Solving your business challenges and empowering your people through AI and Machine Learning

What We Deliver

As well as offering some great, easy-to-implement AI products, we have the ability to provide a semi-bespoke service to solve other business problems that our AI products may not at first glance cover.

We do this by taking our existing tools and models and repurposing them to solve your business problem. There are many different sub-branches of AI, and we don’t offer to work in all of them. We focus on areas where you can use our core competencies, existing tools and products. This ensures we can deliver high-quality solutions which deliver exceptional results at a cost which reflects the reuse of existing tools.

Transformative AI Solutions

Our core skills are in Artificial intelligence and Machine learning, including reinforcement learning and unsupervised machine learning. The specific areas we focus on are:


Our forecasting models fully automate the generation of predictions based on multiple data sources. These can be output via API to a BI tool or other system that relies on the required forecast.
Use Cases

Predicting sales:

We can use AI to help you forecast sales per item and department by whatever time granularity is required. Forecasts can be in real-time for operational reasons or in advance to assist planning.


Demand forecasting:

We can convert the sales forecast into a forecast of the staffing levels required to support that level of sales, including across multi-role teams.

Forecast stock levels:

AI-powered real-time sales forecasting by item can generate an accurate stock level forecast, smart alerts and ordering recommendations.

Optimisation and Resource Allocation

Our resource recommendation model optimises the allocation of both fixed and variable resources by using historical data, rules, targets and client preferences to assign a given resource most efficiently. This will ensure your business maximises the return from that resource.
Use Cases

Revenue optimisation:

AI can recommend how many newspapers should be sent to each store each day to maximise sales and reduce the return of unsold newspapers

Staff Optimisation:

We can use AI to generate the optimum deployment model for locum pharmacists within a group of pharmacies to meet regulatory requirements and generate the maximum revenue for a given level of resource.

Investment optimisation:

Use AI to decide how to spend a fixed budget when it comes to purchasing stock to maximise the amount of revenue.

Predictive Modelling

Our Predictive modelling engine uses your data and third-party inputs to predict future outcomes and give a probability of each outcome occurring. These individual predictions can then be aggregated to provide expected outcomes for the whole group.
Use Cases

Churn prediction:

Our Retention.Ai tool predicts a percentage probability that each employee will leave in 30, 60 and 90 days, along with insights into the highest correlating factors for each person. It then aggregates these probabilities to give a whole organisation view.


Customer life-time value:

AI can estimate how much each customer is worth and then calculate the expected value of a cohort of customers generated by an individual marketing campaign. This can be used to estimate the ROI from marketing campaigns or categorise VIPs quickly.


Predictive inventory and stock management:

When combined with an item-by-item sales forecast and a stock control system, AI can give real-time alerts around ordering, food prep and restocking to optimise revenue.

Robotic process automation

We can use AI robots or “bots” to automate your digital processes, and you can save time and costs whilst improving accuracy. This can also be used to ensure increased data digitisation in support of an AI strategy, converting unstructured data and archived documents into valuable data.

Use Cases

Price monitoring:

RPA bots can continually observe competitor prices on e-commerce retail sites, enabling swift price adjustments in real time.


Data validation:

Bots can be used to cross-check data against publicly available or private data to ensure consistent naming conventions and accurate data sets.

Report preparation and distribution:

Bots can produce reports automatically, examine their contents, and then send emails to applicable stakeholders depending on what they find.

Data Engineering

We have built many tools to enable the collection and usage of data. This data can then be used to support the delivery of the other solutions we offer or in-house and third-party applications.
Use Cases

Data cleaning and sorting:

Removing duplicates and irrelevant data, standardising formatting and correcting errors, pre-calculations, and categorisation are just some of the tasks required to get the vast amounts of data in the format required to support AI and Machine Learning.


Auto ML:

These automated processes enable the implementation of machine learning in real-world scenarios. This encompasses all stages, starting from raw data to the deployment of an ML model.

Process mining:

These data science and process management techniques analyse event logs to gain insights and take action on operational processes. The aim is to transform event data into useful information and decisions.

Solve Your Biggest Operational and HR Challenges Using Proven AI Solutions that Deliver ROI

You don't need development skills or data science engineers to implement our solutions, they are quick and easy to integrate with your existing tech stack.