Project Overview
Cubet partnered with a forward-thinking SaaS startup that was on a mission to deliver intelligent business services across industries. With ambitious goals and a fast-moving roadmap, the client needed a platform that was as agile as it was resilient. The challenge was to design a secure, scalable, and analytics-driven SaaS platform that could onboard new clients rapidly while isolating tenant data and allowing full customisation. The solution needed to support real-time data ingestion, analytics, and visualisation—without compromising on performance or future extensibility.
Industry
Software-as-a-Service (SaaS) / Business Intelligence / Platform Engineering
The Client
A SaaS startup building an intelligent service platform for multi-sector business users, with a focus on performance, scalability, and real-time analytics. Their mission was to provide each client with isolated, insight-rich experiences via a unified product infrastructure.
Challenges Addressed
The client faced the classic high-growth SaaS dilemma: how to move fast while maintaining a rock-solid foundation. Specific pain points included:
- Building a multi-tenant system with strong data isolation
- Delivering real-time data analysis and visualisation per tenant
- Supporting future modules, user roles, and industry-specific logic
- Keeping onboarding and provisioning lightweight and fast
- Ensuring enterprise-grade security, scalability, and observability
A modular, analytics-ready, and extensible architecture was key to enabling sustainable growth across clients and sectors.
Collaboration in Action
Cubet engineered a robust, Python-powered cloud-native SaaS platform designed for velocity and intelligence. The backend combined FastAPI and Django REST to power API-first development and efficient background processing, while PostgreSQL with schema-based multi-tenancy provided clean tenant data separation.
Data pipelines were built to handle both real-time and batch analytics, feeding a custom visual reporting engine for actionable dashboards. Admins could manage tenant roles, plans, and permissions through a centralised portal, while each client accessed their own isolated analytics-rich environment—all monitored, scaled, and secured for enterprise use.
Technologies Deployed
- Backend:
- Python (FastAPI, Django REST)
- Celery for background processing
- PostgreSQL with multi-schema tenancy
- Redis for caching and performance boosts
- Frontend:
- React.js with tenant-specific theming and branding
- Admin and client portals for data access and control
- Analytics Layer:
- Pandas, NumPy for data crunching
- Matplotlib, Plotly for rich visualisation
- Custom reporting engine integrated per user role
- Deployment & Monitoring:
- Dockerized microservices deployed via AWS ECS (Fargate)
- S3 for object storage
- Prometheus and Grafana for system monitoring
- Slack and Sentry for alerting and error logging
- Security & Authentication:
- OAuth2 + JWT for secure access
- Role-Based Access Control (RBAC) for granular permissions
Innovative Feature
The core innovation was a data-first, multi-tenant engine that allowed for rapid onboarding and instant analytics per client—without duplicating infrastructure. Schema-based and row-level data isolation ensured strong boundaries between tenants, while the analytics pipeline generated real-time insights without taxing backend performance.
Add to this a modular design that supported quick rollouts of new features, and the result was a powerful foundation for any intelligent SaaS product looking to scale fast and securely.
Value Delivered
- Under 5 minutes to onboard a new tenant with full data isolation
- Enabled real-time analytics for 20+ clients within the first 3 months
- Achieved 99.9% uptime with auto-scaling and health monitoring
- Backend latency reduced by over 60% through optimised Python processing and caching
- A flexible architecture ready to support new modules and verticals without refactoring
User Feedback
Early client onboarding was smooth, with users praising the system’s speed and the clarity of dashboards. Internally, the engineering team saw faster feature rollouts and easier debugging through modular code and rich observability tools. The business team highlighted the quick time-to-market and stability as critical enablers for early client wins.
Conclusion
This project showcases how Python-based backend systems can offer both development agility and long-term stability. By focusing on multi-tenancy, real-time insights, and modular design, Cubet delivered a SaaS engine that’s built to grow with the business—without rework, reboots, or regressions.
Impact Made
The platform enabled the client to confidently scale across industries, serve clients with isolated environments, and deliver real-time business intelligence—without compromising speed or integrity. What started as a vision is now a living product powering smart decisions across multiple verticals, built on a rock-solid Python foundation.