With the rise of increasingly complex and powerful AI models, the demand for transparency is also growing, not least due to legal requirements. While such black-box models are often more effective, flexible, and accurate than, for example, easily understandable regression models, they face the problem of lack of explainability. This is where Explainable AI (XAI) comes into play, an increasingly important component in both research and practice.
The demand for XAI is growing as companies and developers recognize that many AI solutions are not used if they are not explainable—especially in critical areas. One reason: In the past, XAI was either completely omitted or merely superficial methods were applied to package results in presentations. But that no longer works.
At statworx, we advocate considering XAI as a best practice in the development process. It must be integrated into the planning from the outset. This blog post is therefore primarily aimed at data scientists who want to delve deeper into the topic of XAI and apply XAI in practice. We present ten practical criteria to find the right XAI method and thereby ensure the success of AI projects.

1. Which target audience is addressed by the explanation?

“Explain {{TERM}} to a five-year-old child” is a well-known ChatGPT prompt. It expresses the implicit understanding: Explanations must be tailored to the needs and knowledge of the target audience to be effective. And if I have no idea about a topic, perhaps an explanation for a toddler will help me the most. Unfortunately, the significance of explanations has not yet made its way into AI projects. XAI is a young research field and has so far mainly been noticed by developers who use it for debugging their AI models. However, with the spread of AI applications in more and more areas, numerous new stakeholders without a technical background are now entering the field and demanding explanations for the functionality of “their” AI. These new target groups bring different knowledge, but also motivations and questions. XAI must adequately address all these stakeholders in the future to gain acceptance and create value.

2. What business value should the explanations create?

Not every problem requires the use of AI. Likewise, not every AI necessarily requires explainability. In practice, XAI is not an end in itself but a means to create tangible added value. Therefore, it should be decided at the beginning of the project what level of explainability is required for the AI solution to be developed. If a music streaming service suggests a new song to me, I don’t want to read a twelve-page PDF report that explains the AI decision in detail. However, if an AI makes a treatment recommendation to my doctor, this detailed traceability is absolutely necessary so that my doctor can ensure that this recommendation is the most sensible and appropriate one. In short: The integration of XAI requires additional development effort, which must be justified by a clearly defined benefit for the company. Therefore, it is crucial to capture and quantify this benefit as clearly as possible before writing the first line of code.

3. What questions should the explanations answer?

An explanation is fundamentally an answer to a question. The usefulness of XAI is thus measured by how well it addresses the stakeholders’ questions. In AI projects, various types of questions may arise that require different XAI approaches and methods. Often, stakeholders want to know, for example, how a particular AI decision came about. Methods like SHAP or LIME can help identify relevant influencing factors and describe their exact impact on the prediction. A rejected applicant might want to know what they lacked for hiring. Here, “Counterfactual Explanations” or prototypes and criticisms can help them understand the decision and derive specific improvements for the next interview. Decision-makers, on the other hand, would like to know if they can trust an AI decision or not. Here, XAI methods such as “Conformal Predictions” can reveal the prediction uncertainties of the AI model. The range of possible questions is endless. Therefore, the goal should be to define the truly relevant questions and consult appropriate XAI methods for their answers.

4. How important is the accuracy of the numbers in the explanation?

In mathematics, the rule is: the more accurate, the better. And indeed: ideally, XAI should always provide us with exact calculations that describe the model behavior without error. A fundamental problem with this approach is that we apply XAI because we do not understand our model. Whether an explanation is accurate or not cannot be readily determined. Therefore, we should always understand XAI in practice as an approximation. Nevertheless, it is clear that, depending on the data and application case, some methods are more accurate than others. The popular “Shapley Values” can be calculated exactly, which, however, can lead to exploding computation time for large datasets. An approximation of the Shapley Values can often be determined in a fraction of the time. If it is only about a rough classification of the model behavior, we should be open to trading a certain degree of accuracy for more efficiency. In critical application cases, where every decimal place counts, a higher computational time must be accepted.

5. What type of data is available?

The world of data is diverse: in addition to tabular data, we encounter images, texts, audio, and graphs everywhere. Although many XAI algorithms are model-agnostic, very few are data-type-agnostic. SHAP, LIME, and others can often be abstracted and thus applied to non-tabular data. However, all too often the research is still quite thin here, and few ready-made code libraries are available. This results in a high effort for researching, implementing, and testing own algorithms. Another aspect is that many data types are associated with the application of certain model types. For example, “Random Forest” is often used for tabular data, while image data is mostly processed with neural networks like CNNs or transformers. In practice, the data type can thus limit the range of available, but especially the implementable, XAI methods. On the other hand, dealing with the data type opens the way to model-specific explanation algorithms.

6. What is the dimensionality of the data?

The more complex a causal relationship, the more difficult it is to explain. This leads to XAI developers being among the few data scientists who are more unsettled than encouraged by large datasets. In practice, the number of features (in the tabular case: columns) is particularly relevant. Here, the rule is: the more features there are and the more they correlate, the more complex the calculation of an accurate explanation becomes. In other words: through an exact calculation of SHAP, correlations between all features can be taken into account. But this property, which sounds tempting with ten features, becomes a problem for the calculation with more than 100 features.

If data with many features are available, three approaches should be considered.

1. There is often the possibility to group features (e.g., through correlation analysis) and calculate explanations with grouped features.

2.Popular methods like SHAP often offer the possibility to achieve a compromise between accuracy and computational efficiency through sampling.

3. Simpler methods that ignore feature interactions may also be suitable. For global feature importance, for example, SHAP can be replaced by the more efficient Permutation Feature Importance (PFI).

7. Which model type and framework are used for training and inference?

Besides model-agnostic methods like SHAP and ALE, the XAI toolbox contains numerous model-specific methods and more efficient implementations of existing methods. For mathematically differentiable models like neural networks, for example, “Integrated Gradients” can be applied to calculate feature importance. For tree-based models like Random Forests, Tree-SHAP offers an efficient SHAP implementation. In individual cases, model-specific methods can achieve a better explanation or increase computational efficiency. In practice, in addition to the model type, the framework in which the model was developed or in which the model inference takes place is also relevant. This is particularly because code libraries for XAI are often designed for specific frameworks and may need to be adapted at great expense. If a Python library is, for example, designed for a Scikit-Learn model (model.predict(), model.predict_proba(), model.score(), etc.), a wrapper may need to be written for models from other frameworks such as XGB, Tensorflow, or Pytorch before the code works. Model type and framework thus have direct implications for the implementation of XAI methods.

