AegisFlow

AegisFlow is a flagship enterprise-grade FinTech SaaS platform designed to process high-volume financial data securely. It combines a highly responsive Next.js frontend with a robust, asynchronous FastAPI backend, leveraging PyTorch for advanced financial modeling and fraud detection.

Financial institutions require real-time processing of massive datasets with zero tolerance for data loss or security breaches. Existing monolithic solutions were too slow to adapt to new fraud patterns and suffered from high latency during peak transaction windows.

Financial institutions require real-time processing of massive datasets with zero tolerance for data loss or security breaches. Existing monolithic solutions were too slow to adapt to new fraud patterns and suffered from high latency during peak transaction windows.

• Achieve sub-second latency on the frontend dashboard.
• Process up to 10,000 transactions per second (TPS) on the backend.
• Integrate real-time ML inference for fraud detection.
• Maintain strict PCI-DSS compliance and data security.

High-Level Architecture

A decoupled microservices architecture. The Next.js frontend communicates via REST to the FastAPI backend. Background ML tasks (PyTorch) are handled via asynchronous task queues (Celery/Redis) to prevent blocking the main API threads.

System Components

• Frontend: Next.js (App Router), React, Tailwind CSS, Recharts for predictive trajectories.
• Backend: FastAPI (Python 3.10+).
• ML Engine: PyTorch models served via dedicated inference endpoints.
• Database: PostgreSQL (Primary), Redis (Caching & Task Queues).

High-Level Architecture

A decoupled microservices architecture. The Next.js frontend communicates via REST to the FastAPI backend. Background ML tasks (PyTorch) are handled via asynchronous task queues (Celery/Redis) to prevent blocking the main API threads.

System Components

• Frontend: Next.js (App Router), React, Tailwind CSS, Recharts for predictive trajectories.
• Backend: FastAPI (Python 3.10+).
• ML Engine: PyTorch models served via dedicated inference endpoints.
• Database: PostgreSQL (Primary), Redis (Caching & Task Queues).

• Programming Languages: TypeScript, Python, SQL.
• Frameworks: Next.js, FastAPI.
• Libraries: Pydantic (validation), SQLAlchemy (ORM).
• AI Models: Custom PyTorch models for anomaly detection.

AegisFlow relies on a strict relational architecture hosted on Supabase (PostgreSQL).

• clients: Houses client demographics, risk tiers, and dynamically computed risk profiles.
• invoices: Tracks individual transaction states, due dates, and settlement velocity.
• feedback: Production telemetry loop allowing beta testers to communicate directly with the database.

• Authentication: OAuth2 with JWT tokens.
• Authorization: Granular Role-Based Access Control (RBAC).
• Logging: Structured JSON logging for ELK stack integration.
• Trade-offs: Chose standard REST over GraphQL to minimize query complexity and cache payload responses more efficiently at the CDN edge.

• Containers: Fully Dockerized (docker-compose for local, Kubernetes for prod).
• CI/CD: GitHub Actions for automated testing and deployment.
• Database: PostgreSQL with strict Row-Level Security (RLS) policies.

• Authentication: OAuth2 with JWT tokens.
• Authorization: Granular Role-Based Access Control (RBAC).
• Logging: Structured JSON logging for ELK stack integration.
• Trade-offs: Chose standard REST over GraphQL to minimize query complexity and cache payload responses more efficiently at the CDN edge.

• Challenge: PyTorch inference was blocking the FastAPI event loop under heavy load.
• Solution: Offloaded ML inference to a dedicated Celery worker pool, maintaining sub-100ms API response times.
• The 422 Schema Sync Bug: Next.js payloads were failing validation on the Python backend. Resolved by rigorously typing Next.js JSON payloads against strict Pydantic models.
• Recharts Render Crash: Resolved negative dimension rendering issues by injecting explicit minWidth={1} DOM boundaries to prevent React race conditions.

• Challenge: PyTorch inference was blocking the FastAPI event loop under heavy load.
• Solution: Offloaded ML inference to a dedicated Celery worker pool, maintaining sub-100ms API response times.
• The 422 Schema Sync Bug: Next.js payloads were failing validation on the Python backend. Resolved by rigorously typing Next.js JSON payloads against strict Pydantic models.
• Recharts Render Crash: Resolved negative dimension rendering issues by injecting explicit minWidth={1} DOM boundaries to prevent React race conditions.

• Migration of the production Vercel build to a dedicated .tech domain namespace.
• Expand GAN stress-testing parameters to include granular industry-specific regional shocks.
• Automate K-Means client clustering natively inside the database via Supabase Edge Functions (CRON jobs).

• "How did you prevent the PyTorch inference models from blocking the asynchronous FastAPI event loop in AegisFlow?"
• "What security measures did you implement in AegisFlow to ensure financial data remained secure?"