Four problems. AI-native solutions.

Enterprise discovery sessions generate hours of unstructured input. I built an AI system that synthesizes raw session output into structured, traceable requirements matrices — compressing days of manual work into hours.

Translating requirements into functional and technical specs is where nuance gets lost. I built an AI approach that generates comprehensive specs while preserving domain terminology, delivery standards, and architectural intent.

Incomplete test coverage is the silent killer of implementation timelines. I designed an AI-driven system that produces complete UAT test libraries mapped directly to specs — eliminating the gaps that surface too late.

Go-live cutover is where implementations are most vulnerable. I built an AI system that assembles sequenced cutover plans with ownership assignments and contingency paths — reducing risk at the moment it matters most.