Our mission at Kairntech is to create business impact from documents. Trust, accuracy and human-in-the-loop continuous improvement of language assistants all contribute to reach this goal. Ultimately, the value obtained from documents enhances the collective intelligence of organizations.
Even though Large Language Models (LLMs) are getting better all the time, you cannot rely solely on these models to create sustainable business value. Hallucinations, consistency over time and missing links to information sources all contribute to the fact that few GenAI intiatives actually make it to production. This concerns in particular the more complex natural language initiatives.
Build trust and accuracy with context
At Kairntech we believe that context and metadata anchor GenAI models in reality. Internal documents, knowledge bases such as Wikidata and business vocabularies all contribute to enhance trust in the accuracy of GenAI language initiatives.
Down-to-earth tasks such as document conversion, dataset creation, extraction of entities or relations, building classification or entity recognition models are needed to create relevant and trustworthy context. There is no magic bullet here, the best results are obtained by getting your hands dirty.
Kairntech Studio, our low code platform accessible to Subject Matter Expert and Data Scientist, allows you to create high quality dataset, train AI models and design pipelines. Studio contains a wide range of pre-packaged technologies, off-the-shelf models and clever tools such as Active Learning.
Continous improvement of AI models with human-in-the-loop features is also an important ingredient to build trust over time.
Finally, for question answering (RAG) use-cases leveraging Large Language Models, the context provided by showing the original document sources contributes to trustworthiness.
The customization imperative
GenAI and Large Language Models are only a component in the chain of technologies needed to derive sustainable business value from documents. It is of vital importance to customize the elements within this chain (AI pipelines).
The capacity to retrieve the right information from documents depends on many factors. Even basic elements such as the way how you search (a keyword or a complex question, broad or narrow scope) or what you search (a summary, a list…) have a strong impact.
An extensive range of technology components need to be integrated, customized and combined. These include document conversion, chunking, indexation, vectorization, prompting, information extraction, text classification. Search methodologies have a big impact. The underlying language models as well. The latter can be customized as well.
Customization maximizes accuracy and ultimately trust within a specific and often unique context. For more details, discover our whitepaper.
The GenAI NLP factory
Creating customized AI Pipelines is one thing. Industrializing these pipelines is quite another. Kairntech Server is build to scale GenAI language assistants securely. This server can be deployed on premise, in private clouds as well as in B2B SaaS mode.
The integration of single sign-on and the capacity to deploy on a distributed environment ensure scalability in a secure manner.
Finally, a rich REST API enables existing business applications to benefit from GenAI language assistants.
Conclusion
With Kairntech AI data teams, software vendors and system integrators can build a large amount of customized GenAI language assistants and deliver trusted and tangible business impact from documents.
The Kairntech Team