Artificial Intelligence in Treasury Management

Common Mistakes in the Development of AI Assistants

How fortunate that people make mistakes: because we can learn from them and improve. We have closely observed how companies around the world have implemented AI assistants in recent months and have, unfortunately, often seen them fail. We would like to share with you how these failures occurred and what can be learned from them for future projects: So that AI assistants can be implemented more successfully in the future!

Let's build an Enterprise AI Assistant

In the previous blog post we have talked about basic principles of building AI assistants. Let’s take them for a spin with a product case that we’ve worked on: using AI to support enterprise sales pipelines.

ChatGPT & Co: LLM Benchmarks for September

Find out which large language models outperformed in the September 2024 benchmarks. Stay informed on the latest AI developments and performance metrics.

Open-sourcing 4 solutions from the Enterprise RAG Challenge

Our RAG competition is a friendly challenge different AI Assistants competed in answering questions based on the annual reports of public companies.

The Intersection of AI and Voice Manipulation

The advent of Artificial Intelligence (AI) in text-to-speech (TTS) technologies has revolutionized the way we interact with written content. Natural Readers, standing at the forefront of this innovation, offers a comprehensive suite of features designed to cater to a broad spectrum of needs, from personal leisure to educational support and commercial use. As we delve into the capabilities of Natural Readers, it's crucial to explore both the advantages it brings to the table and the ethical considerations surrounding voice manipulation in TTS technologies.

Jörg EgretzbergerBlog

8 tips for developing AI assistants

AI assistants for businesses are hype, and many teams were already eagerly and enthusiastically working on their implementation. Unfortunately, however, we have seen that many teams we have observed in Europe and the US have failed at the task. Read about our 8 most valuable tips, so that you will succeed.

Workshop

AI Workshops for Companies

Whether it's the basics of AI, prompt engineering, or potential scouting: our diverse AI workshop offerings provide the right content for every need.

So You are Building an AI Assistant?

So you are building an AI assistant for the business? This is a popular topic in the companies these days. Everybody seems to be doing that. While running AI Research in the last months, I have discovered that many companies in the USA and Europe are building some sort of AI assistant these days, mostly around enterprise workflow automation and knowledge bases. There are common patterns in how such projects work most of the time. So let me tell you a story...

Database Analysis Report

This report comprehensively analyzes the auto parts sales database. The primary focus is understanding sales trends, identifying high-performing products, Analyzing the most profitable products for the upcoming quarter, and evaluating inventory management efficiency.

Part 1: Data Analysis with ChatGPT

In this new blog series we will give you an overview of how to analyze and visualize data, create code manually and how to make ChatGPT work effectively. Part 1 deals with the following: In the data-driven era, businesses and organizations are constantly seeking ways to extract meaningful insights from their data. One powerful tool that can facilitate this process is ChatGPT, a state-of-the-art natural language processing model developed by OpenAI. In Part 1 pf this blog, we'll explore the proper usage of data analysis with ChatGPT and how it can help you make the most of your data.

Part 4: Save Time and Analyze the Database File

ChatGPT-4 enables you to analyze database contents with just two simple steps (copy and paste), facilitating well-informed decision-making.

5 Inconvenient Questions when hiring an AI company

This article discusses five questions you should ask when buying an AI. These questions are inconvenient for providers of AI products, but they are necessary to ensure that you are getting the best product for your needs. The article also discusses the importance of testing the AI system on your own data to see how it performs.

Part 3: How to Analyze a Database File with GPT-3.5

In this blog, we'll explore the proper usage of data analysis with ChatGPT and how you can analyze and visualize data from a SQLite database to help you make the most of your data.

Matus ZilinskyBlog

Creating a Social Media Posts Generator Website with ChatGPT

Using the GPT-3-turbo and DALL-E models in Node.js to create a social post generator for a fictional product can be really helpful. The author uses ChatGPT to create an API that utilizes the openai library for Node.js., a Vue component with an input for the title and message of the post. This article provides step-by-step instructions for setting up the project and includes links to the code repository.

Strategic Impact of Large Language Models

This blog discusses the rapid advancements in large language models, particularly highlighting the impact of OpenAI's GPT models.

