Uncovering Real-time Answers Hidden In Your Data

Unified Intelligence

We believe the future of AI is embedded, operationally aware, and continuously learning. We’re building the intelligence layer for critical infrastructure operations.

Product → 001

Continuously updating operational picture.

Entopy creates a live operational intelligence layer by combining real-time data, targeted predictive models and a continuously evolving world model.

Product → 002

Proactive communication with operators.

The Entopy Operational Agent detects forming operational risks, understands evolving context and proactively communicates with operators.

Product → 003

Scenario analysis and what-if testing.

The Entopy Strategy Agent enables operators to explore scenarios, test interventions and understand operational | impact before decisions are made.

Country

Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Explore

Trusted →

Resources.

Unified Intelligence: a new era in decision support.

Quantifying the value of knowing earlier.

PAPER

Unified Intelligence: a new era in decision support.

Why operational intelligence must evolve beyond dashboards, digital twins, and copilots to become an always-on capability for understanding and shaping complex systems.

Read paper

Paper

Quantifying the value of knowing earlier.

A practical framework for quantifying the ROI of Unified Intelligence by measuring the operational, financial, and strategic value of knowing earlier and acting sooner.

Read paper

PAPER

Building Unified Intelligence.

A practical examination of the economics, risks and opportunity costs of building Unified Intelligence versus partnering with specialist providers.

Read paper

Entopy AI Agent

Watch the webcast for a look at Entopy’s AI Agent, combining LLMs with AI-enabled Digital Twins to support operational leaders across ports and logistics.

Watch video

Entopy Unified Intelligence

Watch the webcast exploring Unified Intelligence as a category, application and technology; covering what it is, why it’s needed and how it works.

Watch video

Always-On Intelligence

Watch the webcast exploring Always-on Intelligence. How real-time awareness helps organisations detect risks, surface opportunities and act faster.

Watch video

CEO Problem

How leading teams connect every mile with confidence.

Watch video

Latest news.

Entopy Named AI SME of the Year at the National AI Awards 2026.

June 16, 2026

Micromodels for mega impact: Optimising infrastructure one layer at a time.

June 10, 2026

How to calculate the ROI of Unified Intelligence in complex operations?

May 25, 2026

Operate ahead of consequence →

Unified Intelligence

Build your intelligence layer.

Harness the future of operational intelligence.

Entopy is the always-on intelligence layer that helps complex operations anticipate consequence and act earlier.

Speak with an expert

PAPER

Unified Intelligence: a new era in decision support.

Why operational intelligence must evolve beyond dashboards, digital twins, and copilots to become an always-on capability for understanding and shaping complex systems.

Read paper

Paper

Quantifying the value of knowing earlier.

A practical framework for quantifying the ROI of Unified Intelligence by measuring the operational, financial, and strategic value of knowing earlier and acting sooner.

Read paper

PAPER

Building Unified Intelligence.

A practical examination of the economics, risks and opportunity costs of building Unified Intelligence versus partnering with specialist providers.

Read paper

01 | A continuously updating operational picture.

Inside the system ↘

Data foundation

Real-time and historical data from both internal and external sources is cleansed, standardised, validated and enriched using Entopy core.

Entopy’s proprietary synthetic data generation technology enables sparse and incomplete datasets to be improved and rare event data to be expanded.

AI micromodels

Many discrete, targeted AI/ML models are deployed to specific areas of the operation, predicting specific dynamics.

Outputs from many micromodels are dynamically integrated together with real-time and historical data to create a dynamic network of intelligence.

Dynamic intelligence network

A continuously updated intelligence layer connects data, models and operational context into a shared view of what is happening now.

This enables teams to understand evolving conditions, anticipate change, and act earlier with greater confidence.

02 | A shared model of operational reality.

Connected view ↘

Operational context

Data is mapped against assets, events, constraints and workflows so teams can see what each signal means in context.

This creates a connected operational model rather than another static dashboard.

Live dependencies

Relationships between systems, locations and decisions are represented as changing dependencies.

