Data Science & Machine Learning
Partnering with enterprise and public sector organisations to apply data science and machine learning to complex analytical and predictive challenges.
Providing data science and machine learning expertise to turn complex data into insight and decisions that deliver measurable value.
We deliver data science and machine learning solutions for some of the UK's largest and most complex organisations, combining consultancy expertise with hands-on model development. From building predictive models for a global medical device manufacturer, to developing anomaly detection systems for a nationwide energy provider, our work spans industries, use cases and analytical techniques.
We work with organisations to identify where data science and machine learning will have the greatest impact, assess data maturity and develop models that are interpretable, validated and built around how your organisation operates.
We deliver data science and machine learning engagements from initial problem framing through to model development, validation and handover, working closely with your data and subject matter expert teams throughout.
We begin every engagement with a structured discovery phase to understand the problem in its operational context. We assess data availability, quality and suitability, identify gaps and define what a successful output looks like before any modelling begins. Working closely with your subject matter experts, we ensure the analytical approach reflects how your organisation makes decisions and what outputs need to enable.
Data preparation and feature engineering follow, addressing quality, consistency and the specific requirements of the modelling techniques under consideration. We work with your teams to resolve data gaps through augmentation or alternative sources where possible, and are clear about what gaps mean for model performance where they cannot be resolved.
Model development is iterative, with your teams involved throughout. We evaluate and test candidate approaches against defined performance criteria and validate outputs against held-out data across conditions that reflect real operational variability. Interpretability is a core requirement across all engagements, particularly in public sector and regulated environments where model-informed decisions need to be explainable and auditable.
Outputs are documented clearly and designed to be actionable, whether that means structured recommendations, explainable model outputs or analysis your teams can build on directly. Where validated models are ready to progress to production deployment, we support a smooth transition to the next stage of delivery.
Commodity contracts and services supported for one of the world's largest agricultural organisations
Funding allocation managed each year for the nation’s largest funder of health and care research
Pupils tracked across 12,000 UK wide schools
Annual sales supported through a knowledge management platform for a global manufacturer
Applying data science and machine learning to complex analytical and predictive challenges
From forecasting outcomes and surfacing insight, to developing optimisation models that determine the best course of action across constrained problems.
Building machine learning models that forecast outcomes, predict demand, identify risk and support forward-looking decisions, trained on your data and validated against your operational context.
Applying data science techniques to surface patterns, segment audiences, detect anomalies and uncover the relationships within your data that are difficult to identify through conventional analysis.
Developing models that determine the best course of action across complex, constrained problems, from resource allocation and scheduling to personalised recommendations and dynamic pricing.
Using industry standard tools and technologies
From PyTorch and TensorFlow, to Hugging Face and MLflow, we use the latest, industry-standard tools and platforms to develop, validate and deploy data science and machine learning solutions.
Delivering data science and machine learning solutions for organisations across industries
From predictive modelling for global manufacturers, to anomaly detection, forecasting and optimisation for healthcare organisations.
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.
A UK-based large food manufacturer, established for over 100 years, providing products as part of a healthy, balanced diet, through a range of products to suit all meal occasions, lifestyles and tastes.
A nationwide energy provider who specialises in supplying energy to a wide range of businesses with a UK-based team, from SMEs through to large national chains, knowing what energy challenges businesses face and how to support them.
A no-code work management platform that enables anyone to replace spreadsheets with custom applications to track and manage work. From marketing professionals, to sales teams, HR managers and agencies of all kinds, empowering people across all industries to innovate by developing the software that they need.
They had a great culture and pragmatic approach, challenging us to think about the data strategy rather than fixing just a short term problem.
- Leading Appliance and Electronics Retailer
Our latest insights in AI and machine learning
Insights on the latest industry developments, technology advancements and practical applications of AI across enterprise and public sector organisations.
Most enterprise AI projects stall not because the model fails, but because the data underneath is incomplete or inaccessible. This article sets out the five dimensions of AI data readiness, examines how data debt compounds across initiatives, and explores the architectural patterns that allow organisations to scale AI.
More than 80% of AI projects fail which is twice the rate of non-AI IT projects. This article explores the five reported consistent root causes of this failure: starting with technology instead of a business problem, weak executive sponsorship, poor data readiness, no path to production, and treating AI like a traditional IT project.
This article explores the role of AI governance in scaling AI beyond pilots and into production. It examines the cost of ungoverned AI, the evolving regulatory landscape including the EU AI Act and the UK's principles-based approach, and outlines three practical governance principles - proportionate, embedded and automated - that enable organisations to scale AI with confidence.
The way that we work is that we are subject matter experts, we know our business, we know our customers, we can then have that conversation with the team at Audacia. It is very much a collaborative two way process and the level of communication is just fantastic.
- AESSEAL plc
Talk To Us
As a first step in the process, we offer a free consultation around your current setup. We'll discuss your challenges and goals and see whether we could be a good fit for delivery.
