Multi-Service AI Inspection Pipeline: Live on iOS and Android
Overview
Inspectors were spending their evenings turning field notes and photos into client-ready reports. The build looks simple until you count the systems involved: capture in the field, extract meaning from messy input, generate a heavy PDF, notify the user, and give an admin visibility over all of it — with none of them allowed to fail silently, and a user waiting at the end..
The Challenge
Inspectors were spending their evenings turning field notes and photos into client-ready reports. The build looks simple until you count the systems involved: capture in the field, extract meaning from messy input, generate a heavy PDF, notify the user, and give an admin visibility over all of it — with none of them allowed to fail silently, and a user waiting at the end.
Our Solution
We architected and shipped five services. A mobile app handles capture, offline tolerance, billing and push. A Python backend runs a dual-provider LLM pipeline that extracts structured findings from inspection input. A dedicated PDF service handles image compression and report rendering. A background worker polls the job table every two seconds and runs a ten-step pipeline: analysis, mini reports, final report, credit deduction, image compression, PDF generation, storage upload, database update, then push notification. An admin dashboard sits over the top. Error tracking runs on every service.
Results
Live in production on both the iOS App Store and Google Play
Months of uptime on EC2 with no incident
Ten-step pipeline with error tracking on every service
Three independent CI/CD workflows deploying the services separately
Tech Stack
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