Alexi Buckingham

Data Language provides AI products and scalable machine learning solutions for global brands.

AI Engineering By Knowledge Graph Experts

AI for High Value Knowledge Organisations
The Problem: Valuable knowledge locked away.

 1. Organisations have high value knowledge assets distributed across systems.
 2. Archives and services are not connected, and the value is locked away.

How we solve it: Liberate and connect your information.

 1. Our AI SaaS products are easily integrated and trained, using your own
    content.
 2. Simply plug in our services, and run your content through the APIs.
 3. Our SaaS learns on the fly, and scales up to whatever volumes you require.
 4. No need for cumbersome training cycles, or for Data Scientists to oversee
    it.

Want to find out more?
Book a meeting with us - we love helping with any type of project.

AI Engineering

Matt Shearer

Data Language is proud to be featured in the "AI for Business" supplement in The
Times (10th December 2020). 

This supplement covers a range of AI solutions across a spectrum of enterprise
use cases.

To implement AI successfully
 1. You need a consistent source of reliable data to feed the AI systems.
 2. You need leadership within the organisation who understand the need to make
    faster, data-driven decisions.

The Times : "AI for Business" - 10th December 2020.Read more
 1. Raconteur AI Section
    [https://www.raconteur.net/topic/technology/artificial-intelligence/]
 2. Data Language Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]
 3. Data Language Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]

The Times "AI for Business" Supplement

Data Language AI for News offers a new market-leading automated news classification SaaS. It beats other SaaS because it uses your own tags.

Data Language AI for News - Automated News Content Classification

Automatically Tag your text with News Tags
We’ve trained our Data Language Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] using a News
training corpus, and are making it available for you to try out.

Simply add your text, and get News tags back. Get the latest News tags,
consistently, and improve your user journeys and SEO.

Industry-leading Auto-tagging AI
Our sophisticated Text AI automated classification service
[https://datalanguage.com/products/datalanguageai/text-ai-services] is designed
with years of experience in Data Science. It beats popular benchmarks, and
tackles all the normal data science issues faced with News auto-tagging.

Get access
Simply use the contact form to request access.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common specialist and scaling problems.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common digital transformation challenges.

Data Language AI - News and AI Product Updates

Data Language AI

Data Language AI for News - Automated News Classification

We work with organisations to deliver their ambitious production AI projects,
and we understand the need for speed, and for managing total cost of ownership.

Avoid getting stuck in "Proof of Concept" phase
In a recent survey of 1,093 industry professionals by Forbes, 64% of respondents
agree that the challenges of implementing AI have been underestimated. 61% of
the same group agree that many AI projects were having trouble getting beyond
Proof of Concept and prototyping stages.

Deliver production value, quickly and early
At Data Language, we have seen all the common issues in the design,
implementation and scaling of AI systems, and we can help you through this
process. We specialise in delivering valuable MVPs quickly and early.

Let's talk
Get in touch with us for a free assessment - we are here to help!

Delivering a Scalable AI MVP

Paul Wilton

"SCOPA" is the original name for our Explainable AI Platform that provides high throughput, scalable, explainable predictive analytics. 

Explainable AI Platform - SCOPA - for high volume specialized XAI

"SCOPA" is the original name for our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] that
provides high throughput, scalable, explainable predictive analytics.

SCOPA is now called Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] 
Our platform allows us to deploy a suite of layered deep learning, ML, and
algorithmic models, in an ensemble. The results of this can be reasoned-upon to
answer very subjective questions about the input data holistically.  Models and
algorithms are individually scalable, and each may identify or predict a
specific outcome, but collectively they can answer more holistic questions. The
orchestration of the models, data analysis and pipelining is contained and
managed within the Explainable AI Platform.

Explainable AI Platform Features
Why "Explainable AI"?
AI should not be a black box. To engender trust it is imperative that consumers
of machine learning predictive analytics understand how the AI reached its
decision.

Whether for decision support or as a decision maker, an AI application must be
able to explain itself and be accountable.

Explainable AI (XAI
[https://en.wikipedia.org/wiki/Explainable_artificial_intelligence]) is a
burgeoning field in machine learning that tackles this challenge, so we, as
ultimate end-users, have faith in machine intelligence and can strive for its
continuous improvement. Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] 
embraces explainability, providing insights into the decision making process of
the predictive output that it has made in answering questions of the data,
ultimately providing a fingerprint of the predictive qualities.

Scalable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
built for scalability. Every model or algorithm deployed into the platform is
containerised and individually scalable. Queuing and constraint theory allows us
to dynamically scale models at every step in a prediction pipeline based on the
performance and throughput required of that model.

Evolvable
Evolvability has been baked into the architecture as a 1st class concern
providing a high degree of potential for customisation and extension. This makes
it an extremely portable solution across diverse domains, and also allows the
predictive model architecture to evolve as the problem space changes over time.

Deployable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
deployable in cloud or on-premises, and can be used either on a fully managed
SaaS basis or as an enterprise self-managed deployment in your own
infrastructure. It also uses contemporary open-source architecture technologies,
with full deployment automation. It can even be deployed on laptops for
demonstrations or at-the-edge applications.

Use Cases
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
ideally suited to enterprise use-cases in a number of sectors, such as
specialised media classification, industrial maintenance, agri-tech, and
healthcare.

It has been deployed to classify terrorist and extremist propaganda imagery for
the security services and the Home Office in the UK at scale. 
Even for such a complex and multi-faceted challenge, a bare bones instance of it
scored remarkable metrics (over 0.95 precision, 0.85 recall) with plenty of room
for substantial improvement.

Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] can
be deployed for use cases in industry such as railway track environment analysis
, or for predictive maintenance.

In Agri-Tech is is suited to use-cases such as crop disease prediction, yield
prediction, or pest analysis using for example, drone footage, or spraying-boom
camera imagery.

In healthcare it can be used for novel image analysis such as bone fracture
detection. 

Please do get in touch if you would like to find out more.

SCOPA - scalable, explainable AI

Data Language's Text AI Services for Publishers provides automatic classification, entity extraction, relationship extraction, and recommendations.

Text AI Services for Publishers - Our Industry-leading SaaS

Our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] were
originally designed to meet the challenges of automated content workflows in the
publishing industry, and are ideally suited to AI classification and automated
tagging of news, sport and entertainment content.

Why do publishers tag their content?
Most large publishers need to tag or classify their content for a number of
reasons :

 * to assist internal processes and work-flows
 * to improve search and discovery
 * to provide richer content and user analytics
 * to drive advertising
 * to deliver richer user experiences

Typically tagging is a manual task, integrated into the editorial workflow and
the CMS. Increasingly, publishers are working to automate this process using NLP
and machine learning techniques.

Automated content tagging
Automating content tagging unlocks a lot more value, as the automated-tagging
system can be used to

 * improve tagging efficiency, consistency and quality on the news desks
 * fully-automate tagging of external news feeds
 * unlock value in long-tail content
 * automate tagging of other content around the organisation

For this reasons, publishers are investing large sums and time in building their
own automated tagging systems. However what may seem like a simple problem in
the R&D labs can turn into a much more complex and expensive problem, when put
into production.

How Data Language Text AI Services can help
O [https://datalanguage.com/tagmatic]ur Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are an AI
and Machine Learning fully managed SaaS product. We built them to make it as
simple as possible to integrate automated tagging and classification into your
publishing systems, addressing all the key challenges :

 1. It is incredibly easy to integrate. Send it content to learn. Send it
    content to tag.
 2. It uses your own dictionaries, taxonomies, and classification vocabularies
    out of the box.
 3. The engineering challenge of scaling, retraining, and efficiently
    redeploying the machine learning models is done for you.
 4. It learns on the fly. As new terms arise in news, our Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services] learn
    them quickly.

