If data is the new oil, intelligence is the combustion engine.

Imagine the power of knowing what is going to happen before it happens, or being able to test future decisions before you make them?

We call it

AI-enabled Digital Twin

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What we do?

Applying Artificial Intelligence (AI) and Big Data Analytics to unique, dynamic, complex real-world operational environments is challenging and requires a new approach. Our AI-enabled Digital Twin platform combines AI, Big Data, and workflows to deliver unrivalled intelligence to operators.

The Benefits of Entopy

Highly
customisable
intelligence

Every real-world, operational environment is unique From the type of operation and workflows within it, to the systems being used and the data being generated. Entopy’s technology and software enables us to deploy models specifically for your operation, integrating with your data and capturing specific workflows within your operation to deliver effective and long-lasting intelligence.

In high pressure, fast-paced operational environments, accuracy is everything. Entopy’s micromodels technology delivers networks of small and very specific AI models, with absolute focus on elemental parts of a problem. This enabled us to achieve very high accuracy of the underlying intelligence feeding into the overall model.

Highly accurate
models deliver highly
effective results

Overcome data
barriers to maximise
impact

Data availability is a rising challenge preventing businesses from harnessing the power of AI and Big Data. Our distributed approach enables us to bring together data across multiple stakeholders in a way that ensures privacy and security, as well as improving underlying data quality to deliver highly effective results, overcoming critical data barriers.

Now, what’s your
question?

Case studies

Entopy’s software was used to deliver predictive intelligence across the strategic road network of a major UK port to inform operators of future freight flows to support data-driven operational intervention to help mitigate and control congestion.

Delivering effective predictive intelligence across the strategic network is challenging. The network is dynamic, comprising many dynamics and has frequent outlier occurrences such as traffic accidents, scheduled road networks and one-time events that alter traffic activity.

Entopy’s software was used to create a semantic model of the road network, depicting key aspects. This creates a dynamic model able to capture and organise data in a way that can deliver the actionable intelligence that the port needs. This included understanding historical dynamics and using real-time data to deliver operational intelligence. But it also required a novel approach to Artificial Intelligence, able to combine computational and stochastic models to be able to deliver predictive intelligence, considering ‘black swan’ events that are more difficult to predict.

Entopy’s novel micromodels technology offers a more dynamic approach to predictive intelligence. The technology focuses on breaking complex predictive problems into smaller, specific ‘chunks’. Machine learning models can then be focused on more ‘atomic’ pieces of the problem. Entopy’s foundational software orchestrates the outputs of multiple ‘micromodels’ and networks them together semantically with real-time, event-based data, creating a network capable of delivering dynamic predictive intelligence.

Entopy deployed multiple micro-machine-learning models at junctions across the strategic road network. These models predicted traffic by type using multiple inputs including weather, day, time, and seasonality. The models are then linked semantically, combining spatial and temporal logic to capture relevant relationships. For example, there is a directional relationship between Junction 10 and Junction 11, each with independent micro-machine-learning models predicting traffic flows at those specific parts of the road at given time intervals.

Real-time event-based data is captured from multiple source systems including RNS, highways and social media. These events, including traffic accidents, scheduled road networks and one-time events were captured, located, categorised, and formed new nodes on the network, again, semantically mapped to other events and models based on key parameters.

The result is a dynamic predictive network capable of delivering accurate predictive intelligence, considering key inputs and outlier events. Current performance shows predictions derived from Entopy’s software >90% accuracy, using the port’s commercial data as a validation dataset. The software is live in the ports control tower, supporting operators to making more informed and data-driven decisions.

Entopy’s software supported a global hospitality brand to better understand operational dynamics and consumer behaviours, identifying commercial and operational interventions to drive performance.

Large food markets comprising multiple, independent concessionaires are situated globally, offering exciting hospitality venues for a dynamic mix of consumers. Data is ever-present across environments, but the use of disjointed and disparate data is challenging, leading to a lack of intricate and scientific understanding, ultimately driving intuitive decision-making.

Entopy’s software unified and orchestrated data across multiple internal systems to create a dynamic data model, capable of delivering actional intelligence to leaders. Data from various systems was input into Entopy’s core software. Using Entopy’s proprietary software, data was processed and mapped to create a dynamic data model.

Entopy was able to use data from source systems to deliver accurate insights including true conversion rates, average customer value and to identify target customer segments, ultimately leading to a large potential sales uplift per market being identified.

As dynamics are uncovered, the flexibility to extend use of data is critical. With Entopy’s software, new datasets and models can be introduced retrospectively without breaking anything. Data from external sources such as weather and local events, geopolitical dynamics and competitor activity can be factored in, delivering real-time operational intelligence to support more data-driven commercial and operational decision-making. Entopy’s micromodels technology can be added to provide predictive intelligence, further extending the overall intelligence across the operation.

The result is a better understanding of customer and operational dynamics, supporting improved commercial and operational performance.

Entopy supported a major IT services provider, using its software combine data across a complex, multi-stakeholder supply chain ecosystem to create a dynamic data foundation to support a digital trade platform.

Supply chain ecosystems comprise many independent stakeholders, each performing key roles throughout the overall supply chain. From traders to freight operators to ferry operators to ports. Therefore, being able to gain effective operational intelligence over consignments moving through this network is a complex challenge, requiring data from multiple individual organisations to be brought together and made sense of.

Using its proprietary ontology, Entopy can connect to multiple source systems across the supply chain ecosystem, capture data from them and combine that data with other sources, delivering multidimensional insights from it. But critically, it can do this in a way that the data is segmented at a granular level, ensuring granular permission-based controls can be effectively implemented to maintain privacy and data security across the respective stakeholders.

Entopy creates a semantic data model, attributing data to entities, helping to organise the data within its platform. This foundational layer comprises various tools that ensure critical data segmentation can be delivered. Entopy’s dynamic ontology then captures entity relationships within contexts, from which real-time events can be captured and communicated across the supply chain ecosystem.

Critical events regarding a consignment journey can then be communicated to key stakeholders. These events can be used to inform operations or used by authorities to automate processes.

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