AI Prototypes and PoCs
Validating AI concepts through research, prototyping and experimentation to de-risk development and drive informed decision making.
Providing the evidence organisations need to make informed, de-risked decisions on AI investment, through rapid prototyping and structured proof of concept delivery.
From image classification prototypes to identify stock for a leading supermarket product retailer, to proof of concepts for predictive maintenance for a global industrial manufacturer, we provide end-to-end AI research and prototyping services to help organisations validate ideas and make informed decisions on AI investments.
With extensive research expertise across industries, we evaluate use cases, data readiness and requirements to determine where AI can solve problems, assessing performance, value and feasibility throughout.
We deliver full lifecycle AI prototyping services, from research and analysis to prototype building, user testing and reporting.
We begin every engagement by gaining a thorough understanding of your needs and objectives. Our analysts evaluate use cases, data quality and project feasibility to determine where AI aligns to both short and long term goals. Working closely with stakeholders, we rapidly build prototypes to validate assumptions, refine requirements and identify any gaps or challenges.
We leverage leading AI cloud services including Azure Machine Learning and AWS Bedrock to accelerate prototyping across use cases. With pre-built models, APIs and infrastructure, AI cloud services enable rapid prototyping to validate concepts within days to weeks, for use cases such as sentiment analysis, image detection and anomaly detection. For organisations with specific infrastructure requirements, we work with on-premise and hybrid environments to deliver the same pace and quality of prototyping within your existing setup.
With user feedback and benchmarking, we deliver actionable reports outlining benefits, limitations, lessons learned and recommendations on next steps, enabling organisations to make informed, de-risked decisions on investing in AI and machine learning.
We rapidly build prototypes across a variety of AI domains and capability areas that solve high-value business problems, including computer vision models for image recognition and video analytics, natural language processing for chatbots and text analytics, predictive modelling for forecasting and recommendations, as well as planning and scheduling optimisation and anomaly detection algorithms.
By prototyping across capability areas, we provide the coverage needed to validate AI applicability for the diverse challenges and opportunities across your organisation.
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
Rapidly validating AI concepts across a range of domains and capability areas
From prototyping language and conversation use cases, to validating vision and detection models and exploring prediction and optimisation opportunities.
Validating natural language processing, conversational AI and text-based use cases, from chatbots and virtual assistants to document analysis and sentiment detection.
Testing computer vision, image recognition and video analytics use cases, as well as anomaly detection models for quality control, safety monitoring and operational inspection.
Exploring forecasting, recommendation and planning optimisation use cases, validating whether predictive models can deliver the accuracy and reliability needed to justify full development.
Using industry standard tools and technologies
From Microsoft Agent Framework and Azure AI Search, to OpenAI and Claude, we use the latest, industry-standard tools and platforms to rapidly prototype and validate AI concepts.
Delivering AI research & prototyping projects to organisations across industries
From manufacturing and healthcare, to rail, retail and energy.
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.
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.
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 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.
From start to finish the working relationship between Audacia’s team and ours was productive from the iterative development approach, meaning we worked in shorter time frames but increased levels of communication to ensure all updates were reviewed quicker. Audacia’s end platform delivered on all aspects.
- The National Institute of Health Research (NIHR)
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 examines why traditional software testing falls short for LLM-powered systems and what organisations need to do differently. It covers the scale of the hallucination problem, evaluation approaches for RAG and agentic AI systems, the emerging regulatory requirements around AI testing, and how engineering leaders can build the evaluation capability needed to deploy AI responsibly.
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.