Total cost of ownership
Building you own AI based tagging solution is no mean feat. It is not just a
data science problem, but involves software engineering, testers, DevOps,
infrastructure, scaling automation, model deployment automation, continuous
machine learning model training automation, as well as the project management
and business processes that accompany a project of this nature. Both the total
cost of ownership and time-to-deliver is substantial.

With our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] you can be
up and running in hours, not months, at a fraction of the total cost and you can
put your data scientists to work on your market differentiating data outcomes
instead.

Get in touch
Use the contact form to get in touch - we are here to help.

News and product updates about our Text AI Services. Read about how our Text AI products are being used in innovation projects, and by our clients.

Read the latest on our Text AI Services. Projects, Innovation initiatives, and new developments for AI features in specialized use cases.

News and Product Updates about our Text AI Services

Text AI Services

Text AI Services for Publishers

Tolga Ergin

A great AI or ML model is worthless if it can't be delivered in a scalable, cost effective and technically sustainable way. Find out how.

Data Science at Scale - 10% AI, 90% Engineering

A great AI or ML model is worthless if it can't be delivered in a scalable, cost
effective and technically sustainable way. Our data science team excels in the
design and implementation of machine and deep learning models for the analysis
of data and media at scale.

Data Science seems to have something mystical about it, to the extent that
enterprises have almost come to consider it as a magical solution to impossible
problems. The reality is that behind DS there are tons of unfashionable data
engineering and analysis tasks, before statistical models can be applied to the
problem. 

Our team of talented experts here at Data Language can leverage years' worth of
technical excellence to ensure your AI solution can actually be scaled and
delivered at the highest standards of software engineering.

Understand your data 
Unlock the full potential of your data through early discovery, analysis, and
visualisation.

Learn fast
With so many variables to tweak, rapid feedback and short iteration cycles are
imperative to an effective delivery.

Models are software
As with any piece of code, statistical models also benefit from engineering best
practices for delivering at scale, with controlled total cost of ownership.

--

Read our white paper on rethinking how you do data science in your organisation:

 
[https://datalanguage.com/static/whitepapers/rethinking-data-science-in-the-enterprise.pdf]

Data Science at Scale

Resolute Square launches, using Data Graphs as a knowledge graph platform, combining CMS, VOD, DAM, SCV & MDM, complete with powerful APIs.

Resolute Square - launches with our knowledge graph SaaS

We are pleased to announce the launch of Resolute Square
[https://resolutesquare.com/?utm_source=datalanguage&utm_medium=newsPost&utm_campaign=launchPost]
, a pro-democracy media organization using Data Graphs [https://datagraphs.com/] 
to provide its knowledge graph -powered Linked Media Platform.

We will publish more detail about the technology in future posts.

Why did Resolute Square choose Data Graphs?
Firstly, Data Graphs enabled the team to combine CMS, VOD, DAM, SCV, MDM, live
streaming, and a knowledge graph to connect the media, all in one place. This
enabled the project to launch quickly, with an enterprise media publishing
platform, complete with powerful APIs, without the need to integrate 4+ vendor
solutions for this central purpose. Speed to Market!

Secondly, this approach enables the Resolute Square team to interlink its media
in order to deliver rich user experiences, and to innovate and pivot rapidly on
an API-first platform. This kind of capability is not available anywhere else in
the market in an integrated SaaS; projects normally need to pin together several
services to achieve this. 

The Knowledge Graph combines Single Customer View with Single Subject View
The domain model joins Creative Works with People, Places, Organisations, and
the Single Customer View "User" model.Another innovation in this project - Video
Streaming
The Resolute Square platform also includes an innovative ultra-low-latency
participatory live video streaming experience, provided by our friends at 
Perfogram
[https://perfogram.com/?utm_source=datalanguage&utm_medium=newsPost&utm_campaign=launchPost]
.

More soon!

Resolute Square Launches, powered by Data Graphs for speed to market

This video was aired on 6th May at the Knowledge Graph Conference 2022 in NYC, as part of the "Knowledge Graph Tools Track".

The 13-minute knowledge graph: Netflix, Disney, Amazon, Hulu

This video was aired today at the Knowledge Graph Conference 2022 in NYC, as
part of the "Tools Track".

This video was aired live at KGC2022We showed participants how they can create a
Knowledge Graph in 13 minutes using open Netflix, Disney+, AmazonPrimeVideo, and
Hulu data from Kaggle.

It ends with a graph path analysis of how Kevin Bacon connects with Sandra Oh,
via shows and casts, using the graph that we implemented in Data Graphs in 13
minutes.

NB - we had made some careful model design and data preparation before setting
up the knowledge graph.

We took our Knowledge Graphs as a Service product "Data Graphs" to The Knowledge Graphs Conference 2022, loaded with Netflix, AmazonPrimeVideo, Hulu and Disney+ data.

Knowledge Graph Conference 2022

The 13-minute knowledge graph, with Netflix, Disney+, AmazonPrimeVideo, and Hulu data

Find out about our feature at KGC2022, and explore this Netflix, Hulu, and Amazon Prime knowledge graph, using our product "Data Graphs".

Data Graphs at the Knowledge Graph Conference 2022

We've launched a read-only version of Data Graphs for participants at the
Knowledge Graph Conference 2022 to have a go, hands-on.

Data Graphs is our Knowledge Graphs as a Service product. It is used by global
brands to provide a knowledge graph platform to accelerate product
time-to-value.

Lowering the barrier to entry for Knowledge Graph Platforms
Often knowledge graphs are seen as a high-cost and high-risk strategy that only
the largest of companies can implement, over a very long time. 

With Data Graphs, we've ensured that businesses can see value rapidly.
Specifically, this means speeding up the ability to do proof of concepts, and
simple but informative demos that allow organizations to realise early value at
low cost and low risk. Here's an example of one of those demos right now.

13 mins to set up:
Netflix, Disney+, AmazonPrime, and Hulu
When developing Data Graphs, we wanted to make the setup and creation of a
knowledge graph, simple and quick. 

This video, prepared for KGC2022, shows how we created a knowledge graph,
complete with secure API and powerful UI tools, ready for production, in 13
minutes:

Try it yourself! - play with the Data Graphs demo here
[https://media-graph.datagraphs.io/media-demo/_all/concepts/?q=&pn=1&ps=15&sf=_label&sd=asc&layout=list&viewport=s000000000,1&utm_source=website&utm_medium=kgc-post&utm_campaign=faceted-search]
This demo version was created using Netflix, Disney+, AmazonPrime, and Hulu
data, and gives you a good view of just some of the key Data Graphs features.

1. Create your Domain Model for your Knowledge Graph
After you have designed and created your domain model with the business entities
and the relationships that link them together, Data Graphs generates a visual
representation of the model so that you can see clearly how the knowledge of
your business interconnects. 

Check out the Domain Model for Shows, Cast, Genres, Ratings here
[https://media-graph.datagraphs.io/media-demo/_domain-model/?utm_source=website&utm_medium=kgc-post&utm_campaign=domain-model]
.

2. Use our powerful Faceted Search over your knowledge graph
The search capabilities of Data Graphs are stellar and allow you to find and
explore your data precisely and most importantly, quickly. With the Advanced
Search feature, you can build complex queries by chaining and grouping a series
of boolean and numeric expressions, to pinpoint the targets of your search. 

Test the search capabilities of Data Graphs here, on 115k concepts including
shows, cast, directors, genres, countries, and more
[https://media-graph.datagraphs.io/media-demo/_all/concepts/?utm_source=website&utm_medium=kgc-post&utm_campaign=faceted_search]
.

As well as an intuitive visual search tool, all the Data Graphs search features
are available via API so you can integrate them with your business systems and
apps.

