Hi, my name is
Ashish Reddy Jaddu
CTO turning legal workflows into AI copilots
Shipping production RAG systems on Azure + Vertex AI • 4 pivots in 18 months
Building production AI systems at LegiSimple/Trails as CTO. I architect and ship RAG-powered legal research platforms, fine-tune LLMs for accuracy improvements, and build full-stack applications from Python/Django backends to React Native mobile apps. Shipped 4 product pivots in 18 months—from OpenAI-powered research tools to multi-channel workflow automation on Azure infrastructure.
Product Velocity
4 pivots
shipped in 18 months
from idea to production MVP
AI Accuracy Gain
82% → 87%
via Vertex AI fine-tuning
for legal document retrieval
Workflow Impact
3hr → 15min
83% time reduction
saved 12 paralegals 3hrs each
Building Innovative Solutions
Startup CTO with 4+ years of experience building LLM-powered legal-tech products at LegiSimple/Trails, from GPT-based research to production RAG systems. I architect secure Azure/GCP backends, fine-tune AI models, and ship MVPs that transform complex workflows into intuitive software law firms actually use.
My journey began in 2020 at HCL Technologies in India, building RESTful APIs and optimizing databases. After earning my Master's in Applied Computer Science from Concordia University (2022-2024), I joined the startup world as Chief Technology Officer.
At LegiSimple, I own the technical roadmap and architecture decisions for an AI-native legal-tech startup. I've led 4 product pivots—from GPT-based legal research to Canadian/US case law search to workflow automation—shipping MVPs at each stage and validating them directly with lawyers.
RAG-Based Legal Research
Built GPT-powered semantic search across Canadian & US case law using embeddings, vector search, and Vertex AI fine-tuning (1K→5K records, 82%→87% accuracy)
Workflow Automation Impact
End-to-end sanction workflow platform cutting manual 3-hour process to 15-30 minutes with mobile apps, AI analysis, and real-time dashboards
API Performance & Security
Optimized Python/Django APIs with caching and query tuning (30% faster), added rate-limiting and Cloudflare protection for sensitive legal data
Azure Architecture
Architected secure Azure backend (App Services, Durable Functions, Azure SQL) with Azure OpenAI + AI Search in private VNet
Cloud Architecture
Design and deploy Azure/GCP infrastructure with VNets, App Services, and serverless functions
AI System Design
Build RAG pipelines, vector databases (Pinecone, Azure AI Search), and LLM orchestration with LangChain
API & Backend
Python/Django REST APIs with caching, rate-limiting, and database optimization for production scale
Security & Data Privacy
Implement VNet isolation, encryption, and compliance controls for sensitive legal data
OpenAI & Azure OpenAI
Production LLM integration, prompt engineering, AI agents
RAG Systems
Vector embeddings, semantic search, retrieval pipelines
Vertex AI & Fine-tuning
Model training, API-based fine-tuning, 82%→87% accuracy
Vector Databases
Pinecone, Azure AI Search, embeddings storage
LangChain & LangGraph
AI workflow orchestration, multi-step agents
Python/Django
REST APIs, ORM, 30% performance optimization
Azure Functions
Durable Functions, serverless workflows
Node.js/Express
RESTful services, real-time SignalR integration
API Optimization
Caching, query tuning, rate-limiting
Azure
App Services, VNet, Azure SQL, AI Search, private endpoints
Google Cloud
Compute Engine, Cloud Run, Vertex AI
Docker & Kubernetes
Containerization, orchestration, production deployments
Redis & Caching
Query caching, session management, performance
Next.js/React
SSR, Zustand state management, component architecture
TypeScript
Type-safe applications, advanced patterns
React Native
iOS & Android apps published on App Store & Play Store
Real-time UI
Azure SignalR, live dashboard updates
Where I've Made Impact
Architected and shipped 4 complete product pivots in 18 months (GPT-based legal research → Canadian case law → US case law → workflow automation), building and deploying full-stack MVPs at each stage
Built RAG-based legal research platform: Python NLP for document parsing, OpenAI embeddings for semantic search, Vertex AI fine-tuning (1K→5K training records boosted accuracy 82%→87%), automated retraining pipelines
Developed end-to-end multi-channel workflow automation: React Native mobile apps (iOS/Android live in stores), Python/Django backend with Azure Durable Functions, real-time SignalR dashboards—reduced 3hr manual process to 15min
Designed and deployed Azure infrastructure: App Services, Durable Functions orchestration, Azure SQL databases, Azure OpenAI + AI Search in private VNet for law-firm data security
Optimized production APIs: implemented Redis caching, query optimization, connection pooling (30% faster responses), memory reduction (25%), rate-limiting, and Cloudflare DDoS protection
Built Next.js/React frontends with TypeScript, Zustand state management, Azure SignalR real-time updates, and analytics instrumentation (Mixpanel, PostHog) to identify and fix UX friction points
Developed and deployed RESTful APIs powering internal React applications, optimized MySQL queries improving page-load by ~20% and reducing DB response times by ~10%
Debugged complex production issues across frontend and backend services, created database-backed defect catalog boosting QA effectiveness by ~40%
Streamlined deployments for 5 web apps on Heroku, improving configuration and resource usage to cut hosting costs by ~15%
Projects That Make a Difference
Here are some of the projects I've worked on that showcase my skills and passion for creating impactful solutions.
● Challenge
Law firm partner faced 3-hour manual sanction process requiring field visits, document review, and coordination across multiple stakeholders—creating bottlenecks and compliance risks.
● Solution
Built end-to-end workflow platform with React Native mobile apps for field capture, Azure Durable Functions orchestrating AI analysis via Azure OpenAI, and Next.js dashboard with SignalR for real-time updates and audit trails.
✓ Result
Saved 12 paralegals 3 hours each per case—reduced process time from 3 hours to 15-30 minutes (83% faster), eliminated manual handoffs, and logged all actions for compliance audits.
● Challenge
Lawyers needed semantic search across 1,000+ Canadian & US case law documents, but keyword matching was insufficient and manual review cost hours per query.
● Solution
Architected RAG pipeline with NLP document parsing, OpenAI embeddings stored in Pinecone/Azure AI Search, and Vertex AI fine-tuning loop fed by lawyer feedback—scaling to 5,000 records with automated retraining.
✓ Result
Enabled instant case law retrieval for 50+ lawyers—improved accuracy from 82% to 87% through fine-tuning, enabled natural language queries, and cut research time from hours to seconds via semantic search.
● Challenge
Field workers needed to capture complaints and deliver documents on-site, but web-only access limited mobility and created data-entry delays.
● Solution
Built cross-platform React Native apps (published on App Store & Play Store) with offline-first architecture, Azure real-time sync, and push notifications for status updates—integrated with backend workflow APIs.
✓ Result
Empowered 30+ field workers with mobile-first workflows—reduced data entry lag through offline capture, provided multi-channel access to legal documents, and enabled real-time case updates in the field.
Let's Work Together
I'm always interested in new opportunities and exciting projects. Whether you have a question or just want to say hi, I'll try my best to get back to you!
ashishjaddu@gmail.com
Connect with me