Risk APIs, metrics dashboards, automated PDF reporting and a unified search layer across services in Spring Boot 3 / Kotlin, NestJS, FastAPI and Flask, deployed on Kubernetes.
10+
Microservices
Polyglot
JVM + Node + Python
Puppeteer
PDF generation
Kubernetes
Helm + ArgoCD-style delivery
An enterprise risk and analytics firm managed large volumes of dispersed data with no way to generate automated reports, compute risk metrics or visualize consolidated insights in real time. We architected a polyglot platform of 10+ microservices on JVM (Spring Boot 3 + Kotlin + Java 21), NestJS 10, FastAPI and Flask, with Vue UIs and Puppeteer-based reporting, deployed on Kubernetes with mature CI delivery.
Client
Enterprise risk and analytics firm
Industry
Business Intelligence & Analytics
Scope
10+ microservices, dashboards, PDF reports, search, insights, K8s delivery
Stack focus
Spring Boot 3, Kotlin, Java 21, NestJS 10, FastAPI, Flask, Vue, Puppeteer
Data was everywhere; insights were nowhere. Manual reports were a bottleneck.
Information was scattered across multiple sources with no unified view.
Generating reports required hours of manual aggregation.
Risk metrics were not computed in real time.
We delivered a domain-decoupled platform with each capability owned by a focused service in the right runtime for the job.
JVM service on Java 21 that owns risk insights and narrative delivery.
Puppeteer + pdf-lib + Handlebars pipeline for automatic report generation.
Python services for metrics, ingestion and aggregation against AWS-style storage.
Dedicated services for risk computation and unified search across data sources.
Vue 2 dashboards with a custom d3-based charting library tailored to the product's data shape.
Helm charts and reusable workflows with ArgoCD-style preview environments per service.
Decisions are now data-driven, with a scalable architecture that grows without redesign.
Auto
PDF reports generated
Real-time
Risk dashboard live
Unified
Search across data sources
Scalable
Architecture grows without redesign