Teams can understand how local changes may affect wider network performance.

Continuous alignment

The model updates as conditions change, keeping people, processes and intelligence aligned.

This helps reduce blind spots and supports faster operational coordination.

03 | Simulation and decision support.

Scenario layer ↘

What-if modelling

Operational scenarios can be simulated against the current state of the system.

This allows teams to explore options before committing resources.

Risk signals

Entopy highlights potential disruption, bottlenecks and cascading impacts before they become visible manually.

Decision-makers can act earlier with better evidence.

Recommended actions

Insights are translated into practical operational choices and workflow prompts.

This connects intelligence directly to action.

04 | Intelligence embedded into workflow.

Action layer ↘

Workflow integration

Insights are surfaced where teams already work, reducing friction and improving adoption.

This keeps intelligence connected to the moments where decisions are made.

Shared reporting

Teams can generate consistent reporting from the same operational intelligence layer.

This improves alignment between strategic, tactical and operational stakeholders.

Compounding capability

As the system learns from more data and decisions, the organisation builds a stronger intelligence foundation.

The result is an operational capability that improves over time.

01 | A continuously updating operational picture.

Inside the system ↘

Data foundation

Real-time and historical data from both internal and external sources is cleansed, standardised, validated and enriched using Entopy core.

Entopy’s proprietary synthetic data generation technology enables sparse and incomplete datasets to be improved and rare event data to be expanded.

AI micromodels

Many discrete, targeted AI/ML models are deployed to specific areas of the operation, predicting specific dynamics.

Outputs from many micromodels are dynamically integrated together with real-time and historical data to create a dynamic network of intelligence.

Dynamic intelligence network

A continuously updated intelligence layer connects data, models and operational context into a shared view of what is happening now.

This enables teams to understand evolving conditions, anticipate change, and act earlier with greater confidence.

02 | A shared model of operational reality.

Connected view ↘

Operational context

Data is mapped against assets, events, constraints and workflows so teams can see what each signal means in context.

This creates a connected operational model rather than another static dashboard.

Live dependencies

Relationships between systems, locations and decisions are represented as changing dependencies.

Teams can understand how local changes may affect wider network performance.

Continuous alignment

The model updates as conditions change, keeping people, processes and intelligence aligned.

This helps reduce blind spots and supports faster operational coordination.

03 | Simulation and decision support.

Scenario layer ↘

What-if modelling

Operational scenarios can be simulated against the current state of the system.

This allows teams to explore options before committing resources.

Risk signals

Entopy highlights potential disruption, bottlenecks and cascading impacts before they become visible manually.

Decision-makers can act earlier with better evidence.

Recommended actions

Insights are translated into practical operational choices and workflow prompts.

This connects intelligence directly to action.

04 | Intelligence embedded into workflow.

Action layer ↘

Workflow integration

Insights are surfaced where teams already work, reducing friction and improving adoption.

This keeps intelligence connected to the moments where decisions are made.

Shared reporting

Teams can generate consistent reporting from the same operational intelligence layer.

This improves alignment between strategic, tactical and operational stakeholders.

Compounding capability

As the system learns from more data and decisions, the organisation builds a stronger intelligence foundation.

The result is an operational capability that improves over time.

01 | A continuously updating operational picture.

Inside the system ↘

Data foundation

Real-time and historical data from both internal and external sources is cleansed, standardised, validated and enriched using Entopy core.

Entopy’s proprietary synthetic data generation technology enables sparse and incomplete datasets to be improved and rare event data to be expanded.

AI micromodels

Many discrete, targeted AI/ML models are deployed to specific areas of the operation, predicting specific dynamics.

Outputs from many micromodels are dynamically integrated together with real-time and historical data to create a dynamic network of intelligence.

Dynamic intelligence network

A continuously updated intelligence layer connects data, models and operational context into a shared view of what is happening now.

This enables teams to understand evolving conditions, anticipate change, and act earlier with greater confidence.

02 | A shared model of operational reality.

