Global telecommunications company needed advanced machine learning software while migrating legacy code. Off-site Vaco software engineers led them to success and flourish in an ongoing partnership.
Mastering Machine Learning
A global telecommunications giant faced a twofold challenge on an extremely tight timeline: build a bleeding edge machine learning software application designed to support law enforcement body cameras, and migrate legacy code over to a modern technology framework (Node.JS) to improve performance and scalability. Complex budget constraints added fuel to the fire, as the company had made a large capital investment in offshore teams that failed to deliver on the project. As urgency increased and budgets shrunk, finding quality full-time software engineers to complete the project in the company’s local Chicago market was proving to be labor intensive as well as cost prohibitive.
Deliver Working Demo Using Agile
Vaco proposed an executed agile software development approach with distinctive milestones, showcasing working product features in a series of two-week sprint cycles. To reduce talent cost, Vaco built a self-directed off-site team in Dayton, Ohio – where the cost of living is an average of 20% lower than Chicago – while still providing the level of machine learning expertise and software development talent the client expected.