Felix KrauseBlog

License Plate Detection for Precise Car Distance Estimation

When it comes to advanced driver-assistance systems or self-driving cars, one needs to find a way of estimating the distance to other vehicles on the road.

LLM Performance Series: Batching

Beginning with the September Trustbit LLM Benchmarks, we are now giving particular focus to a range of enterprise workloads. These encompass the kinds of tasks associated with Large Language Models that are frequently encountered in the context of large-scale business digitalization.

Data Science, Artificial Intelligence and Machine Learning

For some time, Data Science has been considered the supreme discipline in the recognition of valuable information in large amounts of data. It promises to extract hidden, valuable information from data of any structure.

Business Intelligence

Business Intelligence (BI) is a technology-driven process for analyzing data and presenting usable information. On this basis, sound decisions can be made.

Operationalization of Data Science (MLOps)

Data and Artificial Intelligence (AI) can support almost any business process based on facts. Many companies are in the phase of professional assessment of the algorithms and technical testing of the respective technologies.

IBM Watson Knowledge Studio

In IBM Watson Knowledge Studio, you train an Artificial Intelligence (AI) on specialist terms of your company or specialist area ("domain knowledge"). In this way, you lay the foundation for automated text processing of extensive, subject-related documents.

Demand Planning, Forecasting and Optimization

After the data has been prepared and visualized via dashboards and reports, the task is now to use the data obtained accordingly. Digital planning, forecasting and optimization describes all the capabilities of an IT-supported solution in the company to support users in digital analysis and planning.

IBM Cloud Pak for Data

The Cloud Pak for Data acts as a central, modular platform for analytical use cases. It integrates functions for the physical and virtual integration of data into a central data pool - a data lake or a data warehouse, a comprehensive data catalogue and numerous possibilities for (AI) analysis up to the operational use of the same.

IBM Watson® Knowledge Catalog/Information Governance Catalog

Today, "IGC" is a proprietary enterprise cataloging and metadata management solution that is the foundation of all an organization's efforts to comply with rules and regulations or document analytical assets.

IBM Watson Discovery

With Watson Discovery, company data is searched using modern AI to extract information. On the one hand, the AI uses already trained methods to understand texts; on the other hand, it is constantly developed through new training on the company data, its structure and content, thus constantly improving the search results.

Data Governance

Data Governance describes all processes that aim to ensure the traceability, quality and protection of data. The need for documentation and traceability increases exponentially as more and more data from different sources is used for decision-making and as a result of the technical possibilities of integration in Data Warehouses or Data Lakes.

Referenz

Standardized data management creates basis for reporting

TIMETOACT implements a higher-level data model in a data warehouse for TRUMPF Photonic Components and provides the necessary data integration connection with Talend. With this standardized data management, TRUMPF will receive reports based on reliable data in the future and can also transfer the model to other departments.

Application Integration & Process Automation

Digitizing and improving business processes and responding agilely to change – more and more companies are facing these kind of challenges. This makes it all the more important to take new business opportunities through integrated and optimized processes based on intelligent, digitally networked systems.

IBM Cloud Pak for Data – Test-Drive

By making our comprehensive demo and customer data platform available, we want to offer these customers a way to get a very quick and pragmatic impression of the technology with their data.

Referenz

Flexibility in the data evaluation of a theme park

With the support of TIMETOACT, an theme park in Germany has been using TM1 for many years in different areas of the company to carry out reporting, analysis and planning processes easily and flexibly.

Microsoft Azure

Azure is the cloud offering from Microsoft. Numerous services are provided in Azure, not only for analytical requirements. Particularly worth mentioning from an analytical perspective are services for data storage (relational, NoSQL and in-memory / with Microsoft or OpenSource technology), Azure Data Factory for data integration, numerous services including AI and, of course, services for BI, such as Power BI or Analysis Services.

Conception of individual Analytics and Big Data solutions

We determine the best approach to develop an individual solution from the professional, role-specific requirements – suitable for the respective situation!

IBM Planning Analytics mit Watson

IBM Planning Analytics with Watsons enables the automation of planning, budgeting, forecasting and analysis processes using IBM TM1.

Microsoft Azure Synapse Analytics

With Synapse, Microsoft has provided a platform for all aspects of analytics in the Azure Cloud. Within the platform, Synapse includes services for data integration, data storage of any size and big data analytics. Together with existing architecture templates, a solution for every analytical use case is created in a short time.

IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.

IBM Cloud Pak for Data Accelerator

For a quick start in certain use cases, specifically for certain business areas or industries, IBM offers so-called accelerators based on the "Cloud Pak for Data" solution, which serve as a template for project development and can thus significantly accelerate the implementation of these use cases. The platform itself provides all the necessary functions for all types of analytics projects, and the accelerators provide the respective content.

Daniel PuchnerBlog

Make Your Value Stream Visible Through Structured Logging

Boost your value stream visibility with structured logging. Improve traceability and streamline processes in your software development lifecycle.

IBM SPSS Modeler

IBM SPSS Modeler is a tool that can be used to model and execute tasks, for example in the field of Data Science and Data Mining, via a graphical user interface.

IBM InfoSphere Information Server

IBM Information Server is a central platform for enterprise-wide information integration. With IBM Information Server, business information can be extracted, consolidated and merged from a wide variety of sources.

Introduction to Functional Programming in F# – Part 8

Discover Units of Measure and Type Providers in F#. Enhance data management and type safety in your applications with these powerful tools.

Data Integration, ETL and Data Virtualization

While the term "ETL" (Extract - Transform - Load / or ELT) usually described the classic batch-driven process, today the term "Data Integration" extends to all methods of integration: whether batch, real-time, inside or outside a database, or between any systems.

IBM Watson Studio

IBM Watson Studio is an integrated solution for implementing a data science landscape. It helps companies to structure and simplify the process from exploratory analysis to the implementation and operationalisation of the analysis processes.

IBM Cloud Pak for Automation

The IBM Cloud Pak for Automation helps you automate manual steps on a uniform platform with standardised interfaces. With the Cloud Pak for Business Automation, the entire life cycle of a document or process can be mapped in the company.

Talend Application Integration / ESB

With Talend Application Integration, you create a service-oriented architecture and connect, broker & manage your services and APIs in real time.

Innovation Incubator at TIMETOACT GROUP Austria

Discover how our Innovation Incubator empowers teams to innovate with collaborative, week-long experiments, driving company-wide creativity and progress.

Introduction to Functional Programming in F# – Part 9

Explore Active Patterns and Computation Expressions in F#. Enhance code clarity and functionality with these advanced techniques.

Decision Optimization

Mathematical algorithms enable fast and efficient improvement of partially contradictory specifications. As an integral part of the IBM Data Science platform "Cloud Pak for Data" or "IBM Watson Studio", decision optimisation has been decisively expanded and embedded in the Data Science process.

Introduction to Functional Programming in F# – Part 2

Explore functions, types, and modules in F#. Enhance your skills with practical examples and insights in this detailed guide.

Introduction to Functional Programming in F# – Part 11

Learn type inference and generic functions in F#. Boost efficiency and flexibility in your code with these essential programming concepts.

Introduction to Functional Programming in F# – Part 4

Unlock F# collections and pipelines. Manage data efficiently and streamline your functional programming workflow with these powerful tools.

Introduction to Functional Programming in F# – Part 3

Dive into F# data structures and pattern matching. Simplify code and enhance functionality with these powerful features.

Introduction to Functional Programming in F# – Part 7

Explore LINQ and query expressions in F#. Simplify data manipulation and enhance your functional programming skills with this guide.

Introduction to Functional Programming in F# – Part 5

Master F# asynchronous workflows and parallelism. Enhance application performance with advanced functional programming techniques.

Introduction to Functional Programming in F# – Part 12

Explore reflection and meta-programming in F#. Learn how to dynamically manipulate code and enhance flexibility with advanced techniques.

Sebastian BelczykBlog

Composite UI with Design System and Micro Frontends

Discover how to create scalable composite UIs using design systems and micro-frontends. Enhance consistency and agility in your development process.

State of Fast Feedback in Data Science Projects

DSML projects can be quite different from the software projects: a lot of R&D in a rapidly evolving landscape, working with data, distributions and probabilities instead of code. However, there is one thing in common: iterative development process matters a lot.

Talend Data Integration

Talend Data Integration offers a highly scalable architecture for almost any application and any data source - with well over 900 connectors from cloud solutions like Salesforce to classic on-premises systems.