British Geological Survey: AI-enabled multi-utility monitoring across a complex research estate

• Sector: Scientific Research and Public Sector
• Client: British Geological Survey
• Challenge: Improve access to fragmented energy data and establish a monitoring platform capable of expanding across a complex scientific research estate.
• Solution: Recovered available data, introduced Optimal Monitoring data loggers and consolidated monitored supplies within EMMA AI. The platform is being expanded through a chiller project covering ten additional meter points across electricity, heat and water flow.
• Impact: Established a central, scalable approach to multi-utility monitoring, with new data incorporated into BGS’s site-wide software agreement. Optimal Monitoring will work closely with BGS to use EMMA AI to identify savings opportunities, implement practical improvements and monitor their impact.

• Sector: Retail (Greeting Cards & Gifts)

• Client: Card Factory  – Over 1,100 stores across the UK and Ireland

• Challenge: Reduce energy costs across a large retail estate, support sustainability goals, and gather actionable data for ESOS compliance.

• Solution: Deployed EMMA AI to monitor energy usage across 193 stores during a 3-month pilot, providing real-time insights without the need for additional hardware or IT integration.

• Impact: Achieved return on investment in under three months; rapid scalability led to rollout across 900+ stores, enhancing energy efficiency and supporting long-term sustainability planning.

Card Factory is the UK’s leading specialist retailer of greeting cards, gifts and celebration essentials, operating more than 1,000 stores across the UK and Ireland. Since opening its first store in 1997, the business has grown into a highly recognisable retail brand, serving customers through a large network of high street, retail park and shopping centre locations.

Like many large retailers, Card Factory faces the challenge of managing energy consumption across a widespread and diverse estate. Each store has its own operating profile, trading hours, equipment, occupancy patterns and local conditions. This can make it difficult to identify where energy is being used efficiently and where avoidable waste may be occurring.

With energy costs continuing to place pressure on operational budgets, Card Factory wanted a practical, scalable way to gain better visibility of electricity consumption across its estate. The business also needed reliable data to support sustainability planning, energy reduction initiatives and ESOS compliance.

To support these goals, Optimal Monitoring‘s Emma AI was deployed across an an initial group of 193 stores as part of a three-month pilot. The aim was to demonstrate how automated energy monitoring could quickly identify savings opportunities using existing meter data, without the need for additional hardware, complex IT integration or disruption to store operations.

Managing energy across a large retail estate presents several challenges. While individual stores may appear relatively small in isolation, even modest levels of avoidable consumption can become significant when multiplied across hundreds of locations.

For Card Factory, the key challenge was not simply collecting energy data, but turning that data into useful, actionable insight. The business needed to understand which stores were performing well, which locations may have been using energy unnecessarily, and where operational changes could deliver measurable savings.

The project needed to support several objectives:

• Reduce avoidable energy costs across a large retail estate.
• Identify stores with unusual or excessive electricity consumption.
• Provide actionable insight that could be used by operational and energy teams.
• Support sustainability commitments with evidence-based reporting.
• Gather accurate data to assist with ESOS compliance.
• Inform future energy-saving investment decisions.
• Deliver value quickly, without requiring major capital expenditure.

Speed and simplicity were particularly important. Card Factory required a solution that could be deployed quickly across multiple stores, using existing infrastructure wherever possible. Any approach also needed to be secure, scalable and suitable for a national retail operation.

Emma AI was deployed across 193 Card Factory stores during an initial three-month pilot. The platform used data from existing meters to monitor daily electricity consumption and automatically identify potential energy-saving opportunities.

Rather than relying on manual analysis or periodic reporting, Emma AI continuously reviewed consumption patterns across the pilot stores. This allowed Card Factory to see where energy use appeared higher than expected, where stores may have been consuming electricity outside normal operating hours, and where further investigation or action could be prioritised.

