
Noxcon, a UK-based ERP testing consultancy in the Healthcare sector, relied on manual testing processes that required extensive human effort and delivered inconsistent accuracy. By implementing an AI-powered automated testing platform with Computer Vision, Noxcon reduced execution time from 1–2 hours to 15–30 minutes, improved accuracy to 99.5%, and achieved scalable, repeatable QA operations across NHS ERP environments.
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150
faster workflow testing
99.5
%
testing accuracy achieved
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Computer Vision
ChatGPT
Tesseract OCR
Qdrant
Django Admin
THE CHALLENGE
Noxcon faced challenges with automating their large-scale platform and NHS healthcare systems testing. They depended on manual processes to create and execute test scripts for verifying system functionality. Each workflow required 1-2 hours of human review, yielding only 70-75% accuracy. As testing volumes increased, the scalability, speed, and consistency of the processes deteriorated, and human errors became more frequent. Noxcon needed a solution to modernize this manual testing framework by implementing AI-driven automation to improve precision, efficiency, and reliability across complex ERP workflows.
The recognition of workflows in testing was slow and lacked the precision needed for complex systems, requiring substantial human input to identify steps and variations in the workflows.
Some testing workflows were embedded in PDF documents containing complex block diagrams with overlapping or misaligned arrows. Traditional parsing tools struggled to interpret these visual elements accurately, requiring extensive manual corrections.
Manual test scripts frequently failed to align with complex ERP workflows, resulting in incomplete or inconsistent test coverage. Aligning hundreds of workflow variations with corresponding scripts demanded intensive human effort and frequent revisions.
Test scripts were created manually by humans, leading to slower generation times and a high risk of errors. Automating script generation was a key need, with human supervision required for quality control.
THE SOLUTION
To overcome these challenges, we developed an AI-powered Automated Test Management Platform, integrated with Computer Vision, to replace Noxcon’s manual testing processes. The platform automatically generates and validates test scripts based on workflows, reducing testing time from 1–2 hours to just 15–30 minutes and improving accuracy to 99.5%. Although the scripts are generated by AI, they are executed under human supervision to ensure quality.
We implemented ChatGPT to align workflows with test scripts and fill in any missing test steps. This allowed the system to identify workflow patterns, ensuring completeness and accuracy in script generation.
AI analyzed workflows to detect missing test steps. Using ChatGPT, the system generated suggestions for these missing steps based on the workflow data. Over 5,000 scripts and workflows were used to train the model, achieving an accuracy rate of 99.5%.
We used Computer Vision to analyze and extract workflows directly from PDF documents. This enabled the system to accurately interpret complex visual elements and convert them into structured test scripts, ensuring full coverage of all workflows.
AI now automatically generates test scripts based on workflows and system behavior, ensuring consistency and speed. Human review remains in place as a quality safeguard, allowing Noxcon to retain control while significantly accelerating execution.
THE RESULT
Implementing the AI-powered automated testing platform dramatically improved operational efficiency across NHS ERP testing workflows. Manual review time was reduced, execution became consistent and repeatable, and accuracy increased to near-perfect levels. The system now enables faster delivery cycles, higher release confidence, and scalable test coverage without increasing QA resources. Human oversight ensures governance while automation drives speed, precision, and reliability.
The solution also unified all testing workflows into a single system, enabling real-time monitoring and scalability for future integrations.
According to the client, the key advantage was that automation not only replaced repetitive manual tasks but also delivered significantly higher quality — allowing the company to take on more projects and scale operations efficiently.
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