Nexius AI: Building an AI Financial Document Processing SaaS
A CV-backed look at the 7-stage Nexius AI platform: OCR, parsing, extraction, validation, journal mapping, reporting, and delivery.
Product Scope
At Quantum Teknologi Nusantara (September 2025 – May 2026), I worked on Nexius AI, an AI-powered financial document processing SaaS. The platform converts uploaded financial documents into structured, validated outputs that users can review and download.
The product spans customer-facing upload flows, partner and affiliate portals, and internal admin dashboards, so engineering decisions need to support both product usability and operational visibility.
Backend Architecture
I standardized FastAPI service architecture using Domain-Driven Design so services remain maintainable as AI integrations and document workflows grow.
Long-running file-processing flows were moved from monolithic processing into RabbitMQ-based distributed workers and Kubernetes worker pods to improve scalability under high upload volume.
- REST APIs for uploads, queue visibility, report/month processing state, and background jobs.
- Server-Sent Events for real-time upload and processing progress.
- Worker heartbeat, queue diagnostics, and progress snapshots for operational debugging.
AI Quality and Observability
The AI workflow includes transaction categorization, ambiguous transaction clustering, Chart of Accounts mapping, and metric-based validation.
I also improved observability with OpenTelemetry, SigNoz, structured logs, and queue diagnostics so production issues are easier to trace.