With data-driven applications making tremors within the software technology landscape, big data and AI has considerably transformed search; equipping it with new data types, sources, and query methods. Riding this wave of transformation while spearheading innovative methodologies, KMW Technology marks its foundation. As a developer of search-based applications, KMW Technology leverages big data, deep learning and Natural Language Processing (NPL) tools to create effective search applications. Over the years, KMW Technology has positioned itself as a one-stop-shop with a strong foundation in enterprise search—perceiving that the modern-day search constitutes of knowledge rather than mere keywords. The company architects and implements solutions to difficult search-related problems. Brian Nauheimer, Senior Project Manager, KMW Technology says, “It’s a boutique where people can deep dive on search issues and solutions, not just for regular enterprise problems, but also for startups trying to bring search into their applications. We help them identify and integrate new technologies to enhance their usage of search to solve business problems.” KMW follows a delivery model of extracting structure from unstructured client data. This data extraction is a key component in unifying these unstructured assets with existing structured data. This enables the client to effortlessly to blend high-performance searching and analytics across the unified data. “So not only do we see search as a text application, but we also see it as a basic engine that provides analytics on top of your data,” adds Kevin Watters, Founder, KMW Technology.
Speculating on the complexities of unifying the data format where silos and legacy systems constitute a primary challenge, Nauheimer states that the fundamental concept of KMW Technology is to completely breakdown the data silos.
One of our general mantras is ‘complexity at index time is a trade-off for simplicity at search time'
In order to do that, the company goes through a daunting exercise of mapping within the silos to form common schema that represents the data in the organization. “One of our general mantras is ‘complexity and extra work up-front at the index time is trade-off for simplicity at search time’,” he adds. The ultimate yield achieved later is a search that is simpler, faster, and easier to query which also simplifies the complexity of user interfaces, is more performant and maintainable in the long run. Extending beyond unified information access, search quality factor is also highly determined by content enrichment. KMW Technology makes its clients capable of creating a uniform indexes with consistent content enrichment, returning exceptional search results and enabling powerful analytics in the same solution.
After gaining traction through enterprise search applications, KMW Technology is a strong believer in open source technologies. The company is focused on providing open source search solutions working around Solr, ElasticSearch and many other open source technologies. These open source technologies are as full featured as their commercial counterparts and can often deliver the functionality at a fraction of the cost.
According to Kevin, open source is more advantageous than commercial vendors, as the open source community is exceptionally innovative in its approach. The engineers at KMW Technology frequently contribute their part to the community where they recently developed a search engine based Graph Query which allows the user to search not only documents but also arbitrary relationships and to traverse relationships between documents. Through this transformative step for search engines, KMW Technology has significantly bridged the gap between traditional relational database and search engines. Working with search solutions, the company compares these solutions with databases, while eliminating voids in functionalities. In addition to that, KMW Technology also engages in relevancy tuning process by interviewing the clients to understand what they precisely need out of their search engine before the entire implementation process.
Sharing his views on the ever-expanding ocean of unstructured data and uncountable data sources, Kevin underlines the vital role of deep learning and neural network models in the effective classification of data and interpolation of missing data points. KMW Technology is currently on a trajectory where deep learning is a key part of the core strategy for the firm. “Using search in a non-conventional way is the competency that we offer and we will continue to reinforce it through innovation in the future,” concludes Watters.