluation metrics to measure accuracy on medical terminology (conditions, medications, symptoms) and legal jargon. We don't just measure word error rate—we measure what matters for case outcomes.Speaker Diarization
Implemented speaker identification that distinguishes between judge, attorney, claimant, and medical expert—critical for legal review. Handles overlapping speech, background noise, and phone testimony.LLM Agent Pipeline
Designed a multi-stage LLM pipeline: transcription → medical term correction → speaker identification → formatting. Each stage uses different models optimized for different tasks, with confidence scoring and human review flagging.Production-Grade Infrastructure
Built robust file handling, progress tracking, error recovery, and cost optimization for processing hours of audio at scale. Engineered for reliability—not just demos.Technical Implementation
Building production AI isn't about finding the right model. It's about building the evaluation, monitoring, and reliability systems around the model.Domain-Specific Accuracy Metrics
Created custom evaluation datasets with ground-truth transcripts featuring medical conditions, medications, and legal terminology specific to SSD cases. Measured model accuracy on critical terms—not just overall word error rate.Multi-Model LLM Pipeline
Combined Whisper for initial transcription, GPT-4 for medical term correction, custom speaker diarization models, and Claude for final formatting. Each model chosen for its specific strengths, with fallback strategies when confidence is low.Audio Processing & Optimization
Built audio chunking strategies to handle multi-hour hearings, noise reduction pipelines for phone testimony, and quality detection to flag problematic audio segments for human review.Monitoring & Reliability
Implemented comprehensive logging, error tracking, cost monitoring per transcript, and quality metrics dashboards. Built systems to detect model drift, accuracy degradation, and API failures before they impact customers.Impact & Results
Lexmed AI launched in 2024 and quickly became the go-to transcription solution for disability lawyers who need accuracy on medical terminology and legal precision.10x FasterHours not days—transcripts ready same day
95%+ AccuracyOn medical terminology and legal jargon
Production AIBuilt for reliability—not just demos
"I had the domain expertise. Clint's team had the AI chops. When we started, I knew the SSD legal space was ready for AI—I just didn't have a technical team who understood what it takes to build production-grade AI products. His team dove into the hard problems: custom evaluation frameworks to measure accuracy on legal and medical terminology, LLM agents that could handle the messiness of real hearing audio, speaker identification that actually worked. They didn't just build what I asked for—they helped me understand what I should be asking for."Nick ColemanLawyer and Founder, Lexmed AI
Technology Stack
OpenAI Whisper
GPT-4
Claude
Speaker Diarization
Python
FastAPI
Next.js
PostgreSQL
Redis
AWS S3
FFmpeg
Custom Evaluation Framework