Our mission at Kairntech is to enhance enterprise productivity with AI language assistants. While many copilots in the market cater individually designed, office-centric use-cases, Kairntech assists enterprise teams with solutions that are aligned with complex workflows.
With teams becoming more productive and by making documents easier accessible, it becomes possible to bridge intra-company silos by adding structured data sources and enterprise applications through tools and agents. And this brings us to a second objective: enhance collective intelligence with tailored AI applications.
With a full-stack AI solution ready to be deployed in SaaS or On-premise environments, Kairntech ensures attractive and optimized run costs for LLM-enhanced workloads. Delivering an optimal price-performance ratio is a third objective.
LLM-centric versus a hybrid approach
Large Language Models have gone mainstream with the introduction of Chat-GPT in 2022. It is tempting to prioritize working exclusively on LLMs by fine-tuning, extensive prompting and adding ever more context.
However, at Kairntech we promote a hybrid approach whereby LLMs are only part of a much broader solution; traditional NLP (Natural Language Processing) techniques can be efficient, cost-effective and well adapted to many use cases. LLMs may or may not be used, but they are part of highly customizable processing pipelines that also contains models, business vocabularies, knowledge bases and a wide range of technical components.
Satisfied customers with question answering systems and chatbots in production have taught us that this hybrid approach boosts accuracy and reduces costs.
It takes more than trustworthy answers
Obviously the level of accuracy is a prerequisite for any AI language initiative. However, there are several other arguments that come into play and also explain why so many AI initiatives today do not make it to production:
- Exploiting confidential documents requires an On premise deployment, which often includes the local installation of an LLM.
- Deployments needs to be enterprise-ready which includes features such as Single Sign-On, scalability on distributed environments, fine-grained role definitions, seamless access to content management systems such as Sharepoint and a REST API to integrate within existing applications.
- To scale use-cases within the enterprise it is required that teams work in full autonomy. This is the reason why we designed a No-code design Studio that allows domain experts and business owners to reduce dependency on IT. The evaluation of quality is part of this Studio and as a company we are backed-up by several EU-funded research projects.
- Last but not least, the Kairntech team has been working together for more than 25 years on language technologies. We have the domain expertise to provide advise on the best mix of technologies on any use case.
Conclusion
With Kairntech AI agent solutions for enterprise teams, software vendors and system integrators can build in full autonomy a large number of customized language assistants and chatbots to deliver productivity gains, enhance collective intelligence while ensuring cost-effective LLM-workload performance.
The Kairntech Team