GPT for

Unlock your data's potential with AI, all while prioritizing data security and sovereignty.

Use Cases

Boost Efficiency

Employees spend 19% of their time searching and and gathering information. Language model can assist at a variety of tasks, increasing the efficiency and accelerating the productivity of knowledge workers.

Semantic search is a search technique that utilizes Natural Language Processing (NLP) and machine learning algorithms to understand the meaning behind search queries and the context in which words are used. It takes into account synonyms, related concepts, and the relationship between words to provide more accurate and relevant search results. This helps users to find what they are looking for more easily and efficiently.

Question answering in the context of Natural Language Understanding (NLU) involves using machine learning algorithms and NLP techniques to understand natural language questions and provide relevant and accurate answers. The system analyzes the question and searches for relevant information from a knowledge base or corpus of text, and then generates an answer that is most appropriate based on the context and language used in the question. This technology is useful for a wide range of applications, including customer support, virtual assistants, and information retrieval systems.

Prompting and instructing in the context of Large Language Models (LLMs) involves providing a specific context or task to the language model and asking it to generate relevant text based on that input. This is done by providing a set of instructions or a prompt that specifies the desired output, and the language model generates text based on that prompt. This technology can be used for a variety of applications, including text generation, chatbots, and language translation, among others. The idea is to provide a more human-like interaction with the system, where the user can provide specific input and receive an appropriate response generated by the language model, e.g. "Extract key figures from the report and summarize them".

IDP, or Intelligent Document Processing, refers to the use of NLU and machine learning algorithms to automatically extract and classify information from various types of documents, such as invoices, contracts, and forms, to reduce the time and effort needed for manual processing and improve the accuracy and efficiency of data extraction. An example is removing sensitive user informations from text.

Is this report compliant with our firm’s policies?

The report contains sensitive data which according to own firm’s data protection policy disallows redistribution.

Remove sensitive data from the report

Your Data

Use AI with your data.

Your Infrastructure

On-Premise or Community-Cloud


Tailormade for your Corporation

Cortecs AI offers custom natural language understanding (NLU) solutions tailored to your specific business needs. By utilizing your own data and infrastructure, our NLU models learn your about your specific domain, providing more accurate and insightful results. You can maintain full control over your data and ensure the highest level of security by deploying our solutions on your own infrastructure.

With the right partners, anything is possible.

Seamlessly integrate

Accelerate AI Usage across your Corporation

Cortecs AI can be easily integrated with existing data storage systems such as SQL, No-SQL or object storage. This allows businesses to leverage their existing infrastructure and data, without having to go through any major changes or migrations.
Additionally, Cortecs AI provides an API interface that enables its integration into existing corporate routines, making it an excellent addition to any digital transformation strategy.

Whats the difference between Cortecs AI and ChatGPT?

The main difference between Cortecs AI and ChatGPT is that Cortecs AI is able to provide answers based on your custom data. It designed to be deployed on-premise, meaning that it can process sensitive data without that data being forwarded to third-party servers. In contrast, ChatGPT is a proprietary language model developed by OpenAI that relies on cloud-based servers to operate. As a result, data must be forwarded to OpenAI servers.

What AI model is Cortecs using?

Cortecs AI is based on open-source Language Models, and the nodel used varies depending on the specific use case and server prerequisites. Cortecs offers different models, ranging from 7 billion to 176 billion parameters. The choice of the specific variant will depend on the company's specific needs, the amount of data and potential time constrains.

How much does Cortecs AI cost?

Cortecs offers customized solutions tailored to each individual client's needs, which means that the cost of Cortecs AI will depend on the specific requirements of each project. The company offers a free consultation to assess the needs of potential clients and provide an estimate for the cost of their services. If you are interested, it is recommended to contact the company directly and discuss your project's requirements.

How does Cortecs AI work?

Cortecs AI has a large language model (LLM) at its core, with several additional layers. These layers include a data integration layer, caching and load-balancing to increase performance, tools for building custom document processing workflows, corporate-wide authentication for departments and individuals, and an API for easy accessibility.

Frequently Asked


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