Audacia

Client 

Northern Trains is a train operating company that provides services across the North of England. With over 500 calling stations, the company connects major cities like Manchester, Leeds and Newcastle. The company plays a crucial role in facilitating transportation and commuting for thousands of passengers every day.

Background 

Northern Trains was facing a high volume of customer service calls related to journey planning. This involved supporting passengers not only during their train journey but also assisting them in reaching their final destination, which could include remote locations where arranging transportation like taxis was necessary. The company, therefore, recognised the need to enhance their customer service offerings by providing a more robust end-to-end transport experience. 

Solution

Audacia partnered with Northern to create an AI WhatsApp chatbot using natural language processing that is capable of handling customer requests related to live train data. Akin to a live departure board found at a train station, the tool is tailored to display relevant train schedules, including any disruptions. The WhatsApp chatbot can, additionally, provide contact details for local taxi operators at a customer's destination station. 

This functionality enables customers to, for example, receive live departure information with a message such as:

“When is the next train from Leeds to Manchester?”

Results

• Live arrival and departure information for over 2500 stations across the UK
• Capable of recognising all CRS station codes
• Trained and tested on 22,000 utterances relating to customer interaction

Background

A leading industrial sealing solutions provider was looking to break down data silos and disparate systems to unlock analytics.

The organisation's vast product data was spread across outdated legacy systems and managed manually in spreadsheets. This presented data silos that stunted cross-functional visibility due to the lack of standards and integration.

Solution

Audacia developed a custom centralised quotation platform to aggregate product data from across the organisation into a single unified view, replacing several legacy systems where data quality issues including duplication were prevalent.

ETL processes were implemented to ingest data from multiple sources into a structured SQL database, with rigorous cleansing and transformation applied throughout migration. Master data management ensured consistent data standards across the organisation, with normalisation and indexing optimised for performance across large, unified datasets.

The database schema was designed to maintain strict separation between product data and order or operational data — including CAD drawing generation and on-site installations — keeping the system organised and easier to maintain at scale. Dedicated schema components handle product combinations and relationships, ensuring clarity and integrity across the data model.

Results

Previously, quotes were issued via a multitude of different routes. Each country might have subtly different processes, and data was stored in an array of locations.  Now that this has been brought together under one centralised system, the manufacturer is now able to compare sales made in Australia to those issued in South Africa, for example, and interrogate this data to identify patterns.

• Consolidated 360-degree view unlocked enhanced analytics across global sites
• Improved data quality and governance
• 72x faster quotation speeds for complex configurations
• Increased visibility from reduced information silos
• Foundations to continue advancing global analytics

Client
Techbuyer is a global leader in the buying, refurbishing and selling of data centre equipment. Founded in 2005, Techbuyer has grown from a company run by just two people, to a global organisation with multiple warehouse facilities located worldwide. Techbuyer have decades of experience in buying used data centre equipment and selling new and refurbished IT parts, and have worked with thousands of private and public sector organisations across 83 countries worldwide.

Background
Techbuyer have hundreds of thousands of products that each have up to eight different naming conventions; with difficulties in building a database that was going to be fast enough to return search results in a short period of time. To avoid lost sales, Techbuyer needed fast search capability with an intelligent search mechanism within a sales-focused database. Techbuyer wanted something easy to use that had 100% up time.

Solution
Audacia developed an intuitive and robust product management system that is integrated with the various platforms that they sell on. The system allows Techbuyer to manage all aspects of the product process, from orders, stock management and returns, enabling them to function more efficiently.

Results 

• 100% uptime with no unplanned maintenance
• Real-time, accurate information on stock levels
• Reliable, sustainable and scalable platform

Client

STERIS is a leading global provider of products and services that support patient care with an emphasis on infection prevention, focused primarily on healthcare, pharmaceutical and medical device customers, with more than 17,000 associates worldwide.

Background

To optimise its operational processes, STERIS sought to implement an application that could accurately predict ideal sterilisation dosage ranges for products prior to processing.

The solution required a machine learning model capable of forecasting outputs based on key parameters like product density and processing speed. Training on historical data would enable the application to learn optimal dosage ranges to maximise efficiency and minimise risk.

Solution

Audacia partnered with STERIS to create a machine learning application that could accurately forecast the optimal efficiency of the sterilisation process of thousands of products per week for the UK and US healthcare systems.

Trained on 15 years of historic data, the solution uses a linear regression model with LGM algorithms to predict the level of sterilisation required for products prior to product loading based on variables such as belt speed, tote density and adjacent tote exposure. The model is continuously refined with automated retraining, ensuring that predictions remain accurate as new data becomes available.

The application was designed for easy integration with the existing tech stack and ERP platform, with Azure Machine Learning offering added flexibility for training models at scale.

Results

• 97%+ accuracy in forecasting optimal dosage ranges
• Sterilisation of 1000 products per week 
• Faster prototyping compared to traditional ML approaches
• Easy integration with .NET systems 
• Flexible training options via local or cloud resources 

Client

ADM Agriculture is a UK subsidiary of ADM, one of the world’s largest agricultural processors and food ingredient providers, with more than 31,000 employees, serving customers in 170+ countries.

Background

ADM Agriculture’s legacy trading platform was becoming a barrier to the business’ growth, with many employees’ jobs starting to involve implementing workarounds to the system to carry out their roles, costing the business time and resources.

ADM sought to find a software development company that could develop a modern commodities trading platform as part of a complex legacy upgrade. It was important that the platform could scale in line with future business growth plans and make time savings for staff.

Solution

Audacia and ADM Agriculture worked together through analysis sessions to understand granular levels of detail of business processes, define scenarios and develop and test prototypes to create a fully future-proof system. This initial analysis discovered how employees were carrying out their work and honed in on what the business wanted to achieve. 

Collaborating with stakeholders and end-users throughout the development process, Audacia used a range of technologies, including SQL Server, Azure DevOps, .NET Core, Typescript, Angular and Cypress, to build a modern commodities trading platform. 

Integrated with ADM Agriculture’s two other core systems, NetSuite and Red Tractor Assurance, the platform streamlines and automates key areas of the business including purchase and sales contracts for products, pricing structures on contracts (added as fixed process or variable price dependent on market value at time of execution), and integrated price alerts to notify traders on price fluctuations.

Results 

• 10.5 million MT of commodity traded per year
• £3.5 billion contracts and services supported annually
• International rollout across 16 countries