Automated Price Prediction for a Large Retail Catalogue
Weekly automated price recommendations across 30,000+ products in 130+ locations, including products with little or no recent purchase history in that store.
AI and Machine Learning
Most useful AI and machine learning work starts with a business problem, not a technology. The problem might be a process that needs automating, a pattern that is too complex for manual analysis, or a decision that needs to be made faster or more consistently.
We help you identify which problems are the right fit, choose the right approach, and deploy models that improve how the business actually operates.
Overview
Common ways AI and machine learning are used
Most AI and machine learning projects fall into one of the broad areas below. Each covers a set of specific capabilities listed further down the page.
Automating decisions at scale
Where the same decision needs to be made over and over, machine learning turns historical data into a deployed predictive system that runs faster, more consistently, and at a scale no manual team can match. The work is not just producing a number. It is building something reliable enough to act on every time.
Some examples
- Demand and revenue forecasting
- Customer churn and retention prediction
- Real-time risk scoring and fraud detection
Recognising patterns in complex data
Some signals are too complex or noisy for hand-crafted rules and basic statistics. Deep learning and modern classification techniques handle the cases where simpler approaches fall short.
Some examples
- Anomaly detection in transactions and operational data
- Classification across complex, multi-feature inputs
- Computer vision and image recognition
Custom AI built and run in production
Custom model development, deployment infrastructure, and the engineering needed to run AI reliably in production. Whether starting from scratch or moving existing prototype work into production, this is where many AI projects fall over, and where end-to-end delivery experience matters most.
Some examples
- Custom AI/ML model development
- MLOps and model deployment
- Performance evaluation and optimisation
Our services
What this looks like in practice
Below are the specific capabilities and use cases that sit within those broad areas. Some span more than one. The list is not exhaustive. If your needs are different or more specific, just get in touch.
Working with us
How we work with you
Most AI and ML work fits one of three modes. Scope and deliverables vary; the examples sketch what each typically involves.
Many engagements use more than one of these.
If it is not clear which of these fits, book a free intro call and we can work it out.
Is this for you?
Who this is for
This is a good fit for organisations that have data and are ready to do something meaningful with it. You do not need a dedicated data science team internally, but you do need access to relevant data and a real business problem you want to solve.
We work across financial services, healthcare, retail, manufacturing and professional services. The relevant factor is always the problem rather than the sector. If you have structured data and a decision you currently make on instinct or incomplete information, predictive modelling is worth exploring.
We also work with businesses that already have AI/ML systems in production and need independent assessment, performance improvements, or extensions to existing models.
When something else fits better
AI and machine learning, data science, and NLP and generative AI all overlap, and many engagements draw on more than one. The starting point on the site usually maps to one of the following:
- Mostly text, documents, or generative AI: NLP & Generative AI
- Analytical insight from existing data without building a deployed system: Data Science & Analytics
- Sizing up where AI fits at all, without a specific use case yet: Getting Started with AI
In practice
See it in practice
A selection of engagements that involved this type of work.
FAQ
Common questions
Do I need clean, well-organised data before starting?
We assess data readiness as part of any engagement. Some projects need substantial clean historical data; others can start with messier data and improve it iteratively. We will be clear about what is needed before any build begins.
What is the difference between AI and machine learning consulting and data science consulting?
They overlap. AI and machine learning consulting focuses on building and deploying predictive systems and automating decisions in production. Data science covers statistical analysis, insight discovery, and experimentation. Many projects draw on both. A conversation will make clear which applies.
Do I need a large in-house technical team to benefit?
No. Many clients come to us precisely because they do not have an in-house ML capability. We can work alongside an existing technical team or directly with business stakeholders. The goal is always to leave your team able to operate and iterate on what we build.
Will you always recommend the most complex approach, such as deep learning?
No. We start with the business problem, not the most sophisticated technique. A well-tuned regression or gradient boosting model often outperforms a neural network on structured data. We use the simplest approach that reliably solves the problem.
Can you work with data that lives in our existing systems?
Yes. We build solutions that integrate with your existing infrastructure, databases, and workflows. Where data access or pipeline work is needed upfront, we flag that early.
Let's Talk
Need a production ML system?
Let’s talk about the problem, the data and what it would take to put a model into use.