The Challenge
An enterprise client needed two distinct but complementary AI capabilities:
- Computer vision for real-world object tracking and classification — algorithms that could be deployed in field equipment to categorise objects in real-time
- Data clustering to find hidden patterns in large datasets — revealing customer segments and insights that traditional analysis methods couldn’t detect
Both required production-grade reliability, not research prototypes. The vision system needed to work in variable real-world conditions, and the clustering solutions needed to deliver actionable business insights, not just interesting charts.
Our Approach
Computer Vision System
We architected and built production computer vision applications using a combination of techniques:
- Object detection and classification: Algorithms that identify and categorise objects in image and video streams, trained on domain-specific data
- Object tracking: Maintaining identity of objects across frames for continuous monitoring applications
- Field deployment: Algorithms designed to run on equipment in real-world conditions — handling variable lighting, weather, angles, and image quality
- Commercial application: The system saw deployment in roadside equipment gathering data continuously, as well as automated image processing workflows
The architecture was designed to be adaptable — the same core vision pipeline could be fitted to different use cases by retraining the classification layer for new object categories.
Data Clustering & Pattern Recognition
For the data clustering work, we applied AI and machine learning techniques to discover structure in complex datasets:
- Advanced statistical methods: Moving beyond simple correlation analysis to find non-obvious relationships in the data
- Customer segmentation: Analysis across multiple data sources revealed distinct customer patterns that weren’t detectable with traditional methods. In one engagement across six consumer goods websites, we identified three distinct customer clusters with differentiated behaviour patterns
- KNN solutions: Implemented K-nearest neighbours algorithms for classification and recommendation problems where the decision boundaries were complex and non-linear
- Actionable output: Every clustering analysis was tied to specific business actions — segmented marketing strategies, product recommendations, or operational optimisations
The Outcome
The computer vision systems were deployed in production field applications, processing real-world image data for object tracking and classification continuously. The data clustering work uncovered customer segments that led to differentiated marketing strategies — resulting in measurably increased engagement for the majority of identified segments.
Both capabilities demonstrated that AI/ML could deliver concrete business value beyond the hype — practical systems solving real problems in production environments.
Technologies Used
- Computer Vision: Deep learning, object detection, classification, tracking
- Data Science: KNN, clustering algorithms, PCA, statistical analysis
- Languages: Python
- Frameworks: Custom ML pipelines, image processing libraries
- Deployment: Field-deployable systems, automated processing workflows