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


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Entopy is redefining unified intelligence
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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.

Artificial Intelligence

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

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

Outcomes ↘
5+
Integrated stakeholder types
<0.2s
Data access via API call
Improved visibility, reduced border processing times

Turning operational complexity into growth potential across industries

Port of Dover Case →

Intelligent traffic management.

Earlier insight for live traffic decisions Case Study
Harwich Haven Authority case study
Harwich Haven Authority Case →

Dynamic port resource planning.

Shared intelligence for berth planning Case Study
Fujitsu case study
Fujitsu Case →

Cross-border supply chain intelligence.

Real-time freight visibility and alerts Case Study
Bio Capital Case →

Real-time energy optimisation.

Predictive optimisation for energy output Case Study
Glasgow Airport case study
Glasgow Airport Case →

Passenger movement intelligence.

Predictive insight for passenger flow Case Study
Product → 001

Continuously updating operational picture.

Turn fragmented live data into a shared, decision-ready understanding of the operation.

Product → 002

Proactive communication with operators.

Our AI Agent identifies emerging situations, explains why they matter, and notifies operators with recommended actions.

Product → 003

Scenario analysis and what-if testing.

Teams can use the AI Agent to explore future scenarios, compare trade-offs, and pressure-test decisions in advance.

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.
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 and we look forward to working together to grow the adoption of the Atamai Freight service and on future projects.

Unified Intelligence

Anticipate the next_
→
with Unified Intelligence

Identify your AI opportunity

Accelerating operational decision-making with Entopy

Entopy helps organisations uncover high-value AI opportunities across complex operations, connecting data, context, and workflows to support faster, more confident decisions.

Explore Entopy
Team →
Background

Inside the Entopy system.

Entopy is a Unified Intelligence layer that continuously understands operations, predicts what’s next, and helps you act with confidence.

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 | Ontology & situational memory.

Inside the system ↘

Ontology

Entopy’s proprietary ontology enables dynamic integration and orchestration of data across domains.

The ontology enables data to be attributed to entities with relationships and rules baked in.

World model

The result is a digital model that reflects the target operation, creating a unique world model.

This gives the system a structured understanding of the operational environment and how its parts relate.

Situational memory

Entopy’s ontology is self-adaptive, enabling new facts to be created autonomously.

Situational memories are derived from situation lifecycle monitoring and treated as first-class entities within the ontology, supporting learning and experience within the system over time.

03 | Live operational intelligence (Always-on Agent).

Inside the system ↘

Situational lifecycle tracking

An AI agent continuously identifies and tracks emerging situations, monitoring how they evolve, escalate and resolve.

As conditions change, it assesses impact and recommends actions, learning from every outcome to improve future decisions.

Proactive participation

The agent participates in operational workflows in real time, using situational awareness to anticipate, coordinate and optimise responses across stakeholders.

It does this through the distribution of communications through familiar channels as well as following defined escalation paths.

Operational response

Intelligence is translated into timely, practical actions that support teams as situations develop.

This enables operators to respond earlier, coordinate more effectively and improve decisions as conditions change.

04 | Exploratory intelligence (Scenario Agent).

Inside the system ↘

Deep analysis & scenario modelling

A dedicated AI agent for deep analysis uses multi-step reasoning to evaluate situations and test scenarios.

It draws on the full Unified Intelligence layer to deliver insights and outcomes grounded in operational reality.

Wargame & cascading impacts

Built into the agent is a wargaming engine that supports the ability to simulate and test decisions.

Alongside it, a cascading impacts engine evaluates how changes propagate through interconnected entities, constraints and processes.

Decision intelligence

The system translates exploration into clear decision pathways, highlighting trade-offs and likely outcomes.

This enables organisations to pressure-test strategies and act with greater confidence before consequences unfold.

Fujitsu transformed cross-border freight visibility.

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

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Real-time operational intelligence for complex, consequence-aware decisions.

Entopy System Detail - 4 Section Responsive Slider Test

01 | A continuously updating operational picture.

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 | Intelligence connected across every operational layer.

Connected layers ↘

Source systems

Operational, commercial and external signals are brought into a single connected environment.

The system keeps source context intact while creating a consistent intelligence foundation.

Operational context

Events, constraints and live conditions are interpreted against the real operational model.

This helps teams understand what is changing, what matters, and where intervention is needed.

Shared visibility

Teams work from the same connected picture instead of fragmented reports and isolated dashboards.

Decisions become faster because intelligence is available in the context where work happens.

03 | Scenario testing before conditions become critical.

Scenario layer ↘

Simulation

Teams can test operational changes and external pressures against a live model of the environment.

This creates a safer way to understand impact before committing action in the real world.

Prediction

AI models identify likely outcomes, emerging risks and knock-on effects across connected systems.

Decision makers can move from reactive reporting to earlier, evidence-led intervention.

Operational response

Recommended actions are grounded in live intelligence, not isolated assumptions.

This supports more confident coordination across teams, assets and partners.

04 | Workflows that turn intelligence into action.

Workflow layer ↘

Guided action

Insights are connected to workflows so teams can act directly from the intelligence layer.

The system supports decision making without forcing users to move between disconnected tools.

Human oversight

AI supports decisions while keeping operational judgement and accountability with expert teams.

This helps build trust in the system as recommendations become easier to understand and validate.

Continuous learning

Outcomes feed back into the intelligence environment, improving future predictions and responses.

The platform becomes more valuable as more operational context is connected over time.