Built a multi-step reasoning engine that breaks down complex legal research questions, executes them across document collections, and synthesizes coherent answers - handles questions that would take paralegals hours or days

Built document understanding that works across formats lawyers use - PDFs, Office docs, emails, images, audio, video

Built multi-provider LLM abstraction with intelligent caching that cut API costs by 85%+

Built Graph RAG that understands relationships between people, organizations, and events - finds connections keyword search misses

Built context management that handles arbitrarily large document sets - compresses intelligently and maintains accuracy even when source material far exceeds context windows

Built citation and provenance tracking - every AI answer links back to exact source documents with page numbers

Zero customer complaints on answer accuracy

Built proprietary canonical entity resolution that disambiguates and links entities across millions of documents - connects aliases, name variations, and references scattered across thousands of pages into a unified identity graph

Designed and built the entire AWS infrastructure in Terraform - one command spins up the full stack from scratch

Built video/audio processing with GPU-optimized clusters - handles conversion, storage optimization, and browser-playable formats at scale

Built a custom ECS autoscaler that watches queue depth and scales workers based on queue pressure

Set up all monitoring, CI/CD, and service-to-service communication

Performed 95% of all SOC II and ISO 27001 compliance work - policies, controls, audit prep

Analyzed and neutralized spend across MongoDB Atlas, AWS, and LLM API calls, saved $100,000+ in wasted infra costs