Explore the Data Graphs API here [https://support.datagraphs.io/].

3. Use the Graph Explorer to visualise your knowledge graph
The graph explorer is one of the star features of Data Graphs. You can now
visualise your knowledge graph in an interactive representation of your data,
that you can then explore and dive into, using the nodes. By visualising your
entire knowledge graph, from any concept in your dataset, we open up a wealth of
possibilities for your structured data. 

Interact with the knowledge graph explorer here, starting from the "Stranger
Things" node
[https://media-graph.datagraphs.io/media-demo/shows/concepts/urn:media-demo:TvShow:netflix-s3686/explore/?utm_source=website&utm_medium=kgc-post&utm_campaign=knowledge-graph-explorer]
.

4. Use the Graph Path Analysis to gain insights
Data Graphs lets you visually explore paths between any entities in the graph,
in order to find the shortest paths, and to specify constraints on both nodes
and properties that make up these paths. A key feature of knowledge graphs, this
type of analysis is typically used in compliance, fraud detection, and supply
chain analytics. 

You can try out the graph path analysis here
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?utm_source=website&utm_medium=kgc-post&utm_campaign=graph-path-analysis]
.

About the Data Graphs demo
The Data Graphs demo is a read-only view of the full version of Data Graphs
[https://datagraphs.com]. The complete product provides a full suite of tools
for curating, ingesting, editing, and maintaining data in a knowledge graph,
along with APIs for integration of your graph data with apps and business
systems.

About the Media Graph data
The media graph data has been sourced from the open data provided by Netflix,
Hulu, Amazon Prime, and Disney+ via kaggle.com here
[https://www.kaggle.com/datasets/shivamb/netflix-shows]. The data sets from
these 4 publishers have been cleaned, loaded, and linked together in our graph
to join 72k actors and directors across 21k  TV shows and movies, all faceted by
genre, rating, country, and year of production.

A knowledge graph is a collation of information and data typically within the domain of your business context. The information is stored in a database, often a graph database such that it can be queried, maintained, and interacted with by humans or machines. We have exceptional experience in the design, implementation and deployment of knowledge graphs. We have been working with graph databases since our inception and have delivered knowledge graphs solutions for global organisations.

We have exceptional experience in the design and implementation of knowledge graphs and have delivered knowledge graphs solutions for global organisations.

Knowledge Graphs - our Work, Features, News and Updates

Knowledge Graphs

Data Graphs at The Knowledge Graph Conference 2022

We are holding a team hack event this week with themes around blockchain, AI,
and knowledge graphs. We're solving some problems, and having fun with new tech.

A "Hack event" is a useful way of rapidly exploring, and testing new ideas. The
basic format uses a framed challenge and a time constraint.  Participants
usually "hack" in teams, and the emphasis is on creating working prototypes to
test hypotheses.

In stark contrast to a formal product delivery process, hack events participants
get to try any route without the need for production-grade code, or the
restriction of "business as usual" concerns. 

More often than not the prototypes created are not "the next unicorn"-type
projects, but this is ok! The point is to explore the problem spaces, try novel
approaches, and test new ideas efficiently. 

The ideas are tested in the context of our products, and concepts may make it
into our core product set.

Data Language Hack Event

Fabio Colasanti

We have recently completed some new "Inline Entity Extraction" benchmarks. This
is a sophisticated "Named Entity Recognition" (NER) product that we launched as
part of our Text AI Services suite in 2020
[https://datalanguage.com/news/inline-entity-extraction-text-ai-ner]. It is
optimized for Knowledge Graph platforms in high-value knowledge organizations.
It identifies entities inline within text media and attributes rich metadata.

The Problem: Automated Tagging in Custom Domains
Our customers are high-value media publishers, knowledge organizations, and DaaS
providers. 

 * A huge portion of their business asset value is locked in their unstructured
   mixed media assets.
 * To liberate this value, accurate inline text classification of 100s of 1,000s
   of entities is required to enable structured connection with bespoke
   information architecture models and/or knowledge graphs.
 * Generic off-the-shelf NLP SaaS solutions do not recognize these custom
   entities, so a mapping exercise is required, and is often a too low
   resolution to provide value.
 * Many NLP solutions do not provide the rich, domain model-aware metadata,
   because they are not trained for the custom domain.
 * Other custom NLP solutions require larger training sets and golden corpora
   that cover the entire entity set.
 * "Build it yourself" becomes very expensive, with a large data scientist and
   engineering overheads, while organizations find themselves re-solving the
   same challenges that we have already solved.
 * "Manage it yourself" has a surprisingly large data science and engineering
   overhead, to overcome production challenges that we have already solved.

Testing methodology
We trained our Inline Entity Extraction SaaS to understand around 180,000
entities from a custom domain.  We then benchmarked it in a controlled
environment where two teams of annotators were asked to annotate 500 documents:-

 * The first team used the old-fashioned method of manually annotating a
   document.
 * The second team moderated the output of the model that had already scanned
   the documents and picked up entities, only needing to confirm or reject
   pre-determined results.

Results: High F1 Score
Here’s how our Data Language Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] Inline
Entity Extraction SaaS performed:

 * Precision: 0.94
 * Recall: 0.90
 * F1: 0.92

We have proven we can train a highly accurate model, specialized for a custom
domain, that can cope with hundreds of thousands of entities with very
high-performing metrics, using a golden corpus that covers only a small subset
of the entities. 

Achieving such high results on the classification of such a high number of
classes is very rare for a model like this in the wild.

Results: Huge Workflow Efficiency Gains
The findings of this exercise were that the error rate of the team that used the
Data Language AI model to pre-annotate the documents was substantially lower
than the team that worked without the model. 

 * The team using "Data Language AI" demonstrated a 57% drop in error rate.
 * The team using "Data Language AI" demonstrated a 53% increase in annotation
   coverage.

The measurable benefits were clearly efficiency and accuracy and added to the
coverage of entities being substantially larger, it’s clear that using our model
was a great success.

Should we keep Humans in the loop, or not?
We were also analyzing whether there is benefit in keeping humans in the loop
and this result proved surprising. While it was clear that helping humans out in
entity extractions tasks by pre-running the content through a model could
significantly assist their operation, we actually found that the error rate of
the model was actually slightly higher when humans were involved than when the
results were totally automated. In this case, humans reviewing results actually
makes things slightly worse!

There are some cases where perfect results are not mission-critical - e.g.
upstream automated tagging to simplify downstream workflow, where "the machines
do the heavy lifting". In these cases a certain amount of error is acceptable
and a totally automated process will work. 

In other cases, where errors are less acceptable, the confidence and evidence
metadata in our SaaS can be used to allocate "less certain" predictions to SMEs
and expert analysts for decisions and feedback loop optimizations.

Comparison to other Text Analytics NER Approaches
So what does our competition offer to compete with our results in the Named
Entity Recognition (NER) space? 

Well, it’s difficult to compare apples to apples in this scenario, as our
competitors suffer from the same problem outlined above: Complex custom domains
with small training corpora.

This is the exact advantage of our SaaS that these benchmarks emphasize. 

To put these results into the context of standardized testing of NER performance
elsewhere in the wider world and industry, one usually finds that most tests of
F1 approach the value of 1, only where these models work by identifying a small
handful of classes. Have a look - you will likely see "People, Places,
Organizations, Events"... the usual suspects, and this problem itself has been
solved daily for the last 12 years by almost every computer science student.

The Benefits of Data Language Text AI Services
The fact that our model can cope with a far greater number of classes than most
other providers, and still see such impressive results, is a strong
differentiator.

Another advantage of our Data Language AI Services
[https://datalanguage.com/products/datalanguageai] that we offer is that they
work out of the box at scale for your production platforms.