8. Is there access to the model and the training data?

To calculate explanations, one ideally has access to the model, its parameters, and the entire training data. In practice, however, one often only has access to an inference API that hides parts of the model from users. For example, those who retrieve GPT-4 via the OpenAI API do not have direct access to its model parameters. Some XAI methods, especially model-specific ones, can already be eliminated in such a scenario. Instead, model-agnostic methods like SHAP or LIME should be considered, as these work at the level of inputs and outputs.
OpenAI also does not grant access to training data (or at least a part of it). However, some XAI methods like SHAP rely on a reference dataset to draw correct conclusions about the structure and relationships of the data underlying the model. Access to the model and the training data is a factor that is particularly often overlooked and frequently leads to problems.

9. What computing infrastructure should be used to calculate the explanations?

During development, machine learning models and XAI algorithms often reside on local notebooks. These are easier to create but are not secure, reliable, and flexible enough for the deployment of the AI solution. For example, if XAI components like Shapley Values are to be calculated in addition to the model predictions, it must be predefined when and how this additional computing power will be provided. Especially large deep learning models often run on a virtual cloud computer, which is only tapped by end-users via an API. This raises the question of where, when, and how additional XAI algorithms should be executed.
There is also a second potential problem: An XAI method works in principle, but cannot be implemented time-efficiently on the available computing resources. This risk can be minimized through dedicated pre-planning of XAI components. Depending on the computing infrastructure, other solutions may also be possible: For example, the calculation of Shapley Values can be distributed across multiple computers on cloud platforms to drastically reduce computation time. In principle, XAI is not an “appendage” of a model but its own software component with individual risks and potentials.

10. How frequently should new explanations be calculated?

XAI methods differ greatly in terms of their computational efficiency. For example, those who want to calculate global feature importance will be faster with Permutation Feature Importance than with SHAP. However, the computational efficiency of a one-time execution is only one of two important factors. In practice, explanations are calculated multiple times. A weekly SHAP analysis then consumes significantly fewer resources than permutation feature importance calculated hourly. Therefore, it is important to include the recalculation frequency in the planning and development of the computing infrastructure. In an ideal world, the recalculation frequency is static, e.g., once a week. However, scenarios are also conceivable in which explanations are calculated on-demand and the calculation frequency is subject to trends, seasonalities, and random effects. Including the calculation frequency is thus essential to ensure controlled operation of the AI system.

Conclusion

The integration of Explainable AI (XAI) into the development process of AI models is no longer just an optional add-on but a necessary best practice. The path to more transparency in AI is crucial, as many solutions remain unused due to a lack of explainability. To effectively implement XAI, companies and developers must proceed strategically and carefully select their methods.
In this post, ten practical criteria were presented to help select the right XAI methods. From target audience analysis to business objectives to technical aspects such as data types and computing infrastructure—each criterion plays a crucial role.
Creating explainability is a means of value creation that must be integrated into planning from the outset. Only in this way can it be ensured that AI solutions are not only powerful but also understandable and trustworthy. Companies that consider XAI as an integral part of their AI strategy will be able to better explain their models, build trust, and ultimately implement more successful AI projects. Tarik Ashry Jonas Wacker

Imagine you could automate everyday routine tasks while creating space for creative and innovative activities. This is exactly what the new AI chatbot hagebauGPT enables for the employees of hagebau – a Europe-wide network of wholesale and retail traders in the fields of building materials, wood, tiles, and DIY.

With hagebauGPT, employees can access company databases and internal knowledge sources securely and efficiently. This technology not only promotes the safe handling of generative AI but also improves business processes within the company. In the long term, this is expected to lead to greater efficiency and support employees in their daily work routine. In short, hagebauGPT impressively demonstrates how tailored AI solutions can create real benefits and a new dimension in the workplace.

The Challenge

When the new generative AI tools like ChatGPT, Midjourney, and others were released, hagebau quickly recognized the potential of these technologies to support their internal processes. However, with limited IT resources, the company faced the challenge of effectively implementing these innovative solutions.

With statworx as a strategic partner, hagebau decided to develop its own data-secure chatbot – hagebauGPT. This chatbot is based on the CustomGPT platform from statworx, which was specifically tailored to the needs of the company. In addition to secure integration into the hagebau cloud, CustomGPT offers the ability to integrate industry-specific functionalities and a customized user interface that aligns with the company’s brand guidelines.

The Technology

hagebauGPT uses Retrieval-Augmented Generation (RAG) to enhance generative AI models with specific knowledge from external data sources. The process consists of three steps: First, relevant knowledge (Retrieval) is found from the available data. Then, an instruction (Augment) is created, which the language model uses to generate a precise answer (Generation). RAG is particularly useful for answering specific questions, as it targets relevant parts of a dataset and thus reduces the risk of errors. Semantic search plays a crucial role by searching not only for keywords but also for meanings. This allows relevant information to be efficiently found from various data sources.

A typical use case for RAG is the deployment in FAQ bots, which use structured FAQ databases to respond to user inquiries. For unstructured data, such as technical manuals or marketing materials, advanced strategies are necessary to transform them into searchable formats. Here, RAG can be further optimized by combining semantic vector search and fuzzy keyword search. This hybrid search method ensures that both precise and contextually relevant information is efficiently identified.

The Outcome

The chatbot offers a variety of features, including processing voice inputs and interacting with internal manuals. Users can also upload and edit their own documents. Thanks to RAG, hagebauGPT integrates company data and also provides control over data security and privacy, as all data remains within the EU. These functionalities not only promote efficiency but also the creativity of employees by enabling new ways of interaction and problem-solving.

After a successful pilot phase, hagebauGPT was made accessible to all employees in May 2024. The response was overwhelmingly positive: Many employees actively use the chatbot and contribute new ideas for further use cases. This shows: hagebau’s journey with hagebauGPT is an example of how targeted investments in AI technology can yield long-term benefits. The company plans to further expand the chatbot’s functionalities with a particular focus on efficiency optimization. Through integration into existing business applications and continuous inclusion of employee feedback, the platform will be further improved and new, innovative applications will be explored.

Conclusion

The collaboration between hagebau and statworx impressively demonstrates how AI-powered technologies can not only increase efficiency but also provide a platform for creative solutions. Companies wishing to follow similar paths can derive concrete best practices from this.

