Applied AI and Machine Learning
AI built to run in production. Not to impress in a demo.
We build machine learning models, generative AI systems, and document automation that deploy into real operational workflows and deliver measurable outcomes from the first week in production.
Our approach
How we take AI from idea to production.
01
Frame
Define the problem worth solving
We identify the decisions that are being made manually or poorly and evaluate whether AI can solve them better, faster, or cheaper than the current process. We distinguish between problems that need traditional ML, generative AI, or a combination. Every engagement starts with a clear business case and a definition of what success looks like in measurable terms.
Deliverables
02
Build
Build it for production, not for the pilot
We develop feature engineering pipelines, model architectures, and deployment infrastructure with production requirements in mind from the start. Models are evaluated against business metrics, not benchmark scores. Every model ships with explainability outputs and documentation your team can understand without a data scientist in the room.
Deliverables
03
Operate
Deploy it and keep it working
We deploy using structured pipelines, monitor for performance degradation, and build the governance controls that keep AI systems reliable over months and years. Observability is built in from day one so your team always knows whether the system is performing as expected and what to do when it is not.
Deliverables
What we build
Specific AI systems for specific business problems.
Not generic AI strategy. Not proof of concepts that never make it to production.
Demand Forecasting
Predict demand at product, category, and location level using historical sales, seasonality patterns, and external signals. Procurement, production, and inventory decisions based on where demand is going. Not where it has been.
Outcome
Businesses implementing demand forecasting reduce overstock carrying cost and cut the frequency of stockouts that could have been predicted with better data.
Churn Prediction
Identify the customers or accounts showing early signs of disengagement before they act on it. Give your commercial team a targeted, prioritised list of at-risk relationships with enough lead time to intervene effectively.
Outcome
Companies implementing churn prediction improve retention in the highest-risk segments and reduce revenue lost to preventable attrition.
Document Intelligence
Extract structured data from unstructured documents at scale. Invoices, contracts, shipping documents, quality reports, compliance records. Manual document processing becomes an automated workflow that runs overnight without supervision.
Outcome
Processing time drops sharply in the first quarter. Keying errors near-eliminated. Staff attention redirected from data entry to exception management.
Recommendation Systems
Personalise what you show each customer based on their behaviour, purchase history, and similarity to other customers. Increase basket size, cross-sell rate, and repeat purchase frequency at scale.
Outcome
Businesses implementing recommendation systems see consistent improvement in basket size and a measurable increase in repeat purchase rate within the first 90 days.
Anomaly Detection
Identify fraud, quality defects, and operational anomalies in real time rather than in the next batch report. Before they become customer complaints, regulatory issues, or production shutdowns.
Outcome
Real-time detection reduces loss from undetected fraud and defects compared to monitoring approaches that only look at completed batches.
Route and Operations Optimisation
Apply optimisation algorithms to logistics routing, production scheduling, and resource allocation. Handle the real-world constraints that make these problems too complex for manual planning at scale.
Outcome
Operations teams reduce planning time substantially and improve on-time performance after deployment.
Generative AI and automation
AI that understands your documents and answers from your data.
AI that understands your documents, answers questions from your data, and automates the high-volume operational tasks that should not require a human.
RAG systems and internal knowledge assistants
We build retrieval-augmented generation systems that let your teams ask questions in plain language and get answers from your own documents instantly. Maintenance manuals, carrier contracts, compliance guidelines, operational SOPs. The answer that used to take an hour to find appears in seconds.
Outcome
Teams report dramatic reduction in time spent searching for operational information. Faster decisions with better grounding in actual policy.
Document processing automation
Invoices, shipping documents, purchase orders, quality reports. Processed automatically using AI that reads documents the way a human does, but at scale and without the errors that come from repetition and fatigue. What your accounts or operations team spent two days processing now runs overnight without intervention.
Outcome
Manual document processing time drops sharply in the first quarter. Keying errors near-eliminated. Staff capacity freed for higher-value work.
Conversational analytics
A natural language interface on top of your data environment. Business users ask questions and get answers without opening a dashboard or requesting a report. What was your on-time delivery rate last month versus the same period last year? The answer is instant, from live data, without an analyst in the loop.
Outcome
Decision cycle time drops when every manager can access operational intelligence through a question rather than a report request.
AI Chatbots
Looking for an AI chatbot specifically?
We build customer service bots, internal knowledge assistants, and document automation for logistics, manufacturing, and retail businesses. Scoped and delivered in weeks.
Tools and platforms we work with
Most data engagements start with a conversation about a specific problem.
Tell us what yours is. We will tell you honestly whether we are the right team to solve it.
We respond within 24 hours.