Data Quality & AI Assurance

Founded on over 26 years’ experience in quality assurance and verification, KJR has always understood the importance of managing IT risk and especially do so now with the steady maturation of various emerging technologies.

We recognise the potential of Artificial Intelligence and Machine Learning and see the specific challenges these technologies pose to traditional forms of quality assurance. We have harnessed our expertise to develop practical approaches to assuring compliant and successful deployments of AI solutions.

Innovative technologies call for innovative solutions

New technologies are changing the way we work and live. The speed of development for most of these are rapid, their practical applications not yet fully realised. We leverage our 25+ years of expertise in risk management and quality assurance to direct innovative solutions for data applications into artificial intelligence and machine learning technologies securely and safely.

Deploy your AI with Confidence !

Common to both artificial intelligence and machine learning is the analysis of vast amounts of data and their ability to build a bigger, richer picture – where data really has value. How this data is managed from an assurance and governance perspective is where KJR comes to the fore. When you are talking data management talk to KJR.

KJR Industry Partner SmartAIConnect is developing a Responsible Al Framework for the interoperability of AI data, governance, ethics and quality assurance of video camera edge AI model management at scale. This framework revolutionises the deployment of Al models at the edge, simplifying the entire process, from deployment to monitoring and distribution of predictions, while ensuring security, audit trails, and governance measures.

KJR’s key partnership with SmartAIConnect provides a fusion of systems for AI model applications.

SmartAIConnect’s AI Framework provide a validated process for managing risks associated with deploying machine learning models on the edge at scale, including support for risk assessment tracking, bias checking, managed deployments, production monitoring and audit trail support.


For broader AI risk, governance and compliance processes, KJR’s Validation Driven Machine Learning (VDML) methodology can guide your organisation in the selection of effective risk assessment, quality assurance and governance mechanisms for a specific operational setting.

Developed by KJR, VDML delivers solutions which can be integrated and governed within a real-world context. VDML is used to structure how to de-risk and understand models. It provides customers the confidence to develop robust and reliable Machine Learning models to deliver AI solutions they can trust.

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Data Quality Assurance

  • Data migration/assurance
  • Data quality assurance
  • Data governance
  • DataOps
  • Data analytics

Artificial Intelligence / Machine Learning Assurance

  • Validation driven machine learning
  • Data augmentation/data generation
  • Secure data labelling
  • Explainability, bias, ethics & compliance
  • Machine learning delivery pipelines

Case Studies

Dashboard security for data assurance, UI web development
The smart steering wheel is a potential solution to monitor, predict and prevent fatigue for truck drivers who travel extremely long routes.
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Data Quality & AI Assurance
Datarwe is a data platform start-up that focuses on the ways in which AI and machine learning (ai-ml) can be applied to data and used to solve real-world problems. Currently, Datarwe’s focus is on long-term data collection from individuals in Intensive Care Units (ICU).
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Natural Language Processing (NLP)
The Australian Government’s lead science and technology agency for defence
KJR was engaged to design and implement a solution for classifying large volumes of scientific documentation to improve document visibility and availability for wider use. Utilising the AWS technology stack KJR achieved this through the development of a Natural language Processing classification model, that classifies text topics based on the All-Science Journal Classification System (ASJC).
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