Connected view ↘

Operational context

Data is mapped against assets, events, constraints and workflows so teams can see what each signal means in context.

This creates a connected operational model rather than another static dashboard.

Live dependencies

Relationships between systems, locations and decisions are represented as changing dependencies.

Teams can understand how local changes may affect wider network performance.

Continuous alignment

The model updates as conditions change, keeping people, processes and intelligence aligned.

This helps reduce blind spots and supports faster operational coordination.

03 | Simulation and decision support.

Scenario layer ↘

What-if modelling

Operational scenarios can be simulated against the current state of the system.

This allows teams to explore options before committing resources.

Risk signals

Entopy highlights potential disruption, bottlenecks and cascading impacts before they become visible manually.

Decision-makers can act earlier with better evidence.

Recommended actions

Insights are translated into practical operational choices and workflow prompts.

This connects intelligence directly to action.

04 | Intelligence embedded into workflow.

Action layer ↘

Workflow integration

Insights are surfaced where teams already work, reducing friction and improving adoption.

This keeps intelligence connected to the moments where decisions are made.

Shared reporting

Teams can generate consistent reporting from the same operational intelligence layer.

This improves alignment between strategic, tactical and operational stakeholders.

Compounding capability

As the system learns from more data and decisions, the organisation builds a stronger intelligence foundation.

The result is an operational capability that improves over time.

01 | A continuously updating operational picture.

Inside the system ↘

Data foundation

Real-time and historical data from both internal and external sources is cleansed, standardised, validated and enriched using Entopy core.

Entopy’s proprietary synthetic data generation technology enables sparse and incomplete datasets to be improved and rare event data to be expanded.

AI micromodels

Many discrete, targeted AI/ML models are deployed to specific areas of the operation, predicting specific dynamics.

Outputs from many micromodels are dynamically integrated together with real-time and historical data to create a dynamic network of intelligence.

Dynamic intelligence network

A continuously updated intelligence layer connects data, models and operational context into a shared view of what is happening now.

This enables teams to understand evolving conditions, anticipate change, and act earlier with greater confidence.

02 | A shared model of operational reality.

Connected view ↘

Operational context

Data is mapped against assets, events, constraints and workflows so teams can see what each signal means in context.

This creates a connected operational model rather than another static dashboard.

Live dependencies

Relationships between systems, locations and decisions are represented as changing dependencies.

Teams can understand how local changes may affect wider network performance.

Continuous alignment

The model updates as conditions change, keeping people, processes and intelligence aligned.

This helps reduce blind spots and supports faster operational coordination.

03 | Simulation and decision support.

Scenario layer ↘

What-if modelling

Operational scenarios can be simulated against the current state of the system.

This allows teams to explore options before committing resources.

Risk signals

Entopy highlights potential disruption, bottlenecks and cascading impacts before they become visible manually.

Decision-makers can act earlier with better evidence.

Recommended actions

Insights are translated into practical operational choices and workflow prompts.

This connects intelligence directly to action.

04 | Intelligence embedded into workflow.

Action layer ↘

Workflow integration

Insights are surfaced where teams already work, reducing friction and improving adoption.

This keeps intelligence connected to the moments where decisions are made.

Shared reporting

Teams can generate consistent reporting from the same operational intelligence layer.

This improves alignment between strategic, tactical and operational stakeholders.

Compounding capability

As the system learns from more data and decisions, the organisation builds a stronger intelligence foundation.

The result is an operational capability that improves over time.

Operate ahead of consequence →

Unified Intelligence

Build your intelligence layer.

Harness the future of operational intelligence.

Entopy is the always-on intelligence layer that helps complex operations anticipate consequence and act earlier.

Speak with an expert

Explore the organisations empowering operations with Unified Intelligence..

Port of Dover Case

BLOG ARTICLE

Building Unified Intelligence for Complex Operations

How Entopy’s architecture unifies fragmented data, context, and reasoning to deliver real-time understanding and faster decisions.

Read article

PAPER

Ontology Driven AI For Complex Systems

How semantic operational models improve context awareness and consequence reasoning.

