Building AI That Scales, Systems That Ship

Challenge: AI agents were confidently generating incorrect responses—citing non-existent policies, fabricating product details, and damaging customer trust.

Solution: Built a dual-layer RAG architecture combining vector search with real-time characteristic validation. Reduced hallucinations from 18-25% (industry baseline) to under 3%, transforming unreliable AI into production-grade customer support.

Challenge: LLM fine-tuning pipelines were failing at scale—ambiguous test cases caused models to produce correct logic that failed arbitrary checks, wasting compute and training iterations.

Solution: Redesigned the evaluation framework with intent-based labeling and comprehensive PyTest suites. Clarified success criteria through structured JSON schemas, boosting model pass rates and accelerating training cycles across the team.

Challenge: Designers were building the same buttons, modals, and forms from scratch for every project—no shared design system meant inconsistent UX and duplicated work across teams.

Solution: Created a centralized component library in Storybook with Redux Toolkit integration. Gave teams pre-built, accessible UI primitives that cut frontend development time by 40% while enforcing design consistency.

Challenge: Legacy monolithic APIs created deployment bottlenecks—every new feature required coordinating releases across teams, slowing innovation to a crawl.

Solution: Architected a micro-frontend system using Module Federation and decoupled async processing with RabbitMQ. Enabled independent team deployments, eliminating cross-team dependencies and accelerating feature velocity.

Challenge: AI agents were confidently generating incorrect responses—citing non-existent policies, fabricating product details, and damaging customer trust.

Solution: Built a dual-layer RAG architecture combining vector search with real-time characteristic validation. Reduced hallucinations from 18-25% (industry baseline) to under 3%, transforming unreliable AI into production-grade customer support.

Challenge: LLM fine-tuning pipelines were failing at scale—ambiguous test cases caused models to produce correct logic that failed arbitrary checks, wasting compute and training iterations.

Solution: Redesigned the evaluation framework with intent-based labeling and comprehensive PyTest suites. Clarified success criteria through structured JSON schemas, boosting model pass rates and accelerating training cycles across the team.

Challenge: Designers were building the same buttons, modals, and forms from scratch for every project—no shared design system meant inconsistent UX and duplicated work across teams.

Solution: Created a centralized component library in Storybook with Redux Toolkit integration. Gave teams pre-built, accessible UI primitives that cut frontend development time by 40% while enforcing design consistency.

Challenge: Legacy monolithic APIs created deployment bottlenecks—every new feature required coordinating releases across teams, slowing innovation to a crawl.

Solution: Architected a micro-frontend system using Module Federation and decoupled async processing with RabbitMQ. Enabled independent team deployments, eliminating cross-team dependencies and accelerating feature velocity.