Our Inline Entity Extraction SaaS comes with all these benefits immediately: 

 * Ability to classify text content to 100s of 1,000s of entities in a custom
   domain.
 * A rich API for interacting with the machine learning service metadata.
 * Automatic management of machine learning model ‘fallout’.
 * Self-optimisation of the machine learning models, on the fly.
 * Capability to cope with an extremely high number of entities.
 * The ability to scale up indefinitely.
 * Production-ready engineering, out of the box.

All the engineering is done, and the security & maintenance are on us!

AI Relationship Extraction for Knowledge Graph Platforms
Later in 2022, we will launch a new commercial Knowledge Graph Relationship
Extraction service as part of Text AI Services - early testing has impressive
benchmarks for high-value knowledge organizations. More to come soon.

For more information on Data Language AI Services, check out the links below.

Data Langauge AI [https://datalanguage.com/products/datalanguageai] | Text AI
Services [https://datalanguage.com/lp/text-ai-services]

New Benchmarks from our Named Entity Recognition (NER) Text AI SaaS Product

After a long history of working together on industry standards, Data Language
has become an official member of the IPTC.

The IPTC is the global standards body for news & media. They provide the
technical foundation for the news ecosystem and involved in setting schemas or
models for the exchange of content in news media. For example, this could be
deciding how the wires are delivered, working on metadata values, controlled
lists of values, and many other functions.

15 Years of Collaboration
Collectively at Data Language we've been working with IPTC on their standards
for the last 15 years, and have been actively involved at many stages. We were
involved in rNews [https://iptc.org/standards/rnews/] which was the schema.org
for news standards, and more recently we've been contributing to their sports
models. Silver Oliver, Data Language's Head of Information Architecture, has
been an active member of the sports model working group, developing those models
to discover what the next generation might be. 

Metadata Innovation in the UK publishing industry
So why did we decide to formalise our involvement with the IPTC? 

Their work is highly valuable in the industry and we want to solidify our
contribution to the innovation, maintenance, support and development of their
standards. We want to help define standards from a Data Language point of view,
and with that be able to encourage the adoption of those standards. As we are
one of the leading agencies supporting metadata efforts in the UK publishing
industry, and given our client expertise, this new relationship with the IPTC is
a great fit.

The opportunity to show our knowledge graph product Data Graphs
[https://datagraphs.com] as an example of good industry standards to other
members of the IPTC is another reason why we wanted to partner even more closely
with them. Having the prestige and quality reputation of the IPTC associated
with Data Language and our push to deliver brilliant knowledge graphs offers
great benefits. We are also always looking at new patterns and tooling in this
space so this membership is an opportunity to validate some of our thinking and
our toolsets against what the IPTC is identifying as the direction that they
think the industry should be going. It's  a really positive way for us to keep
in touch with how the industry itself talks about its needs and what trends are
likely to arise.

Find out more at the links below:

IPTC [https://iptc.org/]| Data Graphs [https://datagraphs.com/] | Knowledge
Graph Engineering [https://datalanguage.com/knowledge-graph-engineering]

Data Language Joins the IPTC

Janes is the latest in a group of high-value, knowledge-driven organisations to
develop their information backbone using rapid innovation in data management
with Data Language.

18th November 2021, London:  Data Language, the data innovation consultancy and
solution provider, today announces it is supporting the interconnection of Janes
120+ years of open-source defence intelligence to create Janes Intara, which
enables the integration and normalisation of disparate data sources, allowing
Janes customers to exploit all intelligence sources in an actionable, shareable
manner.

Data Language has worked as part of a joint team with Janes data and technology
experts to launch a new knowledge graph platform to power the Janes Intara
solution. The platform creates the foundations for ongoing and rapid innovation
of Janes foundational interconnected intelligence.

Ben Conklin, Chief Product Officer at Janes, comments: 

> “Our partnership with Data Language enabled us to augment our development of
Janes Intara, a solution that interconnects millions of assured data points
across Janes foundational intelligence, delivering contextualised insight to our
customers. Working with Data Language on our Knowledge Graph has been imperative
to accelerating the development of our first generation product, further
supporting our integration into customer workflows worldwide.”
Paul Wilton, CEO, Data Language, adds: 

> “The impact that Knowledge Graph and AI technologies can have on high-value
information organisations is significant in terms of both enterprise efficiency
and rapid innovation, enabling Janes to deliver state-of-the-art intelligence
data capability to their customers. Data Language is a global leader in
Knowledge Graph solution delivery, and we're delighted to be working with Janes
to develop their Intara platform.”
Data Language has unique expertise and years of experience working with large
organisations to devise and develop world-class data management and innovative
data products, including Knowledge Graph and AI-based tools.

> Pleased that our data and technology experts worked with Data Language to build
the knowledge graph for Janes Intara! Learn more here: https://t.co/RhAtUiVNdN 
https://t.co/ecWCtOQgVh

— Janes (@JanesINTEL) November 18, 2021
[https://twitter.com/JanesINTEL/status/1461351890243833859?ref_src=twsrc%5Etfw]
About Data Language

Data Language is a UK-based data innovation consultancy and solution provider
that has worked with organisations such as News UK, Syngenta and Cochrane to
deliver digital transformation solutions using knowledge graphs and AI. Learn
more at www.datalanguage.com [http://www.datalanguage.com/].

About Janes

The Janes stamp of trust means supplying our customers with timely, validated,
unbiased and relevant intelligence in a world crowded with increasingly
unreliable information. With an unmatched legacy of more than 120 years, Janes
has adapted, expanded, and developed its unique tradecraft while transitioning
from a traditional military publisher to the leading global agency for
open-source defence intelligence. Janes Intara interconnects millions of assured
data points across Janes foundational intelligence, with the ability to
integrate and contextualise multiple sources, delivering the single source of
truth. Learn more: www.janes.com/whatwedo

To find out more visir Data Language Knowledge Graphs
[https://datalanguage.com/knowledge-graph-engineering] | Janes Intara
[https://www.janes.com/intara-interconnected-intelligence]

Data Language & Janes: A Knowledge Graph Partnership

Get hands-on and try the Graph Explorer yourself. Explore the data and interconnections, and help everyone understand your knowledge graph.

Graph Explorer - To demonstrate your Knowledge Graph

The Graph Explorer feature on Data Graphs [https://datagraphs.com/] allows you
to navigate through your knowledge graph visually and interact with your data in
a new light. Being able to present your information visually in the Graph
Explorer helps provide the context, meaning, and interconnection of your data,
an essential asset to your digital transformation.

Demonstrate your Knowledge Graph
A clickable graph user interface enables a visual way of explaining how your
knowledge graph can work for your stakeholders and users. This approach enables
a clickable journey around your business data so that it brings the key
information to life. In our experience this approach generates that "lightbulb
moment" that you have been looking for!

Try it yourself on our Media Demo [https://media-graph.datagraphs.io/media-demo]
Click here [https://media-graph.datagraphs.io/media-demo] to try out our Media
Demo, a read-only version of our product made solely for exploration purposes so
that you can try out the amazing capabilities of the product with no strings
attached. This knowledge graph is populated by thousands of data points from
Netflix, Disney, Hulu, and Amazon and has a plethora of features available to
try out including Search
[https://media-graph.datagraphs.io/media-demo/_all/concepts], Advanced Search
[https://media-graph.datagraphs.io/media-demo/_all/concepts?aq=], Path Analysis
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search], and Domain
Model [https://media-graph.datagraphs.io/media-demo/_domain-model].

Kevin Bacon on Graph Explorer
Click here
[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore] 
to try out the exact same action being completed in the gif below - exploring
the all media data that links to Kevin Bacon... With Graph Explorer you can
navigate visually through his films, the countries of those films, other actors
he's worked with and much more.