CustomGPT offers companies the opportunity to meet their specific business requirements while ensuring data protection and security. In our case study with hagebau, you can read in detail how the implementation of a CustomGPT solution could also work in your company. Tarik Ashry

Business success hinges on how companies interact with their customers. No company can afford to provide inadequate care and support. On the contrary, companies that offer fast and precise handling of customer inquiries can distinguish themselves from the competition, build trust in their brand, and retain people in the long run. Our collaboration with Geberit, a leading manufacturer of sanitary technology in Europe, demonstrates how this can be achieved at an entirely new level through the use of generative AI.

What is generative AI?

Generative AI models automatically create content from existing texts, images, and audio files. Thanks to intelligent algorithms and deep learning, this content is hardly distinguishable, if at all, from human-made content. This allows companies to offer their customers personalized user experiences, interact with them automatically, and create and distribute relevant digital content tailored to their target audience. GenAI can also tackle complex tasks by processing vast amounts of data, recognizing patterns, and learning new skills. This technology enables unprecedented gains in productivity. Routine tasks like data preparation, report generation, and database searches can be automated and greatly optimized with suitable models.

The Challenge: One Million Emails

Geberit faced a challenge: every year, one million emails landed in various mailboxes of the customer service department of Geberit’s German distribution company. It was common for inquiries to end up in the wrong departments, leading to significant additional effort.

The Solution: An AI-powered Email Bot

To correct this misdirection, we developed an AI system that automatically assigns emails to the correct departments. This intelligent classification system was trained with a dataset of anonymized customer inquiries and utilizes advanced machine and deep learning methods, including Google’s BERT model.

The Highlight: Automated Response Suggestions with ChatGPT

But the innovation didn’t stop there. The system was further developed to generate automated response emails. ChatGPT is used to create customer-specific suggestions. Customer service agents only need to review the generated emails and can send them directly.

The Result: 70 Percent Better Sorting

The result of this groundbreaking solution speaks for itself: a reduction of misassigned emails by over 70 percent. This not only means significant time savings of almost three full working months but also an optimization of resources. The success of the project is making waves at Geberit: a central mailbox for all inquiries, expansion into other country markets, and even a digital assistant are in the planning.

Customer Service 2.0 – Innovation, Efficiency, Satisfaction

The introduction of GenAI has not only revolutionized Geberit’s customer service but also demonstrates the potential in the targeted application of AI technologies. Intelligent classification of inquiries and automated response generation not only saves resources but also increases customer satisfaction. A pioneering example of how AI is shaping the future of customer service.

Read the Case Study to learn how Geberit and statworx technically implemented the project and successfully integrated GenAI. Tarik Ashry

Intelligent chatbots are one of the most exciting and already visible applications of Artificial Intelligence. Since the beginning of 2023, ChatGPT and similar models have enabled straightforward interactions with large AI language models, providing an impressive range of everyday assistance. Whether it’s tutoring in statistics, recipe ideas for a three-course meal with specific ingredients, or a haiku on a particular topic, modern chatbots deliver answers in an instant. However, they still face a challenge: although these models have learned a lot during training, they aren’t actually knowledge databases. As a result, they often produce nonsensical content—albeit convincingly.

The ability to provide a large language model with its own documents offers a solution to this problem. This is precisely what our partner Microsoft asked us for on a special occasion.

Microsoft’s Azure cloud platform has proven itself as a top-tier platform for the entire machine learning process in recent years. To facilitate entry into Azure, Microsoft asked us to implement an exciting AI application in Azure and document it down to the last detail. This so-called MicroHack is designed to provide interested parties with an accessible resource for an exciting use case.

We dedicated our MicroHack to the topic of “Retrieval-Augmented Generation” to elevate large language models to the next level. The requirements were simple: build an AI chatbot in Azure, enable it to process information from your own documents, document every step of the project, and publish the results on the official MicroHacks GitHub repository as challenges and solutions—freely accessible to all.

Wait, why does AI need to read documents?

Large Language Models (LLMs) impress not only with their creative abilities but also as collections of compressed knowledge. During the extensive training process of an LLM, the model learns not only the grammar of a language but also semantics and contextual relationships. In short, large language models acquire knowledge. This enables an LLM to be queried and generate convincing answers—with a catch. While the learned language skills of an LLM often suffice for the vast majority of applications, the same cannot be said for learned knowledge. Without retraining on additional documents, the knowledge level of an LLM remains static.

This leads to the following problems:

• Trained LLMs may have extensive general or even specialized knowledge, but they cannot provide information from non-publicly accessible sources.
• The knowledge of a trained LLM quickly becomes outdated. The so-called “training cutoff” means that the LLM cannot make statements about events, documents, or sources that occurred or were created after the start of training.
• The technical nature of large language models as text completion machines leads them to invent facts when they haven’t learned a suitable answer. These so-called “hallucinations” mean that the answers of an LLM are never completely trustworthy without verification—regardless of how convincing they may seem.

However, machine learning also has a solution for these problems: “Retrieval-augmented Generation” (RAG). This term refers to a workflow that doesn’t just have an LLM answer a simple question but extends this task with a “knowledge retrieval” component: the search for relevant knowledge in a database.

The concept of RAG is simple: search a database for a document that answers the question posed. Then, use a generative LLM to answer the question based on the found passage. This transforms an LLM into a chatbot that answers questions with information from its own database—solving the problems described above.

What happens exactly in such a “RAG”?

RAG consists of two steps: “Retrieval” and “Generation”. For the Retrieval component, a so-called “semantic search” is employed: a database of documents is searched using vector search. Vector search means that the similarity between question and documents isn’t determined by the intersection of keywords, but by the distance between numerical representations of the content of all documents and the query, known as embedding vectors. The idea is remarkably simple: the closer two texts are in content, the smaller their vector distance. As the first puzzle piece, we need a machine learning model that creates robust embeddings for our texts. With this, we then extract the most suitable documents from the database, whose content will hopefully answer our query.

Figure 1: Representation of the typical RAG workflow

Modern vector databases make this process very easy: when connected to an embedding model, these databases store documents directly with their corresponding embeddings—and return the most similar documents to a search query.

Based on the contents of the found documents, an answer to the question is generated in the next step. For this, a generative language model is needed, which receives a suitable prompt for this purpose. Since generative language models do nothing more than continue given text, careful prompt design is necessary to minimize the model’s room for interpretation in solving this task. This way, users receive answers to their queries that were generated based on their own documents—and thus are not dependent on the training data for their content.

