AI Solutions Across Industries

AI and ML solutions are revolutionizing processes, management and intelligence across industries, with new use cases being developed daily. Progress is rapid, and in many industries AI-driven digital transformation is quickly moving from novel pilot project to essential competitive advantage. Don’t get left behind.

Industry 4.0

Artificial intelligence (AI) and machine learning are revolutionizing multiple industries by enabling computers to perform tasks that traditionally required human intelligence, such as learning, problem-solving, decision-making, and communication. These technologies are transforming the way businesses operate and deliver value to customers, leading to what some people refer to as “Industry 4.0” or the Fourth Industrial Revolution.

Our background means that we have particular speciality in applications of AI to large commodity production sectors: oil and gas, agriculture and metals, as well as manufacturing and production further down the value chain. The digital world of bits is transforming how we extract, transport, process and market the oldest raw materials of industrial civilisation. We know that AI-driven digital transition will transform every aspect of the business, from exploration to maintenance, from logistics to market price predictions, from back-office automation to analysis of customer behaviour. See below for some discussion of how AI is transforming industry – and some of the work we have already done for clients in different industries.

Oil and Gas

The Oil and Gas industry is embracing the transformative potential of enterprise AI – and seeing huge impact at every stage of the value chain. Revolutionary AI applications are being found in exploration and well development, well monitoring, production and maintenance scheduling, logistics optimisation and, crucially, safety and risk assessment.

Imagine: Effortless, real-time monitoring of industrial processes and assets across the value chain.

Exploration and Production companies, Mid- and downstream operators and industry service providers will increasingly find they need the predictive intelligence of AI to stay one step ahead of the competition. 

The Oil and Gas industry is also at a crucial moment in its history, facing huge political pressure on environmental issues and pollution, even as the world remains dependent on the energy and products the industry reliably produces. At SourceHorizontal we believe that AI monitoring of leaks, spills and other environmental risks can prove that the industry is fully capable and willing to innovate internally to produce ever cleaner energy, without the need for destructive and overreaching over-reaching government regulation. Advanced technologies will always be better than cumbersome and inflexible regulatory intervention.

We worked with a leading petrochemicals company to improve reliability of both equipment and processes. Tens of millions of dollars worth of value was going missing every year due to unplanned maintenance and plant downtime. Key goals were to identify and address specific culprits for this disruption across the plant and build a system that predict failures of process and equipment. 

We initiated a comprehensive process of data discovery to identify, clean and sort any potentially salient data. We ingested over a decade of maintenance, asset, sensor and visual data to our system and began an exploratory data analysis of everything. From there, we developed an anomaly model to analyse the data and testing on historical data we were able to identify various tags and powerful predictive analysis on causes of failure.

Our solution used this model built on historical data with  real-time data from an IoT network integrated with the existing industrial infrastructure to achieve a continuous-improving predictive model. Our fully-customised solution integrated seamlessly with the plant’s existing operational technology, and we were able to iterate the software in response to feedback from plant’s ownership and operators.


As a labor-intensive industry, agriculture is ripe for digital transformation, with manual processes that can be streamlined and automated through the use of AI and machine learning.

Imagine: Using remote sensing to optimize field management and boost crop yield.

From precision agriculture to smart agricultural robots to big data analysis, AI is helping to increase efficiency and productivity in the agricultural industry. AI can be used to monitor crop health and soil conditions, allowing farmers to more accurately manage their crops and increase yields. AI is also helping to automate tasks such as planting, harvesting, and sorting, freeing up farmers’ time for more profitable activities. Drone and satellite imagery can analyze crop condition, detecting pest infestations early and automating precise spreading of fertiliser. The digital transition is also disrupting livestock management, helping farmers monitor the health and welfare of their animals. And predictive analytics can help farmers plan, with more accurate weather and climate predictions.

We worked with a major US agricultural producer on a project to improve efficiency in production scheduling. The company had various detailed scheduling systems across the business, but had found that the optimisation components were not providing them with the intelligence or flexibility that they were looking for and were cognizant that they were leaving value on the table.

To solve the problem, we leveraged the company’s access to vast data resources,  ingesting over 800 billion data points from a huge variety of sources, including demand forecasting, sales and purchase orders, inventories, bills of materials etc. etc. But we also knew that there was salient data the customer needed, both proprietary and external, that wasn’t currently available to the schedulers and together with the client we began a major process of data discovery and data sourcing.

We then built a machine learning model to find efficiencies and scheduling insights across the production value chain. Over a 4 month pilot period we worked closely with the in-house scheduling team who would be using the product, constantly iterating to make sure the model was useful, intuitive and usable with their existing systems.

Our system was able to empower the client’s team to find major efficiencies in scheduling, use powerful new predictive tools to anticipate problems and opportunities and to gain a new level of analytic overview of their production processes.


The Manufacturing sector has recognized the potential of Artificial Intelligence (AI) to help cut operating costs, predict equipment failure, and increase output.