A key advantage of the solution was its low-disruption deployment model. Emma AI did not require additional on-site hardware or major IT integration, helping to keep the pilot simple and secure. This made it possible to move quickly from data collection to insight, giving Card Factory a clear view of savings opportunities within the pilot period.

The platform helped convert raw consumption data into meaningful outputs for decision-makers. Instead of simply showing how much energy each store used, Emma AI highlighted where energy could potentially be reduced and where operational focus would have the greatest impact

The three-month pilot was designed to test EMMA AI across a meaningful sample of the Card Factory estate. By monitoring 193 stores, the project provided enough coverage to identify patterns, compare store performance and understand the scale of potential opportunity.

During the pilot, EMMA AI analysed electricity consumption across the participating stores and highlighted areas where usage could be reduced. This gave Card Factory clearer visibility of how stores were operating from an energy perspective and helped identify where energy waste may have been occurring.

The pilot also demonstrated how automated monitoring could support wider business objectives. The insight generated was not only useful for short-term cost reduction, but also for longer-term sustainability planning and compliance reporting.

Because the solution used existing meter data, the pilot avoided the complexity often associated with large-scale energy projects. There was no requirement for extensive surveys, disruptive installation work or lengthy technical integration before value could be demonstrated.

The pilot achieved return on investment in under three months, demonstrating the commercial value of automated energy monitoring across a large retail estate.

This rapid payback gave Card Factory confidence that EMMA AI could deliver measurable benefits at scale. Following the success of the initial pilot, the solution was rolled out across 900 stores, significantly increasing the level of visibility across the estate.

The wider deployment enabled Card Factory to move from a pilot-based view of energy performance to a broader estate-wide approach. This allowed the business to identify savings opportunities across many more locations and to build a stronger foundation for ongoing energy management.

Key benefits included:

Fast return on investment
The pilot delivered ROI in under three months, showing that energy savings could be identified quickly and translated into commercial value.

Scalable deployment
The project moved from an initial 193-store pilot to a rollout across 900 stores, demonstrating that EMMA AI could be scaled across a large retail estate.

No additional hardware required
By using data from existing meters, the solution avoided unnecessary capital expenditure and reduced deployment complexity.

No major IT integration
The rollout was kept simple and secure, avoiding the need for complex integration with internal IT systems.

Improved visibility of energy performance
Card Factory gained clearer insight into how electricity was being used across stores, helping to identify locations where consumption could potentially be reduced.

Support for ESOS compliance
Automated data collection and reporting helped provide clearer evidence of energy use, reduction opportunities and progress against energy management objectives.

Better long-term planning
The insight generated by EMMA AI helped inform future sustainability and energy efficiency planning across the estate.

Card Factory is now using Emma AI across its wider estate to build on the success of the initial pilot. The rollout across 900 stores provides a stronger foundation for ongoing energy monitoring, improved efficiency and reduced operational costs.

By continuing to use Emma AI, Card Factory can maintain visibility of energy performance, identify further savings opportunities and support long-term sustainability planning with accurate, evidence-based insight.

The project demonstrates how automated energy management can help large retailers move quickly from data to action, delivering measurable savings while supporting wider environmental and compliance objectives.

DS Smith – Making Corrugation More Cost-Efficient

Sector: Manufacturing (Packaging)

Client: DS Smith – Corrugated packaging plant in Featherstone

Challenge: Improve visibility of energy consumption across a complex manufacturing site, identify avoidable waste and provide the operational team with reliable, real-time data across multiple utilities and production processes.

Solution: Optimal Monitoring deployed a flexible energy monitoring system combining existing site equipment with additional hardware. The platform captures and analyses electricity, gas, water, temperature and humidity data, enabling the team to identify unusual consumption patterns and investigate specific areas of energy waste.

Impact: Within the first six weeks, analysis identified that the corrugator heaters were remaining on standby for around six hours each day, costing approximately £15 per hour. Introducing a low-fire setting reduced the hourly standby cost to around £8, delivering a 47% reduction and estimated annual savings of £15,300. This initial opportunity allowed the investment to pay for itself through the first investigation alone, while providing a scalable platform for identifying further savings across the site.