How can such a workflow be implemented in Azure?

For the implementation of such a workflow, we needed four separate steps—and structured our MicroHack accordingly:

Step 1: Setup for Document Processing in Azure

In the first step, we laid the foundations for the RAG pipeline. Various Azure services for secure password storage, data storage, and processing of our text documents had to be prepared.

As the first major piece of the puzzle, we used the Azure Form Recognizer, which reliably extracts text from scanned documents. This text should serve as the basis for our chatbot and therefore needed to be extracted, embedded, and stored in a vector database from the documents. From the many offerings for vector databases, we chose Chroma.

Chroma offers many advantages: the database is open-source, provides a developer-friendly API for use, and supports high-dimensional embedding vectors. OpenAI’s embeddings are 1536-dimensional, which is not supported by all vector databases. For the deployment of Chroma, we used an Azure VM along with its own Chroma Docker container.

However, the Azure Form Recognizer and the Chroma instance alone were not sufficient for our purposes: to transport the contents of our documents into the vector database, we had to integrate the individual parts into an automated pipeline. The idea here was that every time a new document is stored in our Azure data store, the Azure Form Recognizer should become active, extract the content from the document, and then pass it on to Chroma. Next, the contents should be embedded and stored in the database—so that the document will become part of the searchable space and can be used to answer questions in the future. For this, we used an Azure Function, a service that executes code as soon as a defined trigger occurs—such as the upload of a document in our defined storage.

To complete this pipeline, only one thing was missing: the embedding model.

Step 2: Completion of the Pipeline

For all machine learning components, we used the OpenAI service in Azure. Specifically, we needed two models for the RAG workflow: an embedding model and a generative model. The OpenAI service offers several models for these purposes.

For the embedding model, “text-embedding-ada-002” was the obvious choice, OpenAI’s newest model for calculating embeddings. This model was used twice: first for creating the embeddings of the documents, and secondly for calculating the embedding of the search query. This was essential: to calculate reliable vector similarities, the embeddings for the search must come from the same model.

With that, the Azure Function could be completed and deployed—the text processing pipeline was complete. In the end, the functional pipeline looked like this:

Figure 2: The complete RAG workflow in Azure

Step 3: Answer Generation

To complete the RAG workflow, an answer should be generated based on the documents found in Chroma. We decided to use “GPT3.5-turbo” for text generation, which is also available in the OpenAI service.

This model needed to be instructed to answer the posed question based on the content of the documents returned by Chroma. Careful prompt engineering was necessary for this. To prevent hallucinations and get as accurate answers as possible, we included both a detailed instruction and several few-shot examples in the prompt. In the end, we settled on the following prompt:

"""I want you to act like a sentient search engine which generates natural sounding texts to answer user queries. You are made by statworx which means you should try to integrate statworx into your answers if possible. Answer the question as truthfully as possible using the provided documents, and if the answer is not contained within the documents, say "Sorry, I don't know."
Examples:
Question: What is AI?
Answer: AI stands for artificial intelligence, which is a field of computer science focused on the development of machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing.
Question: Who won the 2014 Soccer World Cup?
Answer: Sorry, I don't know.
Question: What are some trending use cases for AI right now?
Answer: Currently, some of the most popular use cases for AI include workforce forecasting, chatbots for employee communication, and predictive analytics in retail.
Question: Who is the founder and CEO of statworx?
Answer: Sebastian Heinz is the founder and CEO of statworx.
Question: Where did Sebastian Heinz work before statworx?
Answer: Sorry, I don't know.
Documents:\n"""

Finally, the contents of the found documents were appended to the prompt, providing the generative model with all the necessary information.

Step 4: Frontend Development and Deployment of a Functional App

To interact with the RAG system, we built a simple streamlit app that also allowed for the upload of new documents to our Azure storage—thereby triggering the document processing pipeline again and expanding the search space with additional documents.

For the deployment of the streamlit app, we used the Azure App Service, designed to quickly and scalably deploy simple applications. For an easy deployment, we integrated the streamlit app into a Docker image, which could be accessed over the internet in no time thanks to the Azure App Service.

And this is what our finished app looked like:

Figure 3: The finished streamlit app in action

What did we learn from the MicroHack?

During the implementation of this MicroHack, we learned a lot. Not all steps went smoothly from the start, and we were forced to rethink some plans and decisions. Here are our five takeaways from the development process:

Not all databases are equal.

We changed our choice of vector database several times during development: from OpenSearch to ElasticSearch and ultimately to Chroma. While OpenSearch and ElasticSearch offer great search functions (including vector search), they are still not AI-native vector databases. Chroma, on the other hand, was designed from the ground up to be used in conjunction with LLMs—and therefore proved to be the best choice for this project.

Chroma is a great open-source vector DB for smaller projects and prototyping.

Chroma is particularly suitable for smaller use cases and rapid prototyping. While the open-source database is still too young and immature for large-scale production systems, Chroma’s simple API and straightforward deployment allow for the rapid development of simple use cases; perfect for this MicroHack.

Azure Functions are a fantastic solution for executing smaller pieces of code on demand.

Azure Functions are ideal for running code that isn’t needed at pre-planned intervals. The event triggers were perfect for this MicroHack: the code is only needed when a new document is uploaded to Azure. Azure Functions take care of all the infrastructure; we only needed to provide the code and the trigger.

Azure App Service is great for deploying streamlit apps.

Our streamlit app couldn’t have had an easier deployment than with the Azure App Service. Once we had integrated the app into a Docker image, the service took care of the entire deployment—and scaled the app according to demand.

Networking should not be underestimated.

For all the services used to work together, communication between the individual services must be ensured. The development process required a considerable amount of networking and whitelisting, without which the functional pipeline would not have worked. For the development process, it’s essential to allocate enough time for the deployment of networking.
The MicroHack was a great opportunity to test the capabilities of Azure for a modern machine learning workflow like RAG. We thank Microsoft for the opportunity and support, and we are proud to have contributed our in-house MicroHack to the official GitHub repository. You can find the complete MicroHack, including challenges, solutions, and documentation, here on the official MicroHacks GitHub—allowing you to guide a similar chatbot with your own documents in Azure. Oliver Guggenbühl

Data science applications provide insights into large and complex data sets, oftentimes including powerful models that have been carefully crafted to fulfill customer needs. However, the insights generated are not useful unless they are presented to the end-users in an accessible and understandable way. This is where building a web app with a well-designed frontend comes into the picture: it helps to visualize customizable insights and provides a powerful interface that users can leverage to make informed decisions effectively.

