High-Load CRM Performance Optimisation & Scaling
Improving performance and reliability of a high-usage CRM handling large datasets and daily operations.
LIVE SIGNAL · API / BACKEND · V2.4 · PROD
0103
In plain English
I helped turn a messy operational workflow into a clearer system, making it easier for the team to manage data, reduce manual work, and support daily business operations.
Business value
- Less manual work
- Clearer data flow
- Better operational visibility
- Easier maintenance
- Role
- Full Stack Owner / Sole Engineer
- Design · Build · Ship
- Timeframe
- 2024 – Present
- Domain
- Real Estate / Internal Systems
- Category
- API / Backend
Context
The CRM was being actively used across multiple teams, handling large volumes of records including leads, listings, and operational data.
As usage increased, performance issues started affecting daily workflows. Pages became slower to load, filters were less responsive, and backend processes began to take longer than expected.
The Problem
The challenge was improving performance without disrupting existing workflows or introducing instability.
Constraints included: - Large and growing datasets - Existing production dependencies - Multiple modules interacting with the same data - Need to maintain backwards compatibility
Changes had to be incremental, safe, and measurable.
System design
I approached the problem by identifying bottlenecks across the full stack and optimising both backend and frontend layers.
Key improvements included: - Query optimisation and indexing strategies in MySQL - Reducing unnecessary data loading and improving pagination - Refactoring API responses to return only required data - Introducing caching for frequently accessed resources - Improving frontend rendering and state handling in Angular
The focus was on reducing load times while keeping the system stable and predictable.
feedback · calibration loop
Frontend
Backend
Database
Infrastructure
Also used
Outcome
- Reduced response times
- Improved system stability
- Better user experience
The system became noticeably faster and more stable under regular usage.
- Faster page load times for high-volume lists - Improved responsiveness in filters and actions - Reduced server load during peak usage - Better overall user experience for daily operations
These improvements allowed the system to handle growth without degrading performance.
Reflection
I would introduce more structured performance monitoring earlier, including detailed metrics and alerting for slow queries and API latency.
Additionally, I would invest sooner in load testing to better simulate real-world usage at scale before issues appear in production.
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