[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore]
What's the shortest path between Sandra Oh and Ricky Gervais?
Click here
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?from=Sandra+Oh%28urn%3Amedia-demo%3APerson%3A6XCMzX4fttIARGWlEPynkN%29&to=Ricky+Gervais%28urn%3Amedia-demo%3APerson%3A2AafOAewdEpIBHxVTKAOHg%29&constraints=0%7Bi%5Byear%3Ar%3A1990%3A2020%5D-%3Ecast-%3E*%7D&view=graph?utm_source=socials&utm_medium=twitter&utm_campaign=shortest-path] 
to complete the action below - finding the shortest path between Sandra Oh and
Ricky Gervais, with path constraints on other actors and shows between 1990 and
2020, and then clicking the Graph Explorer button to visualise the path.

[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?from=Sandra+Oh%28urn%3Amedia-demo%3APerson%3A6XCMzX4fttIARGWlEPynkN%29&to=Ricky+Gervais%28urn%3Amedia-demo%3APerson%3A2AafOAewdEpIBHxVTKAOHg%29&constraints=0%7Bi%5Byear%3Ar%3A1990%3A2020%5D-%3Ecast-%3E*%7D&view=graph?utm_source=socials&utm_medium=twitter&utm_campaign=shortest-path]
And this entire knowledge graph can be created in just 13 minutes in Data
Graphs... Watch this video to find out how.

Find out more about Data Graphs here [https://datagraphs.com/].

Graph Explorer - Demonstrate your Knowledge Graph

Data Language announces promising results from this Innovate UK -funded AI
project, building on their long-running technology partnership with Cochrane. 

Innovate UK Project
Title: “AI Classification of COVID-19 studies to ensure clinical guidance and
healthcare policy is informed by current meta-analysis of all available evidence
”

This Innovate UK grant-funded project ran from August 2020 to April 2021.

Data Language has worked with Cochrane since 2014, designing and delivering a
microservice-based linked data platform that encodes an authoritative knowledge
graph of healthcare clinical evidence. This has included work to refine, iterate
and scale the platform whilst creating a suite of applications to publish
evidence in healthcare as structured data. The platform enabled Cochrane to
rapidly deploy their Covid 19 Study Register in response to the coronavirus
pandemic in 3 weeks last March.

This Innovate UK -funded AI Project has delivered a new capability in Data
Language’s Text AI services to enable automated creation (“clinical coding”) of
PICO Graphs, using values from Cochrane vocabularies and standard medical
vocabularies assigned to Population, Intervention, Comparison and Outcome
structures for clinical analyses.

The specific outcomes from the initial phase of project work can be summarised
as:

 1. We created a working Machine Learning approach.
 2. We have a clear set of next steps.

Following the successful conclusion of the project work to develop the AI
components, the next steps are to focus on key optimizations to move it into
productionization and commercialization. Timeframes are yet TBC.

Results from our Text AI Services Covid-19 Project with Cochrane

Our knowledge graph product Data Graphs [https://datagraphs.com/] has a brand
new feature. You can now view your entire knowledge graph in the 'Domain Model'
view. This allows you to see your data presented as a spider diagram and explore
it interactively by moving each data point to wherever you like. 

Being able to present your data visually is an essential facet in your company's
digitisation journey and this new Data Graphs feature makes this simple.

Find out more about Data Graphs and Data Visualisation here
[https://datagraphs.com/].

Domain Modelling

New Data Graphs Feature: 'Domain Model' View

Data Language is proud to be featured in today's 'AI for Business' supplement in
The Times (10th December 2020)

Created by Raconteur, this supplement covers a range of AI solutions across a
spectrum of enterprise use cases. In it we offer our view on how to implement AI
successfully: 

 1. You need a consistent source of reliable data to feed the AI systems.
 2. You need leadership within the organisation who understand the need to make
    faster, data-driven decisions.

The Times : "AI for Business" - 10th December 2020.Read more:
 1. Raconteur AI Section
    [https://www.raconteur.net/topic/technology/artificial-intelligence/]
 2. Data Language Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]
 3. Data Language Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]

Data Language featured in The Times' AI for Business Supplement

Data Graphs is our brand new a structured data solution that makes creating, managing and organising your data easy. Start your free trial now.

Launching Data Graphs | Our new knowledge graph product

We are very proud to announce the launch of our latest SaaS product - Data
Graphs! We wanted to create a data solution that could provide really easy
structured data management and we believe we've done that with this new
knowledge graph product.

As a fully managed SaaS solution, Data Graphs [https://datagraphs.com/] combines
an intuitive user experience with flexible integration. It allows a team to
curate a knowledge graph for any domain, from simple flat concept collections to
complex structured domain data. This is all wrapped up in an extremely intuitive
user experience, combined with a fully comprehensive API and web-hooks to give
you extremely efficient integration with applications and business systems. 

After working for years with many companies on structured data solutions, we are
thrilled to have now produced a product that can provide businesses with the
structured data they need to embark on their digitisation journey.

Here's how you can explore Data Graphs [https://datagraphs.com/]:

Read more [https://datagraphs.com/] | Sign up for a free trial
[https://datagraphs.com/] | Book a 15-minute discovery call
[https://calendly.com/datalanguage/data-graphs-intro-call]

Launching Data Graphs

Data Language AI Logo on Dark

This platform enables explainable AI and scales out of the box to meet your needs and evolve rapidly. Use your own terms, and move quickly to scale.

Explainable AI Platform - Data Language AI

Our specialist Explainable AI Platform orchestrates suites of predictive
analytics models to answer complex questions in computer vision, media
classification and data analysis.

It uses ‘layers’ of ensemble machine learning models to make these deductions.
For example to predict the likelihood of:

 * a social video being extremist in nature, or
 * a certain movie script or video meeting a relevant film classification.

Our platform is a response to the expensive-to-produce, expensive-to-maintain,
difficult to maintain selection of current products on the market, which
resemble ‘black boxes’ with limited transparency as to the predictions made…

By contrast, our Explainable AI platform is extremely scalable – and the
insights into how predictions are made – are clear!

We are here to help
Simply use the contact form to reach us - we would love to hear about your
project!

Explainable AI Platform

Data Language AI is our suite of intuitive AI products that generic SaaS AI services cannot beat. It is specialized, scalable and evolvable.

Data Language AI - Artificial Intelligence Engineered

Our AI Services
 1. Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]
 2. Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]

Market Leading AI for Digital Transformation
Artificial Intelligence prediction and automation tools are now important
components of your digital transformation journey.

The team at Data Language have developed market-leading AI services to support
your business.

Data Language AI Strengths
 1. Specialized
 2. Scalable
 3. Evolvable

Data Language AI Suite

Data Language AI Strapline

Data Language AI Logo

We are developing our Text AI Media Classification Products to help Cochrane automatically classify Clinical Studies onto Snomed-CT and PICO structured data

AI Classification of Clinical Studies with Text AI to fight Covid-19

We've just received the green light on this public-funded innovation project,
bringing two areas of significant Data Language expertise into play to help
combat Covid-19.

The mission combines two of our favourite things:
1. Clinical Evidence Knowledge Graphs
We've been working with Cochrane on their knowledge graph and data strategy
[https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19] 
since we started the project with them in 2014. As part of this, we recently
helped them launch their Covid-19 Study Register
[https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
, which summarises the current state of Covid-19 clinical evidence from around
the world that has been released into the public domain via authoritative trial
registries and journal databases.