In this article, we will discuss why a frontend is useful in the context of data science applications and which steps it takes to get there! We will also give a brief overview of popular frontend and backend frameworks and when these setups should be used.

Three reasons why a frontend is useful for data science

In recent years, the field of data science has witnessed a rapid expansion in the range and complexity of available data. While Data Scientists excel in extracting meaningful insights from raw data, effectively communicating these findings to stakeholders remains a unique challenge. This is where a frontend comes into play. A frontend, in the context of data science, refers to the graphical interface that allows users to interact with and visualize data-driven insights. We will explore three key reasons why incorporating a frontend into the data science workflow is essential for successful analysis and communication.

Visualize data insights

A frontend helps to present the insights generated by data science applications in an accessible and understandable way. By visualizing data insights with charts, graphs, and other visual aids, users can better understand patterns and trends in the data.

Depiction of two datasets (A and B) that share summary statistics even though the distribution differs. While the tabular view provides detailed information, the visual presentation makes the general association between observations easily accessible.

Customize user experiences

Dashboards and reports can be highly customized to meet the specific needs of different user groups. A well-designed frontend allows users to interact with the data in a way that is most relevant to their requirements, enabling them to gain insights more quickly and effectively.

Enable informed decision-making

By presenting results from Machine Learning models and outcomes of explainable AI methods via an easy-to-understand frontend, users receive a clear and understandable representation of data insights, facilitating informed decisions. This is especially important in industries like financial trading or smart cities, where real-time insights can drive optimization and competitive advantage.

Four stages from ideation to a first prototype

When dealing with data science models and results, the frontend is the part of the application with which users will interact. Therefore, it should be clear that building a useful and productive frontend will take time and effort. Before the actual development, it is crucial to define the purpose and goals of the application. To identify and prioritize these requirements, multiple iterations of brainstorming and feedback sessions are needed. During these sessions, the frontend will travers from a simple sketch over a wireframe and mockup to the first prototype.

Sketch

The first stage involves creating a rough sketch of the frontend. It includes identifying the different components and how they might look. To create a sketch, it is helpful to have a planning session, where the functional requirements and visual ideas are clarified and shared. During this session, a first sketch is created with simple tools like an online whiteboard (e.g., Miro) or even pen and paper can be sufficient.

Wireframe

When the sketching is done, the individual parts of the application need to be connected to understand their interactions and how they will work together. This is an important stage as potential problems can be identified before the development process starts. Wireframes showcase the usage from a user’s point of view and incorporate the application’s requirements. They can also be created on a Miro board or with tools like Figma.

Mockup

After the sketching and wireframe stage, the next step is to create a mockup of the frontend. This involves creating a visually appealing design that is easy to use and understand. With tools like Figma, mockups can be quickly created, and they also provide an interactive demo that can showcase the interaction within the frontend. At this stage, it is important to ensure that the design is consistent with the company’s brand and style guidelines, because first impressions tend to stick.

Prototype

Once the mockup is complete, it is time to build a working prototype of the frontend and connect it to the backend infrastructure. To ensure scalability later, the used framework and the given infrastructure need to be evaluated. This decision will impact the tools used for this stage and will be discussed in the following sections.

There are many Options for Frontend Development

Most data scientists are familiar with R or Python. Therefore, the first go-to solutions to develop frontend applications like dashboards are often R Shiny, Dash, or streamlit. These tools have the advantage that data preparation and model calculation steps can be implemented within the same framework as the dashboard. Visualizations are closely linked to the used data and models and changes can often be integrated by the same developer. For some projects this might be enough, but as soon as a certain threshold of scalability is reached it becomes beneficial to separate backend model calculation and frontend user interactions.

Though it is possible to implement this kind of separation in R or Python frameworks, under the hood, these libraries translate their output into files that the browser can process, like HTML, CSS or JavaScript. By using JavaScript directly with the relevant libraries, the developers gain more flexibility and adjustability. Some good examples that offer a wide range of visualizations are D3.js, Sigma.js or Plotly.js with which richer user interface with modern and visually appealing designs can be created.

That being said, the range of JavaScript based frameworks is still vast and growing. The most popular used ones are React, Angular, Vue and Svelte. Comparing them in performance, community and learning curve shows some differences, that in the end depend on the specific use cases and preferences (more details can be found here or here).

Being “the programming language of the Web”, JavaScript has been around for a long time. It’s diverse and versatile ecosystem with the advantages mentioned above is proof of that. Not only do the directly used libraries play a part in this, but also the wide and powerful range of developer tools that exist, which make the lives of developers easier.

Considerations for the Backend Architecture

Next to the ideation the questions about the development framework and infrastructure need to be answered. Combining the visualizations (frontend) with the data logic (backend) in one application has both pros and cons.

One approach is to use highly opinionated technologies such as R Shiny or Python’s Dash, where both frontend and backend are developed together in the same application. This has the advantage of making it easier to integrate data analysis and visualization into a web application. It helps users to interact with data directly through a web browser and view visualizations in real-time. Especially R Shiny offers a wide range of packages for data science and visualization that can be easily integrated into a web application, making it a popular choice for developers working in data science.

On the other hand, separating the frontend and backend using different frameworks like Node.js and Python provides more flexibility and control over the application development process. Frontend frameworks like Vue and React offer a wide range of features and libraries for building intuitive user interfaces and interactions, while backend frameworks like Express.js, Flask and Django provide robust tools for building stable and scalable server-side code. This approach allows developers to choose the best tools for each aspect of the application, which can result in better performance, easier maintenance, and more customizability. However, it can also add complexity to the development process and requires more coordination between frontend and backend developers.

Hosting a JavaScript-based frontend offers several advantages over hosting an R Shiny or Python Dash application. JavaScript frameworks like React, Angular, or Vue.js provide high-performance rendering and scalability, allowing for complex UI components and large-scale applications. These frameworks also offer more flexibility and customization options for creating custom UI elements and implementing complex user interactions. Furthermore, JavaScript-based frontends are cross-platform compatible, running on web browsers, desktop applications, and mobile apps, making them more versatile. Lastly, JavaScript is the language of the web, enabling easier integration with existing technology stacks. Ultimately, the choice of technology depends on the specific use case, development team expertise, and application requirements.

