WinWire Technologies: Driving Business and Societal Good through Data and AI Expertise

Amid the raging debate of AI’s role as an enabler or a threat, organizations continue to invest in AI without having a well-defined strategy or a product roadmap. The misalignment between business strategy and new technologies invariably leads to failed initiatives and unnecessary cost. Ashu Goel, the CEO of WinWire Technologies argues, “How a company can benefit the society with technology is more important than the technology itself.” Goel leads a team that believes in technology as the key element to improving lives and creating deep social impact. Committed to promote appropriate and effective use of AI and cognitive solutions while enabling companies to remain competitive, WinWire focuses on guiding its clients to take a prudent approach to data and AI. Being at the leading edge of data analytics and AI innovation, the company bridges the gap between business and IT through its core strategy that encompasses people, technology, and execution. Its advancements in modern technologies focus on the ‘right brain revolution’ in which process tasks are automated and labor resources are fully availed to improve business efficiencies.

While some companies struggle to find the right AI solution, many others run into trouble due to lack of sufficient volumes of quality data to efficiently train their machine learning models. The severe shortage of experienced data scientists and effective tools to build models and visualize data add to the woes. WinWire employs a systematic and end-to-end approach that encompasses initial requirements gathering through development and maintenance of AI-driven data models. In its journey to build cohesion between business and IT, the company, at the outset, comprehends clients’ strategies, future goals, defines their business problems, and aids them to envision a solution. A quick proof of concept allows clients to visualize their solution. Subsequently, WinWire helps its clients identify various data sources within the organization and provides assistance with data ingestion, cleansing, and normalization to gain superior data quality. The company either helps clients build data models or builds it for them in case they lack the in-house talent. “If the clients lack data scientists, we can build models for them to resolve a client’s specific business problem.