Case Studies
Recent work across automation, machine learning, and intelligent systems. Each project focused on practical business outcomes.
Automated Data Processing for Acoustic Consultancy
Problem
Noise and acoustic consultancies collect large volumes of measurement data during investigations. Processing this data and producing formal reports requires repetitive manual steps—importing data, filtering signals, performing calculations, and formatting results—which is time-consuming and prone to error.
Solution
Developed a Python-based automation system that imports and processes acoustic measurement datasets, performs required calculations and transformations, and generates formatted report sections referencing relevant regulatory standards.
Outcomes
- Reduced manual data processing effort
- Improved consistency of calculations and reporting
- Enabled faster turnaround of consultancy reports
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Estimating and MIS System for Bespoke Manufacturing
Problem
A bespoke packaging manufacturer relied on manual estimating processes and fragmented management information, making quoting and decision-making slow and inconsistent.
Solution
Designed and scoped a smarter estimating / MIS system to centralise operational variables, automate parts of the quoting process, and support more reliable management insight.
Outcomes
- Reduces estimating friction
- Improves consistency in quoting
- Lays the foundation for stronger operational reporting
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Customer Clustering and Recommendation Modelling
Problem
Businesses often hold customer data but lack clear ways to identify meaningful groups, prioritise opportunities, or understand which customers respond to different products.
Solution
Developed clustering and recommendation approaches to uncover behavioural patterns in customer data and support smarter targeting and prioritisation.
Outcomes
- Helps businesses understand customer segments
- Improves targeting and prioritisation
- Supports more informed commercial decision-making
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AI Lesson Planner for Science Cover Teaching
Problem
Teachers and cover staff often lose time preparing lessons at different difficulty levels and adapting content quickly.
Solution
Developed a Python-based lesson planning system that integrates educational best practice, syllabus content, challenge tasks, and knowledge-check questions to generate structured lesson plans efficiently.
Outcomes
- Reduces lesson preparation time
- Improves consistency of lesson structure
- Supports differentiated delivery across ability levels
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