Imagine: solving production bottlenecks before they even happen.

AI-based solutions are being used for Smart Asset Management and Predictive Maintenance to reduce downtime and ensure the smooth running of operations. AI-powered chatbots are being used to provide customer support, while data-driven analytics are used to optimize processes, control product quality and track worker availability and efficiency. AI-driven computer vision is being used for retail outlets to study customer behaviour, while AI-based solutions are used for market assessment and analysis. 

Manufacturers can deploy AI to gain a new level of visibility of and control over their operations, leading to improved asset and process efficiency. AI solutions can help your business lower operational costs, experience fewer unplanned downtimes, optimize the supply chain, boost on-site safety and security, and encounter fewer production challenges. With a holistic view of operations, from process data, asset performance data, and even environmental data AI can deliver powerful and novel insights to improve productivity, throughput time, and overall product quality. Additionally, our solutions can be integrated with an IIoT network composed of cameras, CCTV, sensors, and OEM sensors. This enables proactive tracking and up-keep of asset health and performance conditions, as well as suggesting design recommendations to streamline the process better. AI-capabilities can also help firms better understand customer behaviour and psychology, as well as market analytics to unlock key business insights.

Mining and Metals

Metals and mining operations are being revolutionized by next-generation predictive asset maintenance and management tech, allowing for nimbler and more profitable systems.

Imagine: identifying safety issues before any staff are on site.

Exploration data analyzed to unlock new insights. Mines monitored continuously to spot efficiencies and risks. With AI, mining sites can have a comprehensive overview of the operations, allowing them to identify potential risks, ensure the security of their sites, and ensure sustainable development. AI can also monitor mining site operations continuously based on satellite or drone imagery, and help in monitoring and reporting risks, improving on-site health, safety and environmental impact, leading to reduced on-site injuries and accidents and heightened resource efficiency.

AI-powered solutions can be used to aid exploration, by analyzing Remote Sensing and Natural Language data, and employing intelligent solutions to identify deposit locations.

We worked with a major aluminium smelting company to redesign their operational technology, integrating data and analytics to transform the visibility into existing processes and their decision-making capabilities. The client was aware that existing operational procedures were based on inflexible protocols with little real-time oversight of processes and that updates to these processes normally took place only after something had gone wrong unexpectedly. They hoped that better data-driven analytics could help them find efficiencies, avoid downtime and improve quality control.

We set out to construct a full digital twin for the smelting operation. Our system gave shop-floor operators full real-time visibility over the production process and understanding of the key factors driving performance. Working closely with operators, we iterated the custom-built system over a three month period to test robustness and effectiveness.

The system succeeded in improving energy efficiency, reducing process variability, improving operational control and, through the integrated decision support system, empowered operators to respond to all production scenarios with effective, data-informed decisions. The client reported a 1.7% efficiency improvement with no loss of product quality.

Consumer Goods

Predict consumer trends, monitor customer behaviour, personalize marketing – AI-powered data analytics can help your business get closer to your customers.

Imagine: Knowing what the customer wants, before they do.

Predict consumer trends, monitor customer behaviour, personalize marketing – AI-powered data analytics can help your business get closer to your customers. But it can also unlock value across the supply chain. AI-driven analytics can help to optimize the supply chain, reducing costs, improving the efficiency of operations, minimizing over-production and out-of-stock scenarios.

Bringing together data from across the value chain and market insights, AI can help your business make efficiency savings in manufacturing and logistics, with real-time insights allowing you to make agile decisions to keep one step ahead. For example, with highly-volatile energy prices there are huge energy cost savings opportunities by making timely decisions at every stage of the product lifecycle. AI can also help your customers’ demands for sustainability by reducing packaging waste and identifying efficient opportunities for the use of recycled materials.


AI solutions are helping the plastics production industry reduce waste, increase efficiency, and improve asset utilization.

Imagine: Automated quality control that can identify problems before they even happen.

As with Oil and Gas, the plastics sector has faced increased public scrutiny in recent years, with public awareness of the potential damage of badly-handled plastic waste. AI/ML solutions can help the industry respond to concerns around waste and respond to customers demands for sustainable production, whilst at the same time making savings across the value chain. AI-driven analytics and machine learning can help to accurately predict how much plastic material is needed for a product, identify defects in production, and better manage assets. AI can also be used to identify defects and errors in the production process. AI-driven inspection solutions can scan plastic products for flaws or defects and alert operators in real-time. Unprecedented market insights into the volatile market for recycled plastics can also help identify opportunities for efficient and smart integration of recycled materials into production processes. As well as reducing waste, ultimately, these solutions can help to reduce costs and improve the overall production process.

Again, at SourceHorizontal we believe that AI/ML solutions can help industries show that the free market is able to innovate to tackle environmental and sustainability challenges, and that advanced technology will beat cumbersome regulatory interventions every time.