GenAI language assistants

From documents to business impact

Accurate, secure, on premise, trustworthy, enterprise grade

Experiment, build, embed, run and maintain
GenAI language assistants.

genai-language-assistants-rag-chatbot
  • Customized conversational RAG
  • Enriched with metadata
  • View source documents
  • Secure on-premise deployment
  • Run LLMs locally
  • Single sign-on and REST API
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language-assistants-low-code-environment
  • Rich set of prepackaged NLP techniques
  • Experiment and customize AI pipelines
  • Large Language Model (LLM) agnostic
  • Quality assessment
  • Model finetuning
  • Feedback loops
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REFERENCES

NEWS

LLM On-Premise: The Complete Guide to Deploying Large Language Models Locally

Posted on
In today’s data-driven world, businesses are increasingly leveraging Large Language Models (LLMs) to unlock new opportunities in automation, decision-making, and customer engagement. However, for organizations in regulated industries or those prioritizing data privacy, deploying LLMs on the cloud isn’t always the best solution. Enter on-premise LLM deployment—a secure, customizable, and cost-effective alternative. This guide explores […]

Kairntech text annotation tool: unlocking the future of AI training

Posted on
In the rapidly evolving world of artificial intelligence, Kairntech stands as a pioneer in text annotation, offering a transformative approach to AI training. Withg high-quality labeled datasets, Kairntech empowers businesses to refine their machine learning models with precision and efficiency. This tool is not just about annotation; it’s about unlocking the full potential of AI […]

NLP Extraction – Techniques, Applications, and Tools

Posted on
In the ever-evolving landscape of Natural Language Processing (NLP), extraction plays a crucial role in structuring textual information. By identifying key entities, facts, and structured elements within a document or across datasets, this technique powers knowledge graphs, intelligent search systems, and automated decision-making. From rule-based approaches to deep learning models, extraction has evolved into a […]

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