Home / Cloud & AI / AI & ML
AI & ML
AI that solves real problems, grounded in your data.
We build the data foundation first, then apply forecasting, personalisation and language models to real workflows — proving value on a small scale before scaling what works.
What we deliver
AI grounded in your own data — not hype
Useful AI starts with clean, connected data. We build the foundation first, then apply forecasting, personalisation and language models to real problems — with clear metrics for whether they actually worked.
Data foundation for AI
Pipelines, warehouses and clean, connected data — the prerequisite most AI projects skip and then fail on.
Predictive analytics & forecasting
Demand, inventory, churn and revenue models that turn your history into decisions you can act on.
Personalisation & recommendations
Product recommendations and tailored experiences that lift conversion and average order value.
LLM & GenAI solutions
Assistants, semantic search and document processing built on LLMs — scoped to real workflows, not demos.
Intelligent automation
Automate manual, rules-heavy work — from data entry to triage — with humans kept in control of the important calls.
MLOps & deployment
Get models into production and keep them healthy with monitoring, retraining and versioning.
Our AI expertise
From process optimization to LLMs and AI agents
CXE has applied AI and machine learning to real business problems for years — not as experiments, but to improve and optimize the processes companies run every day. Today that includes the technologies everyone is talking about: large language models (LLMs), retrieval-augmented generation (RAG) and agentic AI that can actually take action.
Business process optimization
We use ML to streamline and automate real operations — forecasting, planning, routing and decisioning — turning manual, error-prone work into measurable gains.
LLMs & generative AI
Copilots, document intelligence, search and summarisation built on LLMs — grounded in your own data with RAG so answers are accurate and trustworthy.
Agentic AI
AI agents that don't just chat — they take action: orchestrating workflows, calling systems and completing multi-step tasks under human oversight.
Forecasting & prediction
Demand, inventory, churn and risk models that put your own data to work — practical ML that drives better decisions, not a science project.
Build your AI team, faster
Hire CXE AI/ML & Data Engineers
Need senior AI, ML and data talent without the hiring cycle? Bring in CXE engineers on demand — as a dedicated project team, staff augmentation, or white-label capacity under your brand. Experienced, production-focused, and ready to deliver.
Dedicated project teams
A full pod — ML engineers, data engineers and a lead — to own a use case end to end.
Staff augmentation
Plug senior AI/ML and data engineers into your existing team to accelerate delivery.
White-label for SIs
Subcontract capacity delivered under your brand — for agencies and system integrators.
Fractional AI leadership
A senior AI lead part-time — to shape strategy, review architecture and de-risk your roadmap when you're not ready for a full team.
Proven across industries
AI outcomes across industries
We've applied AI and machine learning where it moves the needle — in regulated, data-heavy and operationally complex industries. A few of the places we've delivered.
Healthcare
Document intelligence and process automation for regulated workflows — extracting, classifying and routing clinical and administrative data with accuracy and auditability.
Retail
Demand forecasting, inventory optimization and personalization — predicting what sells where, and tailoring the experience to each shopper.
Construction
Predictive scheduling and cost/risk analytics — spotting delays and overruns early, and turning project data into better decisions.
Wheels & Automotive
Fitment-data intelligence and catalog automation — cleaning, matching and enriching complex parts data so the right product reaches the right vehicle.
Our approach
Start with the problem, not the model
Plenty of AI projects stall because they start from the technology. We start from a use case with a measurable outcome, prove it on a small scale, and only then scale what works.
- Data readiness first — we're honest about whether your data can support the goal yet.
- Prototype before you commit — validate value on a narrow slice before a big build.
- Responsible by default — human oversight, privacy and clear boundaries on what the model decides.
Where clients apply it
Practical starting points that pay for themselves.
How we deliver
From use case to production
A path that avoids expensive science projects.
Identify
Pick a use case with a clear, measurable business outcome.
Prepare data
Assemble and clean the data the model will depend on.
Prototype
Build a focused proof of value on a narrow slice.
Validate
Measure against the metric that matters before scaling.
Deploy & monitor
Ship to production with monitoring and retraining.
FAQ
Common AI & ML questions
We're not sure we're 'ready' for AI. Where do we start?
Do we need our data perfectly organised first?
Can you build LLM / GenAI tools on our own content?
How do you make sure a model keeps working after launch?
Let's scope your ai & ml solutions project.
Tell us what you're running today and where it hurts. We'll come back with a straight, no-jargon plan — build, integrate, migrate or support.
Book a consult