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AI is the abbreviation for artificial intelligence and refers to a range of computer systems that are trained to simulate human tasks such as learning, problem-solving, language comprehension, and decision-making.

In addition to high-performance computers, AI requires enormous amounts of data to “feed” advanced and complex algorithms that identify patterns and correlations. Keep in mind, however, that any AI model is only as good as the data provided to it during training.

Yes, absolutely. As in all other areas of business, high-quality data leads to accurate predictions and outcomes, greater efficiency, and fewer distortions and less waste of resources. Ultimately, there is a clear link between excellent data quality and better business results as well as lower operating costs.

More importantly, clean, balanced, and representative datasets prevent AI models from creating bubbles that produce unfair or discriminatory results.

There is narrow AI, which is used for specific tasks; generative AI, which creates new content—whether text, images, or even code; and general AI, which possesses human-like intelligence—a level that has not yet been achieved.

Some experts believe that artificial general intelligence (AGI) could be achieved as early as 2026, while others say it will take decades, and some even believe it will never be realized.

AI agents are specialized digital assistants that perform tasks independently. They use AI to understand information, make decisions, and automate processes. Unlike traditional software, they operate flexibly, learn from data, and can be customized to meet your specific needs.

Generative AI does more than just analyze data. It is the type of artificial intelligence that creates new content—whether text, images, audio, or video.

Generative AI models are also trained on large amounts of existing data and learn the underlying patterns, structures, and relationships within that data in order to generate new content in response to user input.

ML is a branch of AI that uses algorithms to process large amounts of historical data. Unlike traditional software or applications, ML learns independently from increasingly large datasets without requiring explicit programming.

Within large datasets, algorithms can identify patterns, trends, and correlations. Common applications of ML include fraud detection and image recognition.

LLM stands for "Large Language Model." LLMs are trained on enormous amounts of text, enabling them to understand language, generate text and code, answer questions, process requests, translate text from one language to another, condense lengthy documents into short, concise summaries, or analyze large text-based datasets to identify insights and trends.

Well, the honest answer is: it depends. Developing an AI model—from the initial research through training and refinement—requires not only time, budget, discipline, and perseverance.

More importantly, it requires qualified personnel with in-depth technical expertise in areas such as model architecture, optimization, and data handling. Instead of starting from scratch, you should consider using a pre-trained model, such as AKHET® Local AI, and customize it to your specific needs using your own data.

When employees use AI tools without authorization from management or the IT department, this is referred to as shadow AI. Using personal accounts on public AI platforms carries many risks, such as security breaches, compliance violations, and data protection violations, as well as data leaks. Shadow AI includes public AI tools, third-party AI plugins and agents, or any SaaS application with built-in AI capabilities.

When your AI “hallucinates,” it provides answers that sound plausible but are incorrect. AI hallucinations are mainly caused by insufficient or low-quality training data, flawed logic, or incorrect assumptions in the algorithms. Consider, for example, queries about historical facts that appear to be true but are actually incorrect.

AI bias is a systematic error. If training data contains biases, this leads to errors in the AI’s decision-making process that disadvantage or treat certain demographic groups unfairly.

A workflow describes the individual steps of a process. With AI, these workflows can be intelligently automated so that the AI recognizes what needs to be done and performs the tasks without human intervention.

AI ingest describes a critical process in building an AI model from scratch. Raw data must be collected from various sources, transformed, and loaded.

Typically, the data feed includes both structured and unstructured data. Another important step in the AI ingestion process is preparing the data through cleaning, validation, and structuring.

AI inference puts a trained AI model into practice by making decisions, classifications, or predictions based on new, previously unknown data. In this phase, the AI delivers value by using learned patterns to respond to new, real-world inputs. For example: An AI model was trained on thousands of images of dogs. When it recognizes a dog in a completely new photo, that is inference.

RAG stands for Retrieval-Augmented Generation and is essential for providing accurate and up-to-date information and supporting answers with facts, thereby reducing AI hallucinations.

A RAG pipeline enhances an LLM’s ability to generate accurate and relevant responses by connecting it to external knowledge sources. The process begins by dividing external data into smaller segments and converting them into numerical representations (“embeddings”), which are then stored in a vector database.

The original user query is then combined with the retrieved text segments to create an expanded prompt, which enables the LLM to provide a more specific and relevant response.

Private AI ensures data privacy and security when using AI in the enterprise. Unlike on-premises AI, data remains within your controlled infrastructure, but does not necessarily have to reside on a single on-premises machine. Private AI can be deployed on-premises, in a private cloud, or in a hybrid environment.

When AI operations are performed on a local device or network, this is referred to as Local AI. All datasets are processed and stored entirely on your own hardware.

We use open-source models that can be reviewed and fine-tuned, allowing you to monitor bias in the results at any time and take appropriate action. Since the data remains private, you have full control over the training datasets, which is essential for avoiding the adoption of external biases. Transparency and ethical guidelines are important to us—the responsibility for AI bias lies with you.

Traditional automation follows fixed rules, such as the classic "if-then" principle. AI agents, on the other hand, respond dynamically, learn from experience, recognize patterns in data, and can make complex decisions. This makes AI agents significantly more flexible and versatile.

We keep all data processing strictly internal to prevent any leakage of personal data, such as names, financial information, health data, or login credentials.

While integrated anonymization tools—such as those offered by specialized providers (e.g., Private AI, which identifies and anonymizes PII across languages and entities)—are not included, users can integrate such features or perform their own preprocessing to ensure compliance.

This approach prioritizes data management and encryption and meets heightened data protection requirements.

Deep learning is a branch of machine learning inspired by the human brain. Deep learning uses multilayer neural networks to recognize complex patterns in images, text, and sounds, which can lead to highly accurate insights and predictions.

The term "deep" refers to the number of layers in the networks, which can easily number several hundred. An example of deep learning is an app on your smartphone that instantly provides you with a translation of a street sign in a foreign language.

Yes, of course. Integration with identity providers (SSO, LDAP) and data sources (document repositories, databases, SharePoint, and more) is supported.