Intelligent Middleware

EntityStream is pioneering the Intelligent Data Stream
Realtime flows that renovate and improve your data

Renovated Data Streams

Using a stream as a mechanism to solve a use case is generally more effective and faster at solving the problem than building complex batch processes relying on massive infrastructure.

Internet of Things

Data Augmentation

Capturing data points from a multitude of different IoT devices or sensors around the globe means that there is a vast amount of data generated, but much of this is meaningless without context applied to it. An EntityStream will use the small properties within the data point and augment it with information from internal or external systems to give it a wider metadata scope that can be used to feed it into analytical systems inside your organisation and extract value.


Data points can often contain accurate latitudinal and longitudinal coordinates collected from ubiquitous GPS technology. Such a data point is vital to not only identify the locality of data but also to determine if the device is or will soon be within a pre-determined area often called a radial or polygon. Such areas represent a geo-fence that can be constantly monitored and assessed using an Entitystream to alert monitors of new and vital events and potential opportunities

Transaction Processing

Retail Financial Services

Transactions can originate in retail or commercial situations and in many cases the transactions have a minimal scope. Often payment processing vendors or gateways receive very little data about the transaction and if it is true or fraudulent. An Entitystream adds context to the transaction by identifing the purpose of the transaction plus it can use advanced lookup techniques to determine what product or service that the transaction relates to and can thus determine the best way to handle it

Investment Banking

Trades in a multitude of instruments can occur in a single second in Investment Banking, much of them are classifiable easily using traditional back office and settlement systems. However more and more complex derivative are created by Banks and Investment managers that require more innovative ways to settle and report on activities.
An Entitystream can be built quickly without huge technology investment meaning that the counterparties in a transaction can handle the extra processing needed to complete a transaction within the limits specified and feed data directly to traditional systems.

Swift Messaging

Financial services organisations have increasingly complex and diverse messaging requirements – EntityStream supports SwiftNet the standard for the transmission of trades in Straigh Through Processing


Patient Identification

Healthcare relies upon information to be able to be effective, without a full scope of information health services are unlikely to be able to give the best services when a patient presents. Plus it is key to look for trends and factors outside the patients profile that may be related to family history or from contact tracing. An EntityStream has advanced healthcare oriented data collection (HL7 FHIR 4), matching and publishing techniques to grab and identify patients and the associated administration records to provide such a complete viewpoint to the physician.

Contact Tracing

Patients are not the only aspect of healthcare that is key to identifying a symptom, also the ability to bring in data from a multitude of sources and even to identify personal contacts made between individuals, family members and the physicians themselves. The ability to spin out to discover information in all related data points around it. Using EntityStream relationships can be tenous or determined and measured using facts, enabling quick access to relationships that are less obvious or hidden.

Sequence Matching

Entitystream has advanced Amino Acid and Nucleic Acid Sequence indexing, searching and comparison as part of it’s advanced Bioinformics capabilities

An Example EntityStream

build all crital data augmentation and cleansing in one stream

EntityStream brings together data cleansing, matching, geocoding, machine learning and aggregation into one stream process that can be deployed across an organisation.

Snowflake SnowPipe Compatible

As of version 23 Entitystream is now fully compatible with SnowPipes to push your data directly into Snowflake staging areas


Entitystreams are a NO CODE approach to real time data integration. All work is none in our innovative user interface


Our system allows you to create smart flows using drag and drop, but incorporating massively complex plugins.

In Flow Data Improvements

EntityStream is a real-time data flow that allows for performing complex data improvements within it.
The main purpose of it is to present the user with up-to-date information and keep the state of data updated.


EntityStream enables you to collection multiple data points in real time from many different sources including traditional applications, IoT devices, messages, and sensors, these datapoints are often small and technically oriented. An EntityStream enables you to build a complete view of a business object that can be useful to your business in real time as it flows from its source to your systems.


EntityStream aims to find and unify master data across the enterprise, it does this by matching your data and allowing you to augment the core data elements of your data with additional information from other sources, internal or external. 
Clustering allows you to group similar (but not exactly the same) data points together to form a single relevant data point that represents a business entity.


EntityStream understands that data originates from or represents a location in space, this geographical aspect could simply represent a physical location of a person, asset or event, but it could also represent the locality that owns the data.
In many organisations data is sensitive and should only be processed or stored in a single place, this might be for regulatory purposes or simple efficiency.


Without knowing the past we cannot understand the meaning of now and reliably predict the value of the future. EntityStream can both build models as the data flows through its parallel processing stream, and also use those models to accurately make predictions of the value of the data it can see.
An EntityStream can deploy many Machine Learning algortihms as part of its process flow to in real time act on your data.

Inline Assessment and Alerting

Many organisations dealing with risk, either market, physical, credit, or operational risk do so as part of an analytics process either ov er a period of days or weeks as a retrospective view of the operations. An EntityStream enables you to build your risk modelling into the real time data flows into and across your organisation opening up the opportunity to expose high risk activities before their completion.


The EntityStream Device

Some technical aspects of the Entitystream device and what it can do.


Comparison between data or within a data set or stream can be done in two ways, using traditional deterministic processes or to deploy the use of more holistic fuzzy matching and clustering techniques.


Collecting relationship data that represents formal or informal links between the data is part of the model, but also to be able to utilise those relationships to discover more information about an interconnected entity.


The database layer underneath the EntityStream is distributed using sharding techniques, these can be defined to facilitate geographical spacing of data as well as replication and sharding for performance and efficiency.


The environment is no only fuzzy matching based, but also geospatial enabled so you can use the system to find fuzzy matches within a geofence or geopolygon. Geospatial data is important for some matching where town, city or postcode information is not useful.

Contact us