iKVA is an early-stage company creating AI-enabled knowledge management software solutions. Our software is based on vector mapping technology which, along with other proprietary techniques, enables us to index highly unstructured data from multiple systems, in multiple formats, and with conflicting or non-existent taxonomies or meta-data.
Once in our system, information from multiple sources can then be accessed by users as if it was a single source, which enables them to access significantly more knowledge. Our use of vector mapping to index information makes it language-agnostic and removes the need for expensive translation. This is particularly useful for large organisations that create documents in multiple languages on a daily basis, or for individuals, such as patent attorneys, who access information from databases across the globe.
How did you come up with the idea for the company?
My post-doctoral researcher, Dr Liang Wang, was interested in learning more about neural networks and machine learning but found it challenging to discover relevant information for his studies using traditional keyword search. “Google-esque” search works well for basic searches but when undertaking academic research it returns a high volume of irrelevant results.
This inspired Liang to develop his own solution that would display relevant pages from Wikipedia when he was reading articles and it is this semantic search service that is at the heart of iKVA.
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How has the company evolved over the last couple of years?
Since 2018, iKVA has evolved from a team of three people exploring the potential of a tremendously interesting idea to 14 people delivering a novel solution to customers at enterprise scale.
Our AI data discovery solutions have been adopted by globally recognised names, including The University of Cambridge and Mott MacDonald during this time
What can we hope to see from iKVA in the future?
The continuing march of enterprises towards the cloud, and the ever-growing levels of support for API access to existing services, create opportunities to expand the sources of data we can ingest and the integrations we can create.
The continual evolution in the capability of Large Language Models to interpret, and understand, language is another exciting area of technology research that we will make use of and that will also continue to enhance the performance of our solutions.
As well as expanding into new sectors and geographies, we are continually developing our core technology to improve the overall customer experience. Our close links with the University of Cambridge Computer Laboratory and the Alan Turing Institute greatly support this.
Since we have such a strong technology platform, we are faced with the challenge of choosing the right opportunity for us to pursue – which is a positive challenge to have!