Real-Time Financial Analytics Dashboard
Enterprise-grade FinTech platform delivered in 6 weeks
Project Metrics
6 weeks
Total delivery time from start to production
120 SP
Total effort units delivered in the project
2 senior engineers + AI
Team composition and size
3.5x faster than traditional
Delivery speed compared to traditional development
Overview
Our client, a fast-growing FinTech startup, needed to replace their legacy Excel-based reporting system with a modern, real-time analytics dashboard. The platform needed to handle high transaction volumes, provide instant insights, and scale with their growing customer base.
Client Background
A Series B FinTech company providing financial analytics to institutional investors, managing over $2B in assets under management.
Objectives
- Replace Excel-based reporting with real-time web dashboard
- Process and visualize 100K+ transactions per second
- Provide role-based access for 500+ users across 20+ institutions
- Achieve 99.9% uptime SLA
- Complete migration within 8-week timeline
Challenges & Solutions
1 Real-Time Data Processing at Scale
The system needed to process and visualize 100,000+ financial transactions per second while maintaining sub-second latency for dashboard updates.
Implemented event-driven architecture with Redis pub/sub, WebSocket connections for real-time updates, and optimized PostgreSQL with time-series partitioning. Used TanStack Query for efficient client-side caching.
2 Complex Financial Calculations
Advanced portfolio analytics including risk metrics, correlation matrices, and predictive models required significant computational power.
Built dedicated calculation microservice with Node.js workers, implemented calculation caching strategy, and used materialized views for frequently accessed metrics.
3 Multi-Tenant Security & Compliance
Required SOC 2 compliance, row-level security for 20+ institutions, and comprehensive audit logging for financial regulations.
Implemented JWT-based authentication with role-based access control (RBAC), database row-level security policies, and comprehensive audit logging to PostgreSQL with automatic compliance report generation.
Our Approach
We applied our AI-accelerated development methodology with 2-week development cycles. AI assistants generated boilerplate code, test suites, and documentation while our senior engineers focused on architecture and complex business logic. Daily standups ensured transparency, and the client had real-time access to our progress via the Toolwiz platform.
Technology Stack
Frontend
Backend
Database
Infrastructure
Tools
Key Features
Results
Toolwiz delivered what three other agencies failed to build. The transparency with effort units and daily updates gave us confidence throughout. Our analysts now generate reports in seconds instead of hours.
Sarah Chen
CTO, FinanceFlow Analytics
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