SaaS Price Comparison Platform
Anonymous Client — Price Comparison & Affiliate Marketing
Overview
A growing SaaS company had outgrown manual product data management, creating performance bottlenecks, stale pricing, and increasing operational overhead as traffic scaled. They required a high-performance price comparison platform capable of processing massive product datasets.
Objectives
Build a high-throughput data ingestion pipeline for automated product updates
Implement lightning-fast search and filtering across large datasets
Eliminate manual data management through intelligent automation
Create a scalable architecture that adapts to fluctuating traffic demands
Establish a robust foundation for affiliate marketing integrations
Our Approach
We applied a performance-first, systems-driven engineering approach combining intelligent automation, scalable architecture, and operational resilience. Rather than layering optimisations onto a fragile monolith, we re-architected the platform around automated data flows, performance isolation, and horizontal scalability — ensuring both immediate gains and long-term flexibility. Every component was designed to reduce operational complexity while maximising throughput, allowing the platform to scale without increasing manual workload.
Engineering Highlights
| Focus Area | What We Delivered |
|---|---|
| Data Architecture | Dual-database system using MongoDB for persistence and Redis for sub-millisecond queries |
| Search Infrastructure | Advanced full-text and faceted search with dynamic filtering and real-time indexing |
| Automation Pipeline | Intelligent ETL system with automated scraping, validation, and enrichment |
| API Design | Headless, RESTful architecture enabling flexible frontend and partner integrations |
| Performance Optimisation | Sub-second query responses achieved through intelligent caching and indexing |
| Infrastructure | Containerised microservices with automated scaling and deployment pipelines |
Outcomes
Query response times improved by 80–90% through optimised data architecture
Product data ingestion fully automated, eliminating manual intervention
Platform scales automatically during peak traffic with zero downtime
Operational overhead reduced by 60–70% through intelligent automation
System now supports 10× more concurrent searches than the previous implementation
Deployment cycles reduced from days to hours through automated delivery pipelines
Business Impact
These improvements enabled the client to scale affiliate traffic without increasing headcount, onboard new data sources rapidly, and operate confidently during high-traffic promotional periods without risking performance or revenue.
Reflection
This project demonstrates how intelligent system design and automation can turn data-heavy platforms into self-optimising business assets. By focusing on automation, performance isolation, and scalable architecture from day one, we delivered a system that works smarter — not harder — supporting both current demand and future growth without increasing complexity.
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