Verificator-Augmented Claim-Fraud Triage
Addressing the BPJS Kesehatan Challenge through AI-driven verification workflows.
Our Team
[150-word Team Summary goes here. Describe core competencies and role distribution between the Data Analyst and Tech Lead.]
[Teammate Name]
Data Analyst
[X] years of experience processing BPJS/Kemendikdasmen sector data. Leads feature engineering and policy logic translation.
LinkedInSupported by [Advisor Name], Policy Advisor.
Best Project Execution
Our strongest joint capability is demonstrated by Jonathan's work architecting SiteProtect.ai, a distributed AI Web Vulnerability Scanner. Operating since early 2025, this system automated manual security workflows by deploying a production AI pipeline across 7 global edge nodes. Built on a robust FastAPI, RabbitMQ, and Redis stack, it achieved 100% automated reporting with zero downtime through cutovers. This project proves our ability to successfully replace highly manual, specialized workflows with reliable, distributed AI architectures while keeping humans in the decision loop. This exact pattern—augmenting human expertise with a high-throughput, multi-layer AI backend—is precisely what we are proposing for the BPJS Kesehatan verificator workflow.
SiteProtect.ai (AI Web Vulnerability Scanner)
- Year: 2025 (Early 2025 – Present)
- Stack: FastAPI, RabbitMQ, Redis, Grafana
- Scope: Distributed security engine running across 7 global edge nodes
- Impact: 100% automated reporting, zero downtime through cutover