2. Our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] for
automated text classification
Over that time, we have also cemented our position as industry leaders in
machine learning text classification and analytics, and have developed
particular expertise in high volatility domains (e.g. academic research,
breaking news, R&D) and complex ML engineering challenges. This led us to
creating our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services].
[https://datalanguage.com/tagmatic-ai-document-classification]

Mission: to ensure Covid-19 clinical decision making is informed by the latest,
most comprehensive reliable evidence possible.
With the help of the UKRI [https://www.ukri.org/], Digital Catapult's Machine
Intelligence Garage
[https://www.digicatapult.org.uk/for-startups/acceleration-programmes/machine-intelligence-garage]
, and Innovate UK [https://www.gov.uk/government/organisations/innovate-uk], we
are now bringing our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] into play
with our subject matter expert partner Cochrane to help the fight against
Covid-19.

We will be launching this new HealthTech AI beta before December. 

Get in touch if you want to know more.

Automated Classification of Healthcare Evidence

We are proud to announce the new member of our Text AI Services
[https://datalanguage.ghost.io/products/datalanguageai/text-ai-services] family:
Inline Entity Extraction. This new AI NLP service was created for high-value
knowledge organizations who want to accurately classify their text media at a
granular, inline-level.

The Problem: Automated Tagging in Custom Domains
We created this service to solve the problems that other Named Entity
Recognition (NER) NLP SaaS - and "build it yourself" approaches - could not
solve.

Our customers are high-value media and knowledge organizations. A huge portion
of their business asset value is locked in their unstructured media assets,
which they must leverage to add value to their subscription, DaaS, targeted
advertising, and other business models. This unstructured content must be
connected to their custom business domain via knowledge graphs and other
structured data systems:-

 * Complexity: To liberate this value, accurate inline text classification of 
   100s of 1,000s of entities is required to enable structured connection with
   bespoke information architecture models and/or knowledge graphs.
 * Custom Domain: Generic off-the-shelf NLP SaaS solutions do not recognize
   these custom entities, so a mapping exercise is required, and is often a too
   low resolution to provide value.
 * Rich Metadata: Other NLP solutions do not provide the rich, domain
   model-aware metadata, because they are not trained for the custom domain.
 * Small Training Sets: Other custom NLP solutions require larger training sets
   and golden corpora that cover the entire target entity set.
 * Cost of Build: "Build it yourself" becomes very expensive, with a large data
   scientist and engineering overheads, while organizations find themselves
   re-solving the same challenges that we have already solved.
 * Cost of Maintenance: "Manage it yourself" has a surprisingly large data
   science and engineering overhead, to overcome production challenges that we
   have already solved.

Our Solution: Text AI Services Inline Entity Extraction
Our Inline Entity Extraction SaaS handles a far greater number of classes than
other NER NLP solutions, while delivering impressive performance results. It
also works out of the box at scale for your production platforms.

Our Inline Entity Extraction SaaS comes with all these benefits immediately: 

 * Custom Domains at Scale: It classifies text content to 100s of 1,000s of
   entities in a custom domain.
 * Powerful API: It provides a rich API for interacting with the machine
   learning service metadata.
 * Does its own ML housekeeping: It automatically manages machine learning model
   ‘fallout’.
 * Self-Optimizing: It self-optimizes its machine learning models, on the fly.
 * Scalable: It is able to scale up indefinitely.
 * Production-Ready: It is Production-ready, out of the box.

All the engineering is done, and the security & maintenance are on us!

For more information on Data Language AI Services, check out the links below.

Data Langauge AI [https://datalanguage.com/products/datalanguageai] | Text AI
Services [https://datalanguage.com/lp/text-ai-services]

Product Launch: Inline Entity Extraction SaaS - AI Custom Domain Auto-Tagging

Analysis of 1 month of video stats from Jamie Oliver Christmas videos shows 10x CTR on Data Moments, and extremely high engagement rates.

Data Moments shows benchmark-beating stats on Jamie Oliver videos

Data Moments is a new type of video experience, where users can interact with
moments in video. For Jamie Oliver, this means getting more from each step in a
recipe video, and highlighting products that are used in the video so that
viewers can buy them directly.

This image shows a "Making thyme flavoured oil" video moment, that had an
interaction rate of 62%.From a data sample of 1 month of Data Moments on Jamie Oliver video
[https://datalanguage.com/news/jamie-oliver-christmas-videos-enhanced-with-luminery]
, here are some highlights:

Video Moment Engagement
 * Challenge: Video experiences haven't changed for years. Can we get a new,
   user-centred experience that dramatically improves how viewers use Jamie
   Oliver videos?
 * Solution: Identify steps in the recipe, and the techniques, products and
   ingredients in those steps. Activate the steps as structured "How To" moments
   using Luminery. Add important products so that users can find them directly.
 * Results: 92 new "moment interactions" per 100 video views. 9.3% Interaction
   Rate with HowTo steps (compared to the current ZERO).

Video Marketing Conversion Performance
 * Challenge: Jamie Oliver produces a range of products, and creates incredible
   recipe videos. How do we drive better product sales from these videos?
 * Solution: Identify products in the recipe and activate the moments to help
   users directly reach and buy the products at the right moments using Data
   Moments (saving huge conversion fall-off due to searching and uncertainty
   barriers).
 * Results: 5.4% CTR (clicks as % of video views) generated by the "product
   moments" (compared to 0.5% of standard pre-roll Ads). 9.6% conversion rate at
   the Ecommerce vendor destination for Jamie Oliver products.

What does this tell us?
This demonstrates, again, the benefit of using structured video moments to
activate new value in Video. We've demonstrated new engagement and revenue
-driving opportunities, and a fresh model for video publishing.

News, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video. Brought to you from the team that was foundational in the earliest large scale linked data projects in the UK.

Read news, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video.

Data Moments - News and Video Product Updates

Data Moments

Data Moments - Get the latest on this Video Data Moment Product

Data Moment video Stats from Jamie Oliver Christmas videos

Who is Cochrane?
Cochrane [https://www.cochrane.org/] is the global not-for-profit organisation
whose core mission is to put evidence at the heart of health decision-making all
over the world. Over 90% of WHO guidelines are informed by Cochrane’s systematic
reviews.

What is the Covid-19 Study Register?
The Register is a precisely scoped, authoritative source of the latest clinical
evidence from around the world concerning the Covid-19 pandemic, that is being
used to inform point-of-care clinical decision making and evolving healthcare
policy internationally.

Healthcare professionals can use it to locate all relevant, reliable clinical
evidence about specific healthcare questions from the latest Randomised
Controlled Trial (RCT), observational, modelling and other studies to support
the fight against Covid-19.

This crucial, trusted resource is essential at a time when the evidence base is
developing rapidly, as healthcare professionals require structured and reliable
up-to-date information.

Partners with Cochrane since 2014
We first worked with Cochrane in 2014 on a digital transformation project,
designing and delivering a radical new platform architecture centred around
linked-data and knowledge graph technologies. Since then we have worked to
refine, iterate and scale their platform to create a suite of applications to
publish evidence in healthcare using structured data.

Solid data architecture makes rapid innovation possible
This Covid-19 Studies Register is the latest example of how Cochrane is able to
innovate rapidly on the scalable knowledge graph platform architecture delivered
by Data Language. The structure and flexibility means that new use cases can be
delivered rapidly and efficiently.

See the Covid-19 Study Register here [https://covid-19.cochrane.org/].

Find out more
 * Cochrane's Covid-19 Resources and News
   [https://www.cochrane.org/coronavirus-covid-19-cochrane-resources-and-news] 
   (page updated regularly).
 * More about Cochrane and the rapid delivery of the Covid-19 Study Register
   project
   [https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
   .
 * Our work with Cochrane since 2014 - Information Architecture, PICO Ontology
   and more
   [https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19]
   .
 * More about our Knowledge Graph Engineering
   [https://datalanguage.com/knowledge-graph-engineering].