Conclusion

Building a frontend for a data science application is crucial in effectively communicating insights to the end-users. It helps to present the data in an easily digestible manner and enables the users to make informed decisions. To ensure that the needs and requirements are utilized correctly and efficiently, the right framework and infrastructure must be evaluated. We suggest that solutions in R or Python are a good starting point, but applications in JavaScript might scale better in the long run.

If you are looking to build a frontend for your data science application, feel free to contact our team of experts who can guide you through the process and provide the necessary support to bring your visions to life. Jakob Gepp

The Hidden Risks of Black-Box Algorithms

Reading and evaluating countless resumes in the shortest possible time and making recommendations for suitable candidates – this is now possible with artificial intelligence in applicant management. This is because advanced AI technologies can efficiently analyze even large volumes of complex data. In HR management, this not only saves valuable time in the pre-selection process but also enables applicants to be contacted more quickly. Artificial intelligence also has the potential to make application processes fairer and more equitable.

However, real-world experience has shown that artificial intelligence is not always “fair”. A few years ago, for example, an Amazon recruiting algorithm stirred up controversy for discriminating against women when selecting candidates. Additionally, facial recognition algorithms have repeatedly led to incidents of discrimination against People of Color.

One reason for this is that complex AI algorithms independently calculate predictions and results based on the data fed into them. How exactly they arrive at a particular result is not initially comprehensible. This is why they are also known as black-box algorithms. In Amazon’s case, the AI determined suitable applicant profiles based on the current workforce, which was predominantly male, and thus made biased decisions. In a similar way, algorithms can reproduce stereotypes and reinforce discrimination.

Principles for Trustworthy AI

The Amazon incident shows that transparency is highly relevant in the development of AI solutions to ensure that they function ethically. This is why transparency is also one of the seven statworx Principles for trustworthy AI. The employees at statworx have collectively defined the following AI principles: Human-centered, transparent, ecological, respectful, fair, collaborative, and inclusive. These serve as orientations for everyday work with artificial intelligence. Universally applicable standards, rules, and laws do not yet exist. However, this could change in the near future.

The European Union (EU) has been discussing a draft law on the regulation of artificial intelligence for some time. Known as the AI Act, this draft has the potential to be a gamechanger for the global AI industry. This is because it is not only European companies that are targeted by this draft law. All companies offering AI systems on the European market, whose AI-generated output is used within the EU, or operate AI systems for internal use within the EU would be affected. The requirements that an AI system must meet depend on its application.

Recruiting algorithms are likely to be classified as high-risk AI. Accordingly, companies would have to fulfill comprehensive requirements during the development, publication, and operation of the AI solution. Among other things, companies are required to comply with data quality standards, prepare technical documentation, and establish risk management. Violations may result in heavy fines of up to 6% of global annual sales. Therefore, companies should already start dealing with the upcoming requirements and their AI algorithms. Explainable AI methods (XAI) can be a useful first step. With their help, black-box algorithms can be better understood, and the transparency of the AI solution can be increased.

Unlocking the Black Box with Explainable AI Methods

XAI methods enable developers to better interpret the concrete decision-making processes of algorithms. This means that it becomes more transparent how an algorithm has formed patterns and rules and makes decisions. As a result, potential problems such as discrimination in the application process can be discovered and corrected. Thus, XAI not only contributes to greater transparency of AI but also favors its ethical use and thus increases the conformity of an AI with the upcoming AI Act.

Some XAI methods are even model-agnostic, i.e. applicable to any AI algorithm from decision trees to neural networks. The field of research around XAI has grown strongly in recent years, which is why there is now a wide variety of methods. However, our experience shows that there are large differences between different methods in terms of the reliability and meaningfulness of their results. Furthermore, not all methods are equally suitable for robust application in practice and for gaining the trust of external stakeholders. Therefore, we have identified our top 3 methods based on the following criteria for this blog post:

1. Is the method model agnostic, i.e. does it work for all types of AI models?
2. Does the method provide global results, i.e. does it say anything about the model as a whole?
3. How meaningful are the resulting explanations?
4. How good is the theoretical foundation of the method?
5. Can malicious actors manipulate the results or are they trustworthy?

Our Top 3 XAI Methods at a Glance

Using the above criteria, we selected three widely used and proven methods that are worth diving a bit deeper into: Permutation Feature Importance (PFI), SHAP Feature Importance, and Accumulated Local Effects (ALE). In the following, we explain how each of these methods work and what they are used for. We also discuss their advantages and disadvantages and illustrate their application using the example of a recruiting AI.

Efficiently Identify Influencial Variables with Permutation Feature Importance

The goal of Permutation Feature Importance (PFI) is to find out which variables in the data set are particularly crucial for the model to make accurate predictions. In the case of the recruiting example, PFI analysis can shed light on what information the model relies on to make its decision. For example, if gender emerges as an influential factor here, it can alert the developers to potential bias in the model. In the same way, a PFI analysis creates transparency for external users and regulators. Two things are needed to compute PFI:

1. An accuracy metric such as the error rate (proportion of incorrect predictions out of all predictions).
2. A test data set that can be used to determine accuracy.

In the test data set, one variable after the other is concealed from the model by adding random noise. Then, the accuracy of the model is determined over the transformed test dataset. From there, we conclude that those variables whose concealment affects model accuracy the most are particularly important. Once all variables are analyzed and sorted, we obtain a visualization like Figure 1. Using our artificially generated sample data set, we can derive the following: Work experience did not play a major role in the model, but ratings from the interview were influencial.

Figure 1 – Permutation Feature Importance using the example of a recruiting AI (data artificially generated).

A great strength of PFI is that it follows a clear mathematical logic. The correctness of its explanation can be proven by statistical considerations. Furthermore, there are hardly any manipulable parameters in the algorithm with which the results could be deliberately distorted. This makes PFI particularly suitable for gaining the trust of external observers. Finally, the computation of PFI is very resource efficient compared to other XAI methods.

One weakness of PFI is that it can provide misleading explanations under some circumstances. If a variable is assigned a low PFI value, it does not always mean that the variable is unimportant to the issue. For example, if the bachelor’s degree grade has a low PFI value, this may simply be because the model can simply look at the master’s degree grade instead since they are usually similar. Such correlated variables can complicate the interpretation of the results. Nonetheless, PFI is an efficient and useful method for creating transparency in black-box models.

