Kairntech provides advanced GenAI language assistants designed to enhance business processes through natural language processing (NLP). Built by a team with over 20 years of experience in NLP, Kairntech offers a low-code Kairntech Studio for experimentation and customization, along with the Kairntech Server for scalable deployment and continuous improvement. The platform allows companies to quickly prototype, fine-tune, and industrialize AI models, providing real business value by optimizing workflows and information retrieval.

What are the main features of Kairntech?

Fast Experimentation

Kairntech’s platform includes over 50 pre-packaged components for fast experimentation, enabling users to explore various AI tasks such as question-answering (RAG), information extraction, and text classification in an intuitive web environment.

• Ready-to-use tools like annotators and knowledge bases.
• Access to AI models and Large Language Models (LLMs).
• Supports diverse data sources, including scientific literature and Excel files.

Extensive Customization

With Kairntech, users can customize their AI solutions extensively. The platform offers tools to label text, fine-tune models, and improve performance through active learning, ensuring that language assistants are tailored to specific business needs.

• Build datasets faster with smart pre-annotations.
• Customize assistants with business vocabularies and domain-specific models.
• Fine-tune LLMs and embeddings models to improve accuracy and trust.

Seamless Deployment

Kairntech allows seamless deployment of language assistants through its Kairntech Server, either on-premise or as a SaaS solution. The platform integrates easily with existing applications via a rich REST API and supports distributed environments for large-scale operations.

• Embed assistants within applications through API.
• Run at scale with Single Sign-On support for enhanced security.

Continuous Improvement

Kairntech emphasizes continuous improvement by integrating feedback loops that retrain models and enrich knowledge bases, ensuring that the language assistants become more accurate and effective over time.

• Automate Knowledge Base enrichment.
• Retrain models based on real-world usage for improved performance.