Launching the Cochrane Covid-19 Study Register

Our pitch included using our Explainable AI Platform to provide London's analysts with explainable AI (XAI) decision support.

Explainable AI Platform at London Civic Innovation Challenge

We've just been at City Hall for London's Civic Innovation Challenge Awards
Ceremony. Sadly we didn't win on this occasion, but we were proud to be in the
finalists for the Counter Violent Extremism Social Tech Challenge.

Explainable AI Social Tech to help Londoners
Our pitch included using our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] to
provide London's analysts with explainable AI (XAI) decision support.

Explainable AI decision support - in this case for violent extremism specialists
Thanks to the GLA Team and Social Tech Trust!
The finalists with the GLA team and Social Tech Trust.

News and Product Updates about our Explainable AI Platform (XAI)

Explainable AI (XAI) means that the process, reasoning and decisions made by the AI are transparent and clear to the users and teams involved. Even before this became a trending technical jargon term, we were delivering Explainable AI products and services.

Updates and news about our Explainable AI Platform (XAI). We are specialised in moving new AI services to production rapidly, avoiding pain points in XAI

News and Product Updates about our Explainable AI Platform (XAI)

Proud Finalists in London's 2020 Civic Innovation Challenge

Data Language chosen for the Scaleup Programme.
We are delighted to announce that we have been chosen for the Innovate UK and
EEN Scaleup Programme. This programme supports a select group of companies on a
journey to exponential growth, providing them with countless opportunities with
bespoke, fully-funded one-to-one support.

We are immensely proud of this achievement as admission onto the Scaleup
Programme is a highly selective process. For the first cohort 2,800 companies
made up the initial long list of applications with only 30 selected, so to be
chosen as one of the successful companies in the new cohort has given us even
greater confidence in our ambition.

We can’t wait to get to work with the Scaleup team to start bursting through
barriers to growth and taking Data Language to its full potential.

Scaling Up

The Jamie Oliver team is using Data Moments - our structured data video moment SaaS - to create new "How To", promotion and Product promotion video moments.

Jamie Oliver uses Data Moments for Christmas Videos

We're really pleased to announce the launch of the Jamie Oliver Christmas "How
To" video feature, powered by Data Moments
[https://datalanguage.com/products/datamoments], our SaaS structured data video
moments platform.

Jamie Oliver is using our platform to activate and enhance the valuable moments
within his Christmas videos.

Data Moments contextual commerce "Product Moments"
A product promotion "moment" on Jamie Oliver's Christmas Video Feature.Data
Moments "How To" video moments.
The Jamie Oliver team are interested in whether users used videos "to cook
along" - we are using our HowTo Video Moments to help users navigate between
important steps.

Jamie Oliver uses Data Moments

In September 2019, we enhanced Rugby World Cup video using our Data Moments product to activate their video. 50% of their key moments were in Google top 10

Video Micro-Moment Performance in Rugby World Cup 2019

Data Moments [https://datalanguage.com/products/datamoments] is a new type of
video experience, where users can interact with moments in video. These are
unique opportunities we call Video Micro-Moments, where we can use Data Moments
to meet user needs
[https://datalanguage.com/video-micro-moments-meet-user-needs-at-their-strongest] 
in the actual moment that they occur.

For the Rugby World Cup in 2019, this means enabling rugby fans to discover,
navigate and enjoy the great sporting video moments with ease.

Video Moment Reach
 * Challenge: J SPORTS have unique sporting moments within their videos, that
   cannot be discovered by search engines or users with normal video publishing.
 * Solution: Markup the key sporting events, teams and stadiums with Data
   Moments [https://datalanguage.com/products/datamoments] structured data video
   key moments that are optimised for SERPs (Video SEO).
 * Results: 45% of video moments created for J SPORTS Rugby World Cup ranked in
   the first page of Google SERPs (Search Engine Results Pages). This created a
   completely new and significant SEO surface area, growing reach by 37%, and
   creating Google Discover opportunities too (with 4.2% CTR), 

Video Marketing Conversion Performance
 * Challenge: J SPORTS run an E-Commerce store selling sporting goods, plus a
    Sports SVOD product, and want to increase the traffic from video highlights
   through to conversions.
 * Solution: Create "Buy in the moment" contextual commerce moments, linking
   directly to products at key video moments, meeting the user's "I want to buy"
   video micro-moment need.
 * Results: 1.1% CTR - significantly beating the 0.5% standard for conventional
   video advertising.

Read the latest news and updates about Data Moments video key moments
[https://datalanguage.com/about/datamoments].

Data Moments Stats - Video Key Moments in the Rugby World Cup

J SPORTS is using Data Moments - our video key moment SaaS product - to activate video key sporting moments and brand moments for the Rugby World Cup.

We're really pleased to announce that J SPORTS is using Data Moments
[https://datalanguage.com/products/datamoments] (our SaaS Video Enhancement
Platform) to enhance video moments for their Rugby World Cup highlights.

We have been working on this project with our innovation partners at EAI
[https://www.entertainmentai.co.uk/] to help J SPORTS use our innovative video
moment enhancement technology for SVOD, E-commerce and Sport Event micro-moment
promotions.

Why is J SPORTS using Luminery?
J SPORTS were particularly excited about the Data Moments
[https://datalanguage.com/products/datamoments] activation of video moments that
effectively meet user needs exactly when they arise. Google's marketing
evangelists call these moments "Micro moments
[https://www.thinkwithgoogle.com/marketing-resources/micro-moments/micro-moments-understand-new-consumer-behavior/]
".

Sporting Event Micro-Moments - "I want to know"
With Data Moments [https://datalanguage.com/products/datamoments], each sporting
moment - tries, conversions, line-outs, penalties etc - has a new opportunity
listed on SERPS, shareable, and brand activated.E-Commerce Micro-Moments - "I
want to buy"
With Data Moments [https://datalanguage.com/products/datamoments], each brand
activated moment can feature Contextual Commerce activated promotions for
products, when they are actually relevant to the user!J SPORTS Enhanced Video
Homepage
At: video.jsports.co.jp [https://video.jsports.co.jp] - the videos are only
available for users in Japan, due to RWC rights restrictions.

The J SPORTS With Data Moments [https://datalanguage.com/products/datamoments] 
Video HomepageGet in touch [https://datalanguage.com/contact-us] if you want to find out more.

Data Moments activates video moments for The Rugby World Cup

Tom Fox from the "Hi Growth Unit" at KPMG awarded us with the KPMG Prize at
Venturefest in Oxford for our contextual video moment innovation pitch.

(Update: Luminery has rebranded as "Data Moments
[https://datalanguage.com/products/datamoments]")

Selected from an impressive spread of startups 
The pitching participants.Luminery Demo
The team demonstrate Luminery enhanced video moments at VenturefestBranding
Update @ 2020
"Luminery" has been re-branded as "Data Moments
[https://datalanguage.com/products/datamoments]".

News and Product updates about Luminery, our state-of-the-art Video Micro-Moment SaaS product. Luminery enables Publishers, Brands and Creators to leverage the value of video micro-moments for increased reach, revenue and engagement.

Read News and Product updates about Luminery - the Video Micro-moment SaaS Platform that increases video reach, engagement, revenue and social capabilities.

Luminery - The Video Micro-Moment Platform - News & Product Updates

Luminery

Luminery wins KPMG Prize at VentureFest

Data Language has been accepted onto the Digital Catapult Machine Intelligence
Garage [https://www.digicatapult.org.uk/projects/machine-intelligence-garage] 
program.

This is fantastic news for us. Through MI Garage, Digital Catapult is going to
provide us with plenty of high performance infrastructure and technical support
to power our machine learning and AI product R&D throughout 2019.