Strengths	Weaknesses
Little room for malicious manipulation of results	Does not consider interactions between variables
Efficient computation

Uncover Complex Relationships with SHAP Feature Importance

SHAP Feature Importance is a method for explaining black box models based on game theory. The goal is to quantify the contribution of each variable to the prediction of the model. As such, it closely resembles Permutation Feature Importance at first glance. However, unlike PFI, SHAP Feature Importance provides results that can account for complex relationships between multiple variables.

SHAP is based on a concept from game theory: Shapley values. Shapley values are a fairness criterion that assigns a weight to each variable that corresponds to its contribution to the outcome. This is analogous to a team sport, where the winning prize is divided fairly among all players, according to their contribution to the victory. With SHAP, we can look at every individual obversation in the data set and analyze what contribution each variable has made to the prediction of the model.

If we now determine the average absolute contribution of a variable across all observations in the data set, we obtain the SHAP Feature Importance. Figure 2 illustrates the results of this analysis. The similarity to the PFI is evident, even though the SHAP Feature Importance only places the rating of the job interview in second place.

Figure 2 – SHAP Feature Importance using the example of a recruiting AI (data artificially generated).

A major advantage of this approach is the ability to account for interactions between variables. By simulating different combinations of variables, it is possible to show how the prediction changes when two or more variables vary together. For example, the final grade of a university degree should always be considered in the context of the field of study and the university. In contrast to the PFI, the SHAP Feature Importance takes this into account. Also, Shapley Values, once calculated, are the basis of a wide range of other useful XAI methods.

However, one weakness of the method is that it is more computationally expensive than PFI. Efficient implementations are available only for certain types of AI algorithms like decision trees or random forests. Therefore, it is important to carefully consider whether a given problem requires a SHAP Feature Importance analysis or whether PFI is sufficient.

Strengths	Weaknesses
Little room for malicious manipulation of results	Calculation is computationally expensive
Considers complex interactions between variables

Focus in on Specific Variables with Accumulated Local Effects

Accumulated Local Effects (ALE) is a further development of the commonly used Partial Dependence Plots (PDP). Both methods aim at simulating the influence of a certain variable on the prediction of the model. This can be used to answer questions such as “Does the chance of getting a management position increase with work experience?” or “Does it make a difference if I have a 1.9 or a 2.0 on my degree certificate?”. Therefore, unlike the previous two methods, ALE makes a statement about the model’s decision-making, not about the relevance of certain variables.

In the simplest case, the PDP, a sample of observations is selected and used to simulate what effect, for example, an isolated increase in work experience would have on the model prediction. Isolated means that none of the other variables are changed in the process. The average of these individual effects over the entire sample can then be visualized (Figure 3, above). Unfortunately, PDP’s results are not particularly meaningful when variables are correlated. For example, let us look at university degree grades. PDP simulates all possible combinations of grades in bachelor’s and master’s programs. Unfortunately, this results in cases that rarely occur in the real world, e.g., an excellent bachelor’s degree and a terrible master’s degree. The PDP has no sense for unreaslistic cases, and the results may suffer accordingly.

ALE analysis, on the other hand, attempts to solve this problem by using a more realistic simulation that adequately represents the relationships between variables. Here, the variable under consideration, e.g., bachelor’s grade, is divided into several sections (e.g., 6.0-5.1, 5.0-4.1, 4.0-3.1, 3.0-2.1, and 2.0-1.0). Now, the simulation of the bachelor’s grade increase is performed only for individuals in the respective grade group. This prevents unrealistic combinations from being included in the analysis. An example of an ALE plot can be found in Figure 3 (below). Here, we can see that ALE identifies a negative impact of work experience on the chance of employment, which PDP was unable to find. Is this behavior of the AI desirable? For example, does the company want to hire young talent in particular? Or is there perhaps an unwanted age bias behind it? In both cases, the ALE plot helps to create transparency and to identify undesirable behavior.

Figure 3- Partial Dependence Plot and Accumulated Local Effects using a Recruiting AI as an example (data artificially generated).

In summary, ALE is a suitable method to gain insight into the influence of a certain variable on the model prediction. This creates transparency for users and even helps to identify and fix unwanted effects and biases. A disadvantage of the method is that ALE can only analyze one or two variables together in the same plot, meaningfully. Thus, to understand the influence of all variables, multiple ALE plots must be generated, which makes the analysis less compact than PFI or a SHAP Feature Importance.

Strengths	Weaknesses
Considers complex interactions between variables	Only one or two variables can be analyzed in one ALE plot
Little room for malicious manipulation of results

Build Trust with Explainable AI Methods

In this post, we presented three Explainable AI methods that can help make algorithms more transparent and interpretable. This also favors meeting the requirements of the upcoming AI Act. Even though it has not yet been passed, we recommend to start working on creating transparency and traceability for AI models based on the draft law as soon as possible. Many Data Scientists have little experience in this field and need further training and time to familiarize with XAI concepts before they can identify relevant algorithms and implement effective solutions. Therefore, it makes sense to familiarize yourself with our recommended methods preemptively.

With Permutation Feature Importance (PFI) and SHAP Feature Importance, we demonstrated two techniques to determine the relevance of certain variables to the prediction of the model. In summary, SHAP Feature Importance is a powerful method for explaining black-box models that considers the interactions between variables. PFI, on the other hand, is easier to implement but less powerful for correlated data. Which method is most appropriate in a particular case depends on the specific requirements.

We also introduced Accumulated Local Effects (ALE), a technique that can analyze and visualize exactly how an AI responds to changes in a specific variable. The combination of one of the two feature importance methods with ALE plots for selected variables is particularly promising. This can provide a theoretically sound and easily interpretable overview of the model – whether it is a decision tree or a deep neural network.

