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Use the full potential of artificial intelligence in your company.


Artificial Intelligence

Generative AI is not just a nice-to-have, but serves as the basis for future business success. Mindbreeze InSpire provides the facts for the secure use of Large Language Models (LLM) in the enterprise.


Accelerated knowledge gain

Mindbreeze InSpire uses various AI methods to reduce the time and effort required to gain insights from information.


Personalized and contextualized results

Through artificial intelligence, such as deep learning and large language models, Mindbreeze InSpire interacts with employees and customers in a highly personalized manner.


Process automation

Mindbreeze InSpire streamlines, simplifies, and automates time-consuming business processes for your employees.


Quick answers with the Mindbreeze InSpire AI Chat

Mindbreeze InSpire and Large Language Models

Open standards

Free choice of LLMs

Whether GPT from OpenAI, Llama2 from Meta, or models from Huggingface, you can combine Mindbreeze InSpire with the Large Language Model of your choice and generate answers and insights from internal company data, remarkably similar to humans. Mindbreeze trusts and supports open standards such as ONNX (Open Neural Network Exchange).


Comprehensible answers from LLMs

Ensure data protection in your company easily. You choose which data your LLM uses to generate content and avoid hallucinations - i.e. answers that sound convincing but may be incorrect. Displayed sources allow you to check fact-based answers and guarantee precision at all times.


Continuous improvement through learning

Benefit from the ongoing refinement and optimization of results based on experience from past human interactions. Mindbreeze InSpire learns from you and adapts more and more to your needs.
The queries and behavior of your employees are not transmitted to and evaluated by third-party providers. 


Relevance Model

Using a relevance model based on machine learning and neural networks, Mindbreeze InSpire analyzes user behavior (previous searches, interactions with hits) to predict which content is relevant.

Learn more 

Jakob Praher

Generative AI (GenAI) and tools like ChatGPT have taken the world by storm. However, in order for these technologies to be used professionally in companies, numerous challenges must be overcome - for example, data hallucination, lack of data security, authorizations, critical intellectual property issues, expensive training costs, and technical implementation with confidential company data. Mindbreeze InSpire solves these challenges and forms the ideal basis for making Generative AI a fit for corporate use.

Jakob Praher, CTO Mindbreeze

Generative AI Demo

See for yourself

See how generative AI and natural language question answering (NLQA) is made possible in every Mindbreeze InSpire Insight App through Language Prompt Engineering technology.

Natural Language Question Answering

Mindbreeze InSpire NLQA Use Case

Retrieval Augmented Generation

Mindbreeze InSpire RAG Use Case

Frequently asked questions

Large Language Models (LLMs) are models in the field of AI. LLMs are trained with a large amount of content to “understand” natural language and generate human-like output and dialog.
Foundation models are large machine learning models in the field of generative AI that are trained with a variety of data and optimized for a wide range of applications.
Large Language Models (LLMs), such as GPT, should be used where they are really effective. For example, LLMs generate texts, provide additional ideas for brainstorming, translate and create summaries. These tools provide a certain level of added convenience and, when used correctly, make tasks at work much easier.
Prompt engineering is the process of designing instructions given to Large Language Models (LLMs) to get the most valuable output. Proper prompts guide the LLM towards producing accurate, relevant, and contextually appropriate answers. Prompt engineering is crucial for the improvement of LLM performance.
RAG, or retrieval-augmented generation, is a method that enhances the quality of generated texts of Large Language Models. With this method, relevant information is accessed from a database or a set of documents, and then text-based information is generated based on this retrieved information.
In data hallucination, Large Language Models generate incorrect answers that appear plausible due to coherent and fluent texts. These generated answers may lack completeness, contain outdated information, or even include false statements, potentially leading companies astray and prompting incorrect and erroneous decision-making.
To overcome data hallucination, a company can combine company knowledge extracted by an insight engine with the broader linguistic comprehension of a Large Language Model (LLM). This integration allows for a more detailed analysis, ensuring that generated outputs are not only linguistically coherent but also grounded in accurate and relevant company-specific information. Moreover, the insight engine will provide the factual basis for the answer, so that any answer can be validated easily.
Knowledge graphs are representations of knowledge within a company or other entity that is presented in a graph. Users can see how information is organized and connected from various sources, providing a comprehensive view of how different topics are related. The rich semantics help especially with information retrieval to understand intent and learn how entities are related, enabling meaning-based computing.
Vector search is a technique used in information retrieval to find items that are semantically similar to a given query item. In contrast to conventional keyword-based search systems that only match exact words or phrases, vector search considers the semantic meaning and context of the search query. With any vector search, the quality and context of the vector are crucial for high-quality results, therefore compelling semantic pipelines are highly relevant in such a context to analyze and prepare the information accurately.
Natural Language Processing (NLP) focuses on the interaction between computers and humans through natural language. It enables software to understand and analyze human language as well as respond and generate human-like texts – for example, in chatbots or large language models (LLMs).
An AI model as a service is a cloud-based service that provides access to pre-trained artificial intelligence (AI) models. Mindbreeze uses these AI models to generate answers from internal company data in a safe and secure way.
White Paper - LLM - Large Language Models Poster


Contact us

AI is no longer a nice-to-have!
Let's talk.

Our team will be happy to answer your questions about Mindbreeze InSpire. In this exclusive white paper, you will learn how Large Language Models (LLMs) can help you overcome data and knowledge silos and how to safely use generative AI for critical business decisions.
You will receive the download link to the white paper by e-mail after completing the form.