This will allow us to rapidly advance innovation and development across all our
products, running our ML models at scale, and on GPU kit. It will enable us to
expand our Tagmatic [https://datalanguage.com/tagmatic] ML product suite for
automated content classification; our deep learning image classification
platform, SCOPA; and help with automation on our Contextual Commerce platform, 
Luminery
[https://datalanguage.ghost.io/2019/02/06/digital-catapult-machine-intelligence-garage/luminery.com]
.

2019 is going to be a great year for Data Language!

AI is a core discipline at Data Language. We have an expert team skilled in machine learning and deep learning, and importantly we also have the engineering and deployment skills to deliver AI at scale for your production systems.

AI is a core discipline at Data Language: Our expert team is skilled in machine learning and the engineering skills needed to deliver AI at scale.

Artificial Intelligence delivered at scale - our core discipline.

Artificial Intelligence

Digital Catapult - Machine Intelligence Garage

We had some more great news. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk] has awarded Data
Language a new innovation grant for further funding experimental development and
proving the market of our Luminery [https://luminery.com] platform to deliver
crowd and machine sourced contextual commerce- and brand enhanced- video
moments. 

Our aims are to drastically improve the advertising revenue publishers can make
from video, to improve discovery and reach at a video moment -level, and also to
provide a fair reward mechanism for crowd workers.

You can see a working prototype [https://luminery.com/food-example] here
demonstrating how our contextual commerce works.

Innovate UK

Innovate UK funding for Luminery (Data Moments)

As part of our Text AI Services SaaS product range
[https://datalanguage.ghost.io/products/datalanguageai/text-ai-services], we are
proud to announce the launch of our cutting-edge Text Classification service.

The Problem: Automated Tagging in Custom Domains
We created this service to solve the problems that other NLP Text Classification
products could not solve.

Our customers are high-value media organizations. A huge portion of their
business asset value is locked in their unstructured text content:-

 * Aboutness: To liberate this value, an accurate classification of content
   "Aboutness" is required to classify content to the organization's bespoke
   information architecture models and/or knowledge graphs.
 * Custom Domain: Generic off-the-shelf NLP SaaS solutions do not recognize
   these custom aboutness classifications.
 * Rich Metadata: Other NLP solutions do not provide the rich metadata, because
   they are not trained for the custom domain.
 * Rapid Learning: Other NLP solutions can't learn on the fly, and require
   expensive and time-consuming training cycles to learn new concepts.
 * Cost of Build: "Build it yourself" becomes surprisingly expensive, with a
   large data scientist and engineering overheads, and organizations find
   themselves re-solving the same unforeseen challenges that we have already
   solved.
 * Cost of Maintenance: "Manage it yourself" has a surprisingly large data
   science and engineering overhead, to overcome production challenges that we
   have already solved.

Our Solution: Text Classification AI
Our Text Classification SaaS trains itself and learns your team's in-house style
while delivering impressive performance results. It also works out of the box at
scale for your production platforms.

Our Text Classification SaaS comes with all these benefits immediately: 

 * Customized to your Domain: It classifies text content to 100s of 1,000s of
   entities in a custom domain.
 * Powerful API: It provides a rich API for interacting with the machine
   learning service metadata.
 * Does its own ML housekeeping: It automatically manages the machine learning
   model ‘fallout’.
 * Self-Optimizing: It self-optimizes its machine learning models, on the fly.
 * Scalable: It is able to scale up indefinitely.
 * Follows your in-house style: It learns the tagging style of your SME team.
 * Production-Ready: It is Production-ready, out of the box.

All the engineering is done, and the security & maintenance are on us!

Data Language are specialists in AI Text Classification. This includes automated tagging, machine learning, language agnostic systems, self-optimisation, and specialist text classification areas.

Read about products and projects in AI Text Classification at Data Language. This includes automated tagging, machine learning, language agnostic and specialist text classification areas.

AI Text Classification - News, Technology and Product Updates

AI Text Classification

Data Language AI: Text Classification SaaS

Tagmatic is a service for automatically tagging your content.

It uses sophisticated machine learning techniques to learn how you tag your
content on-the-fly and then will tag new content automatically.

Tagmatic uses your own tag dictionaries. Whether you tag with simple text terms,
identifiers, taxonomies, or linked data, Tagmatic will tag with those. 

There is no data mapping exercise needed, just send your content & tags to
Tagmatic and it gets to work. Tagmatic quickly learns new tags too.

It is incredibly easy to integrate into your CMS or editorial workflows via two
simple web service interfaces.

If you are looking for an automated tagging solution, then look no further.

If you are developing your own automated tagging solution, then pause, give
Tagmatic a try - and be up and running quickly.

Update @ 2020: 
Tagmatic has been renamed as "Text AI Services: Text Classification
[https://datalanguage.com/products/datalanguageai/text-ai-services]" as part of
our "Data Language AI" product suite
[https://datalanguage.com/products/datalanguageai].

News, Blog Posts and Innovation updates about Tagmatic - our sophisticated AI automated content classification SaaS product. We have taken care of all the engineering and data science for you: Tagmatic learns on the fly, is self-optimising, and language-agnostic.

Tagmatic is an AI automated text classification SaaS product by Data Language. It learns on the fly, is self-optimising, and language-agnostic.

Tagmatic - News and Product Updates about our Text Classification AI

Tagmatic

Introducing Tagmatic

Our Text AI Services solve the common AI SaaS pain points: they are specialized to you, language agnostic, self-optimizing, and immediately scalable.

Text AI Services - Data Language AI

Our Text AI services use machine learning to make tagging, entity recognition,
auto classification and content recommendations both easy and efficient. 

They dramatically reduce the time, cost and complexity of organising your text
content. The data science, difficult engineering and scaling are all taken care
of.

Our Text AI classifiers and entity extractors are enterprise grade, state of the
art machine learning, delivering best-in-class capability.

SaaS Services
These SaaS services come with powerful APIs for immediate integration:

 1. AI Text Classification - classify your content using your taxonomies
 2. AI Entity Extraction - document level and inline
 3. AI Entity Relation Extraction - find relationships between entities inline
 4. AI Recommendations - fully automated content recommendations

Advantages
The real advantage with our Text AI services, is that they dynamically spin up
new models as they are needed, without the need for any data scientist. 

As fully managed SaaS products, our Text AI services are:

 1. Easy to integrate
 2. Language agnostic
 3. Use your own vocabularies
 4. Self-optimise on the fly, independent from your workflow
 5. Highly scalable, highly performant
 6. Automated on-the-fly training 
 7. Minimum setup time

Text AI Services combine machine learning and AI to provide world-class text classiciation. Self-optimising, language agnostic and full managed.

Launching Text AI Services | Efficient Text Classification

We have just launched our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services], a fully
managed, SaaS-based set of text services using AI and Machine Learning to
automate key processes. As essential tools in any digital transformation
journey, our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are the next
generation in NLP and text analytics.

With decades of experience in understanding and solving pain points in
structured data, we have now developed a world-class service for ai text
classification. Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are perfect
for out of the box machine learning to make your text tlassification and
recommendations more logical, accurate and efficient.

Find out more about Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] | Book an
intro call with the Text AI Team
[https://calendly.com/datalanguage/data-language-ai-intro-call]

Launching Text AI Services

We had some fantastic news this week. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk], part of the UK
Government Technology Strategy Board has awarded Data Language an innovation
grant to fund the development of a platform to deliver crowd and machine sourced
contextual commerce and structerd data on video moments.

Our aim is to structurally enhance videos at a moment level, which will then
improve the revenue publishers can make from video. The approach will also
provide a fair reward mechanism for crowd workers.

Watch this space!

Data Language featured in The Times' AI for Business Supplement

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