Read paper

PAPER

Scenario Intelligence At Scale

Building operational foresight using AI reasoning and consequence-aware intelligence.

Read paper

Operate ahead of consequence →

Unified Intelligence

Build your intelligence layer.

Harness the future of operational intelligence.

Entopy is the always-on intelligence layer that helps complex operations anticipate consequence and act earlier.

Speak with an expert

CASE STUDY

Real-time freight visibility, event detection, and sub-second data access across complex supply chains.

Fujitsu transformed cross-border freight visibility with Entopy, enabling real-time event detection and sub-second data access across complex supply chains.

In brief ↘

Challenge

Fujitsu’s Atamai Freight platform needed to unify fragmented, multi-party supply chain data into a single, trusted view.

Siloed systems, dynamic relationships, and limited real-time visibility constrained collaboration and delayed decision-making.

Solution

Entopy deployed an ontology-driven digital twin, structuring data around consignment journeys and linking entities such as consignments, vehicles, ports, and smart seals.

Real-time data ingestion via APIs, combined with targeted capture and segmentation, enabled scalable, secure data sharing across stakeholders.

Unified Intleligence

Entopy’s platform applies intelligent data orchestration and event detection to identify real-time events, such as arrivals, delays, and anomalies, across complex datasets.

This enables predictive insight, automated alerts, and faster, more informed decision-making.

Outcomes ↘

>10

Stakeholder types

<0.2s

Data access via API call

Improved visibility, reduced border processing times

Paul Luckett

Head of Digital Trader Services → Fujitsu Services UK

Working with Entopy has been refreshing. Their platform is supporting some of the fundamental elements of the Atamai Freight service.

CASE STUDY

Discrete model predictions, dynamic orchestration, and proactive communication with operators to support multi-stakeholder ecosystem.

Harwich Haven enabled coordinated decision-making across its port ecosystem with Entopy, optimising vessel scheduling under complex operational constraints.

In brief ↘

Challenge

Harwich Haven operates in a highly constrained environment, where vessel movements are impacted by tidal windows, resource availability, and interdependent schedules.

Limited shared visibility across stakeholders made it difficult to coordinate decisions, leading to inefficiencies, delays, and suboptimal berth utilisation.

Solution

Entopy deployed a digital twin of the port ecosystem, connecting vessel schedules, resource constraints, and operational data into a shared, real-time intelligence layer.

By modelling relationships between vessels, towage, berths, and constraints, the platform enabled stakeholders to identify conflicts early and coordinate optimal sequencing decisions.

Unified Intleligence

Entopy’s platform combines ontology-driven digital twins with interconnected micromodels to model vessels, resources, and constraints in real time.

An intelligent agent, supported by memory, proactively communicates emerging issues and enables rapid scenario testing, allowing stakeholders to evaluate options and coordinate optimal decisions under disruption.

Outcomes ↘

Port operational stakeholders

~20%

Improvement to operator response time

Improved cross-ecosystem coordination.

CASE STUDY

Federated micromodel network, real-time data orchestration, and event propagation to support operational decisions under pressure.

Port of Dover reduced traffic congestion with Entopy, enabling predictive decision-making and earlier intervention across one of the UK’s busiest transport routes.

In brief ↘

Challenge

The Port of Dover needed to manage highly variable freight traffic flows, with limited visibility of incoming volumes and congestion risks.

Unpredictable arrivals and external factors (e.g. incidents, weather) created bottlenecks, reducing operational efficiency and increasing disruption across the network.

Solution

Entopy deployed its AI-enabled digital twin platform, combining real-time data with predictive micromodels to forecast freight arrivals and traffic conditions.

The platform provided forward-looking intelligence on volumes and congestion, enabling operators to proactively allocate resources and manage flow.

Unified Intleligence

Entopy’s micromodel architecture chains together discrete models, integrating real-time, event-based data, such as accidents, weather, and network disruptions, to create a continuously adapting traffic model.

