High-End AI Solutions Tailored to Every Use Case
We develop bespoke AI solutions tailored to your business.
Our core areas include semantic text analytics (NLP), text, data and document generation (NLG), large language models (LLMs), machine learning (ML), and artificial intelligence (AI).
Since 2011, our team of computational linguists, data scientists, data engineers, and software developers has been successfully operating in the market.
Text Analytics (NLP)
Automatically Identify Connections with Text Analytics
Text Analytics or text mining utilises a plethora of methods from computational linguistics and artificial intelligence in order to convert unstructured textual data into structured information. Specifically, patterns and structures are extracted from input texts based on lexical properties, syntactic structures, statistical observations and machine learning with the overall aim of gaining deep semantic insights from textual input
Machine Learning (ML)
Aligned with Your Objectives: From Classical Machine Learning and Deep Learning to Large Language Models
Depending on the project goal and context, LangTec employs a wide range of machine learning methods. We use both unsupervised learning techniques — for example, in document clustering, topic modelling, or the creation of vector-based word and language models — and supervised learning techniques, which are applied in tasks such as text and document classification or targeted information extraction. In the deep learning context, we develop customised neural network architectures and, where necessary, deploy pre-trained AI models. In our research-driven projects, we explore new application areas for techniques such as reinforcement learning, transfer learning, and model distillation.
Artificial Intelligence (AI)
AI has been at the core of our project and product development since 2011.
The term ‘artificial intelligence’ is an umbrella concept encompassing all of LangTec’s activities in project and product development. We build solutions that solve real-world problems intelligently.
We hold our solutions to the standard that they significantly improve the quality and efficiency of processes beyond the level of human decision-making. In our view, the term ‘artificial intelligence’ is only truly applicable if the resulting solution solves the task measurably better than a human could. Although artificial intelligence, machine learning, and large language models (LLMs) are closely interconnected today and often used interchangeably, we regard artificial intelligence as a broader concept.
Text Generation (NLG)
Machine-Generated Content from Dynamic Data Sources
With the availability of large language models (LLMs), the automated generation of journalistic content from structured data has almost become a commodity. However, generative language models do not offer full control over the content they produce. For use cases where complete control over generated content is required, alternative approaches such as LangTec’s document generation solution, TextWriter, are more suitable.
LangTec’s solution, TextWriter, provides full content control and generates texts that can be optimised according to various parameters, such as SEO relevance, readability, text length, target audience, or output medium. Typical use cases for TextWriter include domains with high data fluctuation or breadth, such as product data in e-commerce, real-time weather or stock market reports, local news, or sports coverage.
Data and Document Generation
The Turbocharger for Machine Learning: Automated Generation of Fully Annotated Test and Training Data
Over the past ten to fifteen years, machine learning has made available learning methods whose increased performance comes at the cost of a significantly higher demand for annotated training data. In many practical applications, fully annotated test and training data are often not available in sufficient quantities due to data protection, copyright restrictions, or insufficient manual annotation.
The use of synthetically generated training data can help close these data gaps, enabling the creation of fully converged machine learning models that perform robustly in real-world applications.
LangTec’s Document Creator therefore offers the ability to generate large volumes of test and training data with great variability starting from just a single example document. This allows machine learning methods to be robustly trained, evaluated, and tuned even with initially limited training data before going live.
Knowledge Representation
Structured Knowledge for Smart Systems
Structured knowledge representations—formal mappings of the qualitative, domain-specific relationships—have gained tremendous importance in applications over the past ten years, not least because it has become technically feasible to efficiently adapt and deploy large-scale knowledge representations. Structured knowledge representations, such as ontologies, knowledge graphs, or triple stores, make the modeled domain knowledge accessible in a structured way for machine processing, and thus significantly contribute to improving result quality in deep semantic analysis.