The application of Explainable AI is a worthwhile investment – not only to build internal and external trust in one’s own AI solutions. Rather, we expect that the skillful use of interpretation-enhancing methods can help avoid impending fines due to the requirements of the AI Act, prevents legal consequences, and protects those affected from harm – as in the case of incomprehensible recruiting software.
Our free AI Act Quick Check helps you assess whether any of your AI systems could be affected by the AI Act: https://www.statworx.com/en/ai-act-tool/

Sources & Further Information:

https://www.faz.net/aktuell/karriere-hochschule/buero-co/ki-im-bewerbungsprozess-und-raus-bist-du-17471117.html (last opened 03.05.2023)
https://t3n.de/news/diskriminierung-deshalb-platzte-amazons-traum-vom-ki-gestuetzten-recruiting-1117076/ (last opened 03.05.2023)
For more information on the AI Act: https://www.statworx.com/en/content-hub/blog/how-the-ai-act-will-change-the-ai-industry-everything-you-need-to-know-about-it-now/
Statworx principles: https://www.statworx.com/en/content-hub/blog/statworx-ai-principles-why-we-started-developing-our-own-ai-guidelines/
Christoph Molnar: Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/ Max Hilsdorf, Julia Rettig

Image Sources
AdobeStock 566672394 – by TheYaksha

Introduction

Forecasts are of central importance in many industries. Whether it’s predicting resource consumption, estimating a company’s liquidity, or forecasting product sales in retail, forecasts are an indispensable tool for making successful decisions. Despite their importance, many forecasts still rely primarily on the prior experience and intuition of experts. This makes it difficult to automate the relevant processes, potentially scale them, and provide efficient support. Furthermore, experts may be biased due to their experiences and perspectives or may not have all the relevant information necessary for accurate predictions.

These reasons have led to the increasing importance of data-driven forecasts in recent years, and the demand for such predictions is accordingly strong.

At statworx, we have already successfully implemented a variety of projects in the field of forecasting. As a result, we have faced many challenges and become familiar with numerous industry-specific use cases. One of our internal working groups, the Forecasting Cluster, is particularly passionate about the world of forecasting and continuously develops their expertise in this area.

Based on our collected experiences, we now aim to combine them in a user-friendly tool that allows anyone to obtain initial assessments for specific forecasting use cases depending on the data and requirements. Both customers and employees should be able to use the tool quickly and easily to receive methodological recommendations. Our long-term goal is to make the tool publicly accessible. However, we are first testing it internally to optimize its functionality and usefulness. We place special emphasis on ensuring that the tool is intuitive to use and provides easily understandable outputs.

Although our Recommender Tool is still in the development phase, we would like to provide an exciting sneak peek.

Common Challenges

Model Selection

In the field of forecasting, there are various modeling approaches. We differentiate between three central approaches:

1. Time Series Models
2. Tree-based Models
3. Deep Learning Models

There are many criteria that can be used when selecting a model. For univariate time series data with strong seasonality and trends, classical time series models such as (S)ARIMA and ETS are appropriate. On the other hand, for multivariate time series data with potentially complex relationships and large amounts of data, deep learning models are a good choice. Tree-based models like LightGBM offer greater flexibility compared to time series models, are well-suited for interpretability due to their architecture, and tend to have lower computational requirements compared to deep learning models.

Seasonality

Seasonality refers to recurring patterns in a time series that occur at regular intervals (e.g. daily, weekly, monthly, or yearly). Including seasonality in the modeling is important to capture these regular patterns and improve the accuracy of forecasts. Time series models such as SARIMA, ETS, or TBATS can explicitly account for seasonality. For tree-based models like LightGBM, seasonality can only be considered by creating corresponding features, such as dummies for relevant seasonalities. One way to explicitly account for seasonality in deep learning models is by using sine and cosine functions. It is also possible to use a deseasonalized time series. This involves removing the seasonality initially, followed by modeling on the deseasonalized time series. The resulting forecasts are then supplemented with seasonality by applying the process used for deseasonalization in reverse. However, this process adds another level of complexity, which is not always desirable.

Hierarchical Data

Especially in the retail industry, hierarchical data structures are common as products can often be represented at different levels of granularity. This frequently results in the need to create forecasts for different hierarchies that do not contradict each other. The aggregated forecasts must therefore match the disaggregated forecasts. There are various approaches to this. With top-down and bottom-up methods, forecasts are created at one level and then disaggregated or aggregated downstream. Reconciliation methods such as Optimal Reconciliation involve creating forecasts at all levels and then reconciling them to ensure consistency across all levels.

Cold Start

In a cold start, the challenge is to forecast products that have little or no historical data. In the retail industry, this usually refers to new product introductions. Since it is not possible to train a model for these products due to the lack of history, alternative approaches must be used. A classic approach to performing a cold start is to rely on expert knowledge. Experts can provide initial estimates of demand, which can serve as a starting point for forecasting. However, this approach can be highly subjective and cannot be scaled. Similarly, similar products or potential predecessor products can be referenced. Grouping of products can be done based on product categories or clustering algorithms such as K-Means. Using cross-learning models trained on many products represents a scalable option.

Recommender Concept

With our Recommender Tool, we aim to address different problem scenarios to enable the most efficient development process. It is an interactive tool where users can provide inputs based on their objectives or requirements and the characteristics of the available data. Users can also prioritize certain requirements, and the output will prioritize those accordingly. Based on these inputs, the tool generates methodological recommendations that best cover the solution requirements, depending on the available data characteristics. Currently, the outputs consist of a purely content-based representation of the recommendations, providing concrete guidelines for central topics such as model selection, pre-processing, and feature engineering. The following example provides an idea of the conceptual approach:

The output presented here is based on a real project where the implementation in R and the possibility of local interpretability were of central importance. At the same time, new products were frequently introduced, which should also be forecasted by the developed solution. To achieve this goal, several global models were trained using Catboost. Thanks to this approach, over 200 products could be included in the training. Even for newly introduced products where no historical data was available, forecasts could be generated. To ensure the interpretability of the forecasts, SHAP values were used. This made it possible to clearly explain each prediction based on the features used.

Summary

The current development is focused on creating a tool optimized for forecasting. Through its use, we aim to increase efficiency in forecasting projects. By combining gathered experience and expertise, the tool will offer guidelines for modeling, pre-processing, and feature engineering, among other topics. It will be designed to be used by both customers and employees to quickly and easily obtain estimates and methodological recommendations. An initial test version will be available soon for internal use, but the tool is ultimately intended to be made accessible to external users as well. In addition to the technical output currently in development, a less technical output will also be available. The latter will focus on the most important aspects and their associated efforts. In particular, the business perspective in the form of expected efforts and potential trade-offs between effort and benefit will be covered by this.

 

 

Benefit from our forecasting expertise!

If you need support in addressing the challenges in your forecasting projects or have a forecasting project planned, we are happy to provide our expertise and experience to assist you.