The work.

THE PROBLEM

Client needed to surface institutional knowledge across millions of documents. Existing search was keyword-based, returning noise instead of answers.

THE APPROACH

Originated and deployed a Generative AI solution for semantic search. Built go-to-market roadmaps. Supervised multiple client engagements from architecture through deployment.

THE PROBLEM

Underutilized sensor data across manufacturing equipment. No predictive capability for failures. Aftermarket revenue stagnant.

THE APPROACH

Advanced ML solution measuring KPIs for equipment health diagnosis. Optimized previously underutilized data streams for customer adoption and aftermarket revenue generation.

THE PROBLEM

AWS needed to penetrate consumer IoT markets. No service playbooks existed. No targeted campaign strategies for consumer segment.

THE APPROACH

Spearheaded launch of IoT services for consumer markets. Created service playbooks, solution architectures, and targeted campaign strategies from scratch.

THE PROBLEM

No enterprise voice AI practice existed. Global clients wanted Alexa integration but had no pathway, no skills, no trained developers.

THE APPROACH

Initiated the Amazon Alexa for Enterprise group. Deployed devices and created Alexa skills for global clients. Built training programs. Drove service integrations across verticals.

THE PROBLEM

AWS investment required a compelling demonstration of IoT and AI capabilities that could drive client engagement and partnership funding.

THE APPROACH

Built an interactive IoT and AI reinforcement learning game demo. Designed for executive-level engagement. Demonstrated real-time AI decision-making in physical environment.

THE PROBLEM

Global manufacturer needed real-time predictive cargo ship movements to solve lead generation for engine maintenance. No existing solution.

THE APPROACH

Led a data science team to produce predictive real-time cargo ship movement models. Maritime data, weather patterns, maintenance schedules — integrated into a single predictive system.