Acrolinx came to life at the German Research Center for Artificial Intelligence, and has been developed by the world’s leading AI experts in Natural Language Processing. The company provides an AI-powered platform for enterprise content governance. Their customers use the solution to deliver consistent strategy-aligned content at scale, across multiple teams, projects, and locations. Following a three-stage process, the customers -define specific content guidelines to fit their goals by leveraging existing style and terminology guidelines and by running existing content through the Acrolinx engine and then adjusting the initially generated guidelines;create content aligned with the guidelines; Acrolinx uses AI technology that enables content contributors to shorten their editorial process with automation - content contributors can create or optimize their existing content with a Sidebar offering real-time feedback and suggestions along which to fix issues and improve the text;track and analyze the quality and performance of their content for optimization.The platform enables customers to create engaging, readable, consistent, and compliant content in an efficient and cost-effective way at scale. You can choose to watch this explainer video for the Acrolinx solution overview or this demo video for some more detailed product introduction. Some brands using the Acrolinx product are: Google, Amazon, Facebook, Microsoft, Sony, Volvo, Scania, Boeing, Siemens. Acrolinx has an OLTP relational database used by their transactional system. The relational database schema wasn’t designed for the purpose of running complex analytical queries against it. Acrolinx needed to mirror their existing OLTP into a new OLAP warehouse store so they could run analytical queries against their data much more efficiently.

The service was delivered to the US-based Software Innovation Labs - a consortium organization who serve software building clients with a broad spectrum of technical and domain expertise for even the most complex software development projects. The client who needed to further develop their software solution was PressedJuicery - a company active in the processing, production and distribution of premium juices. The client intended to implement a new sales flow process, which was to be supported with an existing legacy python / flask application.At its core, the sales-flow software solution delivered is a new python application built atop the existing legacy system. The approach enabled the client to implement and run the new sales flow process in a reliable and secure way. The sales application is comprehensively covered with unit tests and operates independently from the legacy system codebase; still the added functionality runs in the same context of one and the same software system.As regards integrations, the application was integrated with PixelPoint POS. We chose to implement the integration by creating a dedicated EOI Agent for the latter. The EOI Agent is installed in every store operated with the software system. The agent exposes a secure API which enables the customer to check for stock status and place the order. Technically, the python application communicates with the software agent installed in the selected store to conduct order placement.With the solution in place the customer is now free to order and pay for the products offered online and later collect the products at the store terminal, avoiding the hassle of shopping and queuing in the store. The order placed by the customer online is sent directly to the store terminal for execution upon the customer arrival. The client using the software solution is in a better position to drive their sales with a new sales flow channel.

Neutopia is an online education and publishing platform that invites a new type of learning experience. It caters to organizations who promote their resources/courses and learners who explore the subjects that interest them. The user is enabled to find the most relevant contents and aggregate playlists of their favourite educational materials organized into specific categories such as articles, books, events, courses, videos, websites as well as people with expertise in the fields they are into. The materials can be gathered from different sources, tagged, rated, reviewed and shared. The user can also create and manage their own influencer profile and add comments and ratings to other profiles. The contents can be added manually or embeded from YouTube, Vimeo, Amazon, SlideShare, MeetUp, EventBrite, Facebook and other integrated web services. As regards the technologies used, the service built is based on the Spring Boot framework, which exposes a secure REST API and enables the scheduling of ML related tasks (e.g model retraining). We also used Apache Spark to ensure the option of horizontal scaling and thus fit the system up for supporting significant data volumes in the future. It is also in preparation for high loads that we built the platform using an asynchronous approach. The recommendation engine was developed with the collaborative filtering technique following the associated Spark documentation recommendations; still we adjusted the solution with some improvements to make it better fit the specific context of the project.