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AI Solutions for the Digital Workplace

Creating an intuitive workplace for a digital world

Personnel Changes, Siloed Data, and Digitization of Systems

Whether seated at a desk at the office or comfortably on a couch at home, laptops, tablets, and smartphones have created a virtual space where we can perform our work-related responsibilities from almost anywhere. The traditional concept of a “workplace” is becoming increasingly outdated in our digitally connected world.

The digital workplace needs to be accessible and agile, reliable and trustworthy, intuitive and efficient. That being said, digitalization of an organization – especially a large one that has massive amounts of legacy data and content – is extremely difficult. As the professional landscape changes, companies are seeking out advanced technologies to help them navigate the challenges they contintuously experience.

Semantic AI is the answer, where through knowledge graphs and a “semantic layer,” semantic capabilities can give organizations the framework to optimize workplace structures and processes, thus improving the employee experience and quality of service to customers. The semantic layer can also be used in the context of Generative AI and large language models, improving their output and making them more reliable.

Semantics can transform inconsistent data into a foundation for intelligent applications – all while maintaining the existing infrastructure of the organization so that they do not have to worry about making drastic tool changes.

Built on knowledge graphs, PoolParty has a number of examples of AI solutions for the digital workplace, which can be found below.

The common challenges when crafting a digital workplace

When things are not being linked together, whether through synonyms on a linguistic level or through repositories for data and content management, they simply just sit.  There is no enrichment, there is nothing to learn from, and the potential of these objects are wasted because they’re still in a rather basic stage.

This happens with …

    • Data silos: Critical information is spread and sectioned off into too many places 
    • Multilinguality: No universal or central vocabulary is being used across a database 
    • Data with no knowledge: Different data points and business objects are not being connected to generate valuable insights or 
    • Digitization: Manual and paper-based processes are transformed from analog to digital 
    • Knowledge retention: Changes in human resources impact how and what knowledge is shared and captured

The semantic layer as a basis for AI solutions

A semantic layer sits between the raw data of an organization and all their front-end applications. With a knowledge graph at its core, a semantic layer aggregates the data and creates a unified view that is further supplemented with enriched semantic metadata. The Knowledge Graph models the company’s domain, providing a structure and common interface for all of data and enables the creation of smart relations throughout the databases. For perspective, the graphic below shows a simplified version of a knowledge graph which connects various objects together in a logical way.

An example of a graph database in an easy-to-understand knowledge graph of the Mona Lisa.

 Graphic from Yashu Seth

On the business side, an enterprise knowledge graph contains business objects and topics that are linked, classified, semantically enriched, and connected to existing data and documents.

A semantic layer, which encompasses knowledge graphs, semantic tagging, text mining, and semantic search, helps map information needs and often complex queries to the required information appropriately. These technologies guarantee that all users will always have the most up-to-date and relevant information because it will act as the single source of truth for all your data.

Accessing information in an enterprise knowledge hub

The semantic layer serves as the foundation to knowledge hubs. We can think of a Knowledge Hub as a “one-stop-shop” of data, content, and know-how for an enterprise, where data converges together in a connected workplace and provides the best user experience possible – making it easier for users to find, maintain, and use assets in the future.

Use cases for a knowledge hub

Knowledge hubs are especially helpful when considering data silos across a large organization. Typically, marketing stores their data in one place, product management stores theirs in another, and so on and so forth. Within these different storage systems, there are also vastly different ways to build and manage the data; marketing may use mostly text documents and slide decks, and product management may rely on spreadsheets. The obvious problem here is that none of this data is being shared even though different departments could benefit from it.

Perhaps the marketing team would like to run an analysis on products that have garnered the most customer attention and support, or maybe they want to understand how the product itself works in order to create content for product campaigns. How can the marketer do this when all the information sits in the product management silo that is sectioned off from them?  How can they even begin to search for information when the documents they need are not tagged properly?

Consider also the example of a consultancy firm that takes on various client companies across the globe. With a knowledge hub, dedicated account managers can view all their clients’ information and assets. The knowledge graph that sits in the background connects all their relevant information in one place, so account managers can seamlessly manage their clients’ issues with traceable client history, regardless of the client size or number of subsidiaries they may contain. Account managers can also integrate external information such as local workforce regulations, compliance laws, and tax information to the client profiles, in order to easily advise HR best practices. 

Consultants can anticipate challenges by having a detailed, dynamic overview of their clients’ systems, and recommend the best services in real-time.

Connecting data across your knowledge domains, the knowledge hub draws from the underlying semantic layer and can provide additional benefits:

Company-wide consistency

No matter where your knowledge resides or will be used, profit from a standardized, universal language of your content.

Factual Fidelity

The ability to access a single version of truth is a requirement for deployment scenarios in regulated and critical industries.

Orchestrated Governance

A semantic layer sits between the data and the applications, providing the digital infrastructure with the right levels of management, authentication and authorization.

Less Time to Insights

We combine the knowledge of your experts with the power of machines to produce data that is easier to access, use, and understand.

