Grassdoor's primary challenge was to calculate accurate ETAs and optimize routes for last-mile and on-demand deliveries. The company needed a Distance Matrix API that could handle large API call volumes at scale, run at high throughput and low latency, and be cost-effective. The existing Distance Matrix APIs in the market had limitations, such as a matrix size limited to 25*25 elements, which was insufficient for optimizing a large number of deliveries for Grassdoor's large-scale operations. The cost of these existing APIs was also a concern as they proved expensive and the problem worsened as Grassdoor scaled up. The company was also looking for ways to improve operational efficiency in terms of increased throughput and reduced latency as they scaled.

A leading AI cloud computing company was faced with the challenge of creating a large scale, end-to-end mapping solution for the entire UAE within a span of three months. The company needed to build high precision, country scale maps with rapid refresh rates, a task that is both expensive and extensive. The process involved building a map at a UAE level, adding 50+ custom attributes, performing quality checks and conflict resolution, and constantly maintaining and refreshing map data. The company also needed to derive map data intelligence from multiple imagery sources, which was a time-consuming process. The data structures used by different routing and navigation engines like OSRM and others vary by routing engine type. The client needed map data that could easily integrate with their existing routing and navigation engines.

The leading food delivery company in the US was facing challenges in keeping up with the growing demand and customer expectations. The company was striving to meet its ambitious growth goals without compromising on the quality of service. The existing mapping solutions were rigid and did not cater to the company's specific needs. The company required a more tailored mapping solution that would help them achieve more efficient and on-time deliveries, provide more accurate Estimated Time of Arrivals (ETAs), and reduce costs on Maps APIs.

A leading Indian logistics tech company, known as the country’s largest neutral freight network, was facing challenges in providing an intuitive freight tracking experience for their customers. The company was operating in a complex industry with low tech adoption at different levels. The primary challenges included tracking thousands of data points for each journey, such as accurate routing across highways, country roads, and warehouse locations, traffic data across cities and states, setting up contextual POIs, and additional data such as tire wear-and-tear, vehicle utilization, tolls, and permits. These data points were managed by different stakeholders, causing significant operations challenges. The second challenge was accounting for local nuances like vehicle restrictions & driving behavior, which varied tremendously from state to state in India. Other complications included the type of vehicle, changing topographies, driving patterns, and speed limit depending on the cargo.