Case study 02 / Inova Platforms / 2023 - 2025
Built a production AI platform across 7 models, 4 data APIs, and 38 cloud functions.
James architected and operated a full-stack AI product across a SwiftUI client, Python microservices, GCP, Firebase, subscriptions, observability, and automated distribution workflows.
Context
DeepChamp AI is a full-stack sports analytics product spanning a native consumer application, AI research and reasoning services, production infrastructure, subscriptions, notifications, and analytics.
Mandate
Own the product and technical system end to end, from the customer experience and AI pipeline to cloud delivery, monetization, reliability, and growth operations.
Product and platform
Built a SwiftUI iOS app and Python microservices. Architected a three-stage AI pipeline spanning Perplexity, GLM-4.5, and Gemini, with seven AI models and four sports-data APIs overall.
Production operations
Deployed 38 cloud functions across GCP and Firebase. Added CI/CD, Crashlytics, Cloud Logging, Firebase services, subscriptions through Superwall and StoreKit 2, and automated distribution workflows.
Result
Turned a multi-model AI concept into a live, instrumented production platform with a native customer surface, cloud services, monetization, and operating controls.
Leadership lesson
AI implementation is a systems discipline. Model quality matters, but so do orchestration, data freshness, reliability, product state, monetization, observability, and ownership.