What’s the benefit of a knowledge hub?
Where a library simply stores information, a knowledge hub provides a powerful environment that contextualizes information in one place. Allowing the user to efficiently store, retrieve, and edit all in one digital workplace.

Other Resources



The Semantic Knowledge Hub – where enterprises benefit from connected workspaces and better user experiences

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Success Story:

A Knowledge Hub at HealthDirect Australia – the basis for innovative health services

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Adding smart recommender systems to the mix

Recommender systems are powerful tools that can assist users in accessing information, media, products, and other assets. In an enterprise, recommender systems are only helpful if they match the specific requirements of the employees who are using them.

Building on a knowledge-based approach with the help of knowledge graphs, smart recommender systems provide employees with the content they seek plus additional suggestions that are not explicitly related to their search query; though the employee is not specifically looking for this content, it can provide additional input that is relevant to the query because it is deemed relevant through these implicit relations.

While other systems go through untraceable processes, knowledge-based recommendation systems follow a clearly defined step-by-step process. (1) The user submits the query, which consists of a sentence, a paragraph, a section, or an entire document. (2) The text passes through the text annotation component and (3) receives its semantic footprint. (4) The subsequent query expansion is a traceable intervention in recommendation depth and recommendation sharpness. (5) The matches found thus contain both the implicit knowledge of the domain model and the query-specific adjustments. (6) The finally obtained recommendation is clearly traceable and controlled.


With smart recommender systems, employees are better informed with the information that is supplied to them, so they can complete tasks more efficiently and feel more secure in decision making – ultimately freeing up employee working time.

The Major Benefits of the PoolParty Recommender

Built for you!

Out-of-the-box solutions quickly reach their limits and it can happen that your recommendation system does not generate the added value you expect. The PoolParty recommender system is as flexible as your company’s needs are. We have been integrating knowledge systems for companies for over 20 years, so our systems are prepared for your specific circumstances by design.

The Advanced Assistant

Semantic recommender systems are driven by domain, context, and intent, which means that they are highly customizable to an organization’s specific use case. While other systems cannot function as the “knowing assistant” to the user, the PoolParty Recommender can make knowledge available that makes a difference and adds value.

Your Plug-in-Style Recommender System

While other systems require a company’s knowledge management to be built from scratch, the PoolParty recommendation system leaves the company’s internal knowledge management intact as basis for implementation and training. A usually existing taxonomy, knowledge graph, or corpus can serve as the foundation on which the PoolParty recommender system is built. The recommender can be operated by existing personnel.

How can smart recommender systems help?
It’s not that the data and information you need to do your job isn’t there,
it’s just that it’s never just one click away – it’s many hops, and that’s tiring. That’s where the PoolParty Recommender comes in. A semantic recommender is one of the prized AI solutions for the digital workplace because it can get employees the information they seek based on the sophisticated pairings it’s making to the search query in the background. 

Other Resources


White Paper:

Recommender Systems for Business Professionals

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Building Recommender Systems that Work

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AI solutions for the digital workplace that actually work

Bringing recommender systems, knowledge hubs, and LLMs together for an AI assistant

When a knowledge hub is coupled with semantic recommendations, the outcomes for the workplace are even higher. And today, especially after the boom of Generative AI where employees are looking for AI assistants and chatbots, there’s a demand to add large language models (LLMs) to this equation. Altogether, these elements give the best output. 

LLMs while admittedly powerful, do have a strong deficit in traceability and accuracy – which is where the semantic layer can come in handy. LLMs often don’t know how they arrive at their answers so they can’t always be trusted by employees who are using the LLM to get information or make decisions. As previously mentioned, logic is mapped in a knowledge graph and content suggestions are traceable in a semantic recommender system.  

In this way, a knowledge graph-infused LLM can help ensure that answers are delivered with precision, accuracy, and trust. Graph Retrieval Augmented Generation (RAG) is a framework designed to make LLMs more reliable by incorporating relevant, up-to-date knowledge from the knowledge graph. It uses natural language processing and semantic search to ensure that documents are found based on the user’s intent of the query, as well as generates new content and summaries about this topic while in dialogue with the chatbot.

This screenshot displays an example application that is built on a Semantic RAG model.

An example of this can be found in our Knowledge Hub demo that is powered by Graph RAG. This AI Assistant provides additional document recommendations, summarized answers, and the ability to engage with the chatbot with follow-ups.

Creating an ideal employee experience with the digital workplace


While most orangizations produce large amounts of data on a daily basis, few are able to use this information to gain valuable insights and enhance employee experience, especially when data points need to be connected.

Linking information from different sources using knowledge graphs and a semantic layer provides the basis for a unified system that connects information and enables informed decision-making for knowledge workers in all industries. The semantic layer provides the basis for numerous applications that can sit on top, thus improving the digital workplace.

The digital workplace is not a one size fits all case, but AI companions or assistants are proving to be a reliable way forward.

These steps toward creating an intuitive workplace for a digital world help organizations reduce or minimize impact of personnel changes, breakdown siloed data to create accesibility of content, and promote the digitalization of systems to provide an ideal employee experience.

See examples of AI solutions for the digital workplace in our tailored webinar collection