La Javaness: Cognitive Intelligence to maximize business value
Follow La Javaness on :
Alexandre Martinelli, CEO
“In the future decades, the business technology sector will experience a major transformation from traditional business applications to AI-powered solutions—more profound than the evolution over the past two decades,” begins Alexandre Martinelli, CEO of La Javaness. In order to cope with this digital metamorphosis, human and artificial intelligence (AI) should work hand-in-hand in organizations and facilitate informed decision making and predict new job opportunities, thereby making headway toward profitability.
However, most organizations have been reluctant in adopting AI in their infrastructure. “There are only a handful of scenarios where AI has been deployed in production—the primary reason being organizational resistance,” states Martinelli. According to the CEO, organizations confront resistance from all levels—the top management level is unsure of the viable outcomes while employees fear job loss. Added to human resistance, the biggest challenge looming large in the IT space is the lack of in-depth knowledge in industrializing AI. Aimed at eliminating this issue, La Javaness is dedicated to bringing concrete business value to companies leveraging AI-powered solutions, assisting them in faster AI adoption and harnessing productivity gains.
La Javaness offers two suites of AI-based cognitive solutions. Predictor is a real-time machine learning-powered business solution specifically designed to optimize various revenue levers with the aid of predictive intelligence.
“One of our foremost solutions is the Predictor Smart Pricing, capable of predicting the best price of each product for each customer in real time and enabling salespersons to close deals and increase profit margins,” reveals Martinelli. The Smart Pricing also allows pricing managers to define, test and deploy pricing strategy, and integrate business rules as required.
Deploying deep learning and natural language processing (NLP), La Javaness’ Otto is a cognitive automation solution for facilitating customer support activities. “Otto automates the mundane tasks, including classifying and prioritizing customer emails, tagging and routing the emails, enabling agents to focus on the complex and higher value-added tasks,” explains Martinelli. Besides, Otto provides answers to the simple queries while automating end-to-end workflows by connecting with the backend systems. It provides full automation for up to 50 percent of incoming emails.
Otto and Predictor’s ascension to success can be attributed to three major factors. First, La Javaness’ proprietary machine learning execution engine is capable of processing in real-time sophisticated machine learning models with thousands of variables in milliseconds. Secondly, human users and AI work collaboratively to deliver better outcomes. “The design of the machine allows human users to take control of it and take all decisions,” comments Martinelli. In doing so, the company has successfully empowered the end-users as well as improved the predictive results of the algorithm. Lastly, the solutions aid in easy monitoring of the algorithms and data, thereby instilling transparency in the system. For delivering exceptional results in the cognitive solutions space, Predictor has bagged the Trophée innovation Big Data Paris Award 2018, while Otto has won the Trophée AI Business Day l’Usine Digitale Award 2018.
Playing it big in the AI solutions space, La Javaness has demonstrated the prowess of the Predictor Smart Pricing in supporting business objectives. What makes La Javaness stand out in the market is its ability to manage more than 800 variables in its model, contrary to the conventional model of managing about a dozen. In an instance where one of its clients compared a number of providers via proof-of-concept method, La Javaness’ real-time algorithm processing system outdid its counterparts. That’s not all; La Javaness delivered a robust machine learning model integrated into Predictor’s web interface customized for end users in about three months. “The test revealed a 30 percent increase of profit margin for the client as opposed to only five to ten percent delivered by our counterparts,” concludes Martinelli.