Inspired Entertainment built an AI-orchestrated integration pipeline that turns operator API documentation (PDFs/Markdown) into production-ready Java Spring Boot adapter microservices, cutting adapter development from 6-8 weeks to one week and reducing manual engineering from roughly 40 dev days to 5. The stack centers on Google Antigravity CLI (formerly Gemini CLI) to ingest proprietary JSON APIs and emit adapter code, with generated services deployed on GKE, transactional state in Cloud SQL, assets in Cloud Storage, and edge protection via Cloudflare and Cloud Armor. Gemini/Antigravity also sits in the CI/CD pipeline as an automated code reviewer, and was used to modernize legacy systems, upgrading the core platform from Java 8 to Java 25, Tomcat 9 to 10, and Spring 4 to Spring Boot 3.22 in a week. Security posture improved through faster dependency updates and reduced SCA remediation effort.
The measurable outcomes include shrinking team needs per integration from five to two (1 dev, 1 QA), doubling annual integration throughput potential from about 12 to 24+, and a roadmap to cut integration time further to a single day once testing is fully automated. Lessons learned: offload repetitive boilerplate to AI so skilled engineers focus on core problems, and integrate AI into pull-request review as an active, debatable reviewer to catch edge cases. The write-up closes by soliciting community practices for using AI to manage large-scale framework upgrades.
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