This enables dynamic, network-wide forecasting of congestion and freight flows, even under disruption.

Outcomes ↘

25%

Reduction in Transport Access Protocol

30 minute

Improvement to operator response time

Less disruption, better flow, improved awareness.

CASE STUDY

Data-driven modelling, and complex process orchestration, to boost renewable energy production.

Bio Capital increased renewable energy output with Entopy, using real-time AI to optimise biogas production across its UK-wide AD portfolio.

In brief ↘

Challenge

Bio Capital needed to optimise biogas production across multiple AD sites, where performance is driven by complex, interdependent variables such as feedstock and process conditions.

Solution

Entopy deployed its AI-driven data platform to unify operational data across Bio Capital’s AD sites, enabling real-time monitoring and predictive modelling of biogas production.

By combining historical and live data, the platform identifies key performance drivers and supports data-driven optimisation of feedstock and operational strategies.

Unified Intleligence

Entopy’s predictive models analyse large-scale operational data to forecast biogas yields and optimise energy production in real time.

An intuitive, LLM-powered interface enables teams to query data and access insights instantly, making advanced analytics accessible across the business without specialist expertise.

Outcomes ↘

98%

Forecasting accuracy

6-week

Mobilisation time

Improve yield forecasting, reduced feedstock.

CASE STUDY

Multi-step passenger flow prediction, and dynamic forecasting, to support airport operators improve experience.

Glasgow Airport improved passenger flow visibility with Entopy, enabling predictive planning and scenario testing across surface access and terminal operations.

In brief ↘

Challenge

Glasgow Airport needed to manage growing passenger volumes, with limited visibility of how people arrived, moved through the airport, and impacted infrastructure.

Fragmented data and unpredictable demand made it difficult to anticipate congestion and plan operations effectively.

Solution

Entopy deployed an AI-enabled digital twin of passenger movement, modelling journeys from road access through to check-in and security.

By combining historical and live data, the platform provided forward-looking insight into passenger volumes, car park usage, and processing times across the airport.

Unified Intleligence

Entopy’s micromodels simulate passenger behaviour across the airport, predicting arrivals, dwell times, and processing flows in granular time intervals.

Operators can also test future scenarios, such as infrastructure changes or demand spikes, in a virtual environment, enabling proactive planning and optimisation.

Outcomes ↘

94%

Accuracy of traffic flow models

passenger journey stages

Improved operational planning & scenario testing.

Jon Matthews

Transformation Director → AGS Airports

We are thrilled to collaborate with Entopy as they bring their cutting-edge digital twin technology to Glasgow Airport. Enhancing passenger experience and operational efficiency is a cornerstone of our vision.

Resources.

BLOG ARTICLE

Building Unified Intelligence for Complex Operations

How Entopy’s architecture unifies fragmented data, context, and reasoning to deliver real-time understanding and faster decisions.

Read article

PAPER

Ontology Driven AI For Complex Systems

How semantic operational models improve context awareness and consequence reasoning.

Read paper

PAPER

Scenario Intelligence At Scale

Building operational foresight using AI reasoning and consequence-aware intelligence.

Read paper

An introduction to
Unified Intelligence.

An overview of Unified Intelligence and why it represents a new category.

‘Always-on’
intelligence.

Understanding continuous intelligence and real-time decision-making.

What is
Digital Twin?

A deep dive into Digital Twin and its use in critical infrastructure.

What is
ontology?

Ontology as the foundation of Entopy’s intelligence platform.

AI micromodels
explained.

How AI micromodels enable scalable and composable intelligence.

Synthetic data
generation.

Expanding limited datasets with high-quality synthetic data.

AI
Agent.

An AI Agent designed to support operational decisions and workflows.

Operational
memory.

Persisting system context to inform future decisions.

Cascading impacts
engine.

Modelling how changes propagate across complex systems.

Webcast.

See all webcast

Entopy AI Agent

Watch the webcast for a look at Entopy’s AI Agent, combining LLMs with AI-enabled Digital Twins to support operational leaders across ports and logistics.

Watch video