The University of Sussex had set ambitious environmental goals aligned with EU and UK policy. By 2020, they aimed to:

• Cut greenhouse gas emissions by 20% (from 1990 levels)

• Source 20% of energy from renewables

• Improve energy efficiency by 20%

To reach these targets, they needed clear visibility of how energy and water were used across a large and diverse estate. They also needed a strategy that would deliver value within tight annual budgets.

Optimal Monitoring worked with the University to design a metering strategy that provided both a campus-wide overview and granular insights at the building and service level.

The rollout was split into manageable, value-driven mini-projects. This phased approach:

• Delivered early wins

• Allowed performance verification

• Supported smarter investment decisions

• Maximised impact within annual budgets

Real-time dashboards and data visualisation made the results clear and compelling—not just for facilities teams, but for staff and students too.

Sector: Retail (Betting)

Client: Betfred – 1,400+ high street locations

Challenge: High total energy cost across small, dispersed sites

Solution: EMMA AI® platform for daily insights and shop-level actions

Impact: £234k saved annually, with expected savings rising to £381k

With over 1,400 retail betting shops, each with relatively low energy use but a significant combined footprint, Betfred needed a smarter way to cut costs and carbon. Traditional approaches to energy management weren’t cost-effective at this scale.

The goal: reduce energy spend, support Net Zero ambitions, and show measurable ROI, without adding headcount or investing in new infrastructure.

Optimal Monitoring deployed EMMA AI® to turn Betfred’s existing energy data into real-time, actionable insight. The AI platform analysed data daily, identifying abnormal usage and creating instant work tickets for shop teams.

This decentralised, exception-led model empowered staff across the estate to take immediate action; such as switching off equipment overnight or addressing usage in low-traffic areas, without extra workload or training.

To demonstrate impact, only the East region initially received EMMA AI recommendations. It outperformed all others, validating the technology’s effectiveness.

Optimal Monitoring used an independent AMR and environmental monitoring platform as a more cost-effective alternative to BMS repair. The system was configured to automate complex analysis, combining flow, temperature and consumption data daily. This enabled the hospital team to:

• Track performance in real time

• React quickly to anomalies

• Test assumptions with confidence

The platform was also expanded to include site-wide water, gas and electricity monitoring, supporting better cost apportionment, compliance (eg Legionella), and long-term energy planning.

Wincanton is one of the UK’s leading providers of supply chain solutions. At its Corby site, business volumes rose by nearly 15% in 2012 — with a marked increase in staff numbers and operational hours. Despite this significant uptick in activity, Wincanton managed to keep energy consumption virtually flat. Working with Optimal Monitoring, they harnessed data insights to improve energy efficiency and achieve more with less.

Using Optimal Monitoring’s Business Dynamics™ platform, Wincanton gained full visibility of energy consumption in relation to operational output. By integrating energy data with process control information, the system delivered real insight into when and how energy was being used — and, crucially, how it related to performance.

Key features included:

• Real-time regression analysis correlating energy demand with specific operational drivers.
• Correlation coefficients to verify relationships between expected and actual energy use.
• Ongoing tracking and forecasting capabilities to support long-term planning and failure prediction.

This approach shifted energy from being an uncontrollable overhead to a measurable, manageable cost of sale — giving operational teams greater ownership and accountability.

• 15% increase in pallet throughput
• Just 0.3% increase in energy consumption (an extra 2,574 kWh)
• Over 866,000 kWh of energy managed for improved efficiency
• No impact on operational performance — processes were adapted to align with best practice efficiency levels

Wincanton proved that smarter energy management can support, not hinder, high-performance logistics.

Following the success at Corby, Wincanton is exploring further applications of Optimal Monitoring’s platform across other sites. With a scalable model in place, the business is well-positioned to embed energy accountability across operations, delivering cost savings, improved resilience, and valuable data-led insights at every level.