Technical insights, stories and opinions from our team of consultants, analysts, developers and testers.

Managing Tech Debt within AI and Machine Learning Systems
Managing Tech Debt within AI and Machine Learning Systems
Adam Brookes - 27/11/2024
For AI and machine learning systems, technical debt extends beyond code to include complex dependencies in data, models, and operational workflows. In this article, we explore how technical debt differs from traditional software engineering, and actionable strategies with MLOps that can help to manage tech debt effectively.
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Thoughts from a Tester: Tips and Tricks to Avoid Development Team Horror Stories
Thoughts from a Tester: Tips and Tricks to Avoid Development Team Horror Stories
Emily O'Connor - 19/11/2024
From our latest Halloween-themed Tech Talk, Principal Test Engineer, Emily, shares tips and tricks to avoid development team horror stories, sharing best practices testers can implement to ensure improved quality, effective communication and continuous improvement.
The Role of Generative AI in Legacy System Modernisation
The Role of Generative AI in Legacy System Modernisation
Richard Brown - 24/10/2024
Generative AI is a technology that can help in simplifying legacy modernisation, offering solutions that go beyond what traditional techniques can achieve. This article explores how generative AI specifically enhances various stages of legacy modernisation, from automated code generation and documentation to testing and data migration.
Building User-Centric Products: You are Not Your Customer
Building User-Centric Products: You are Not Your Customer
Emily O'Connor - 16/10/2024
Emily focuses on a common pitfall among development teams - assuming they know their users because they understand the product. Emily highlights how development teams should step outside of their perspective and embrace user-centric design to improve product quality.
YAML in Azure Pipelines: An Overview
YAML in Azure Pipelines: An Overview
Rhys Smith - 15/10/2024
An overview of YAML and its application within Azure Pipelines, from the fundamentals of YAML to the nuances of building efficient pipelines, through to highlighting how YAML streamlines CI/CD processes in modern DevOps practices.
Testing an AI WhatsApp Chatbot
Testing an AI WhatsApp Chatbot
Emily O'Connor - 29/05/2024
Audacia partnered with Northern Trains to create an AI WhatsApp chatbot for live journey information. Being a public-facing AI WhatsApp chatbot, testing was a critical part of delivery. Here, we take you through how we executed automated testing on the project.
Top 5 most read tech articles by IT leaders in 2023
Top 5 most read tech articles by IT leaders in 2023
Audacia - 04/12/2023
As we come to the end of 2023, we round up the most popular technical articles read by IT directors, CIOs and other tech leaders on our technology insights blog, with popular topics including Azure OpenAI Service, the latest developments in Vue, ML.NET for machine learning, and AI tools being applied within software development teams.
Front-End Frameworks: Pros & Cons for Accessibility
Front-End Frameworks: Pros & Cons for Accessibility
Tom Shephard - 31/10/2023
While front-end frameworks like React, Angular, and Vue accelerate web development, they can also introduce accessibility pitfalls. This article explores three popular frameworks' accessibility pros and cons, along with testing best practices.
Implementing temporal table auditing in software development projects
Implementing temporal table auditing in software development projects
Owen Lacey - 23/08/2023
Audacia have successfully implemented Temporal tables in one of our software development projects, Olympus. Here we look at this SQL server feature in more detail and discuss Audacia's journey in replacing our existing auditing solution with temporal table auditing.
Team Stories: Rachel Flynn, Senior Software Engineer
Team Stories: Rachel Flynn, Senior Software Engineer
Audacia - 14/08/2023
Senior Software Engineer, Rachel Flynn, discusses her experience in software development and her recent work on a machine learning project.