-1.7%Growth (Monthly CAGR)
0.1 yearsTime Since Last Round
Trax’s mission is to enable brands and retailers to harness the power of digital technologies to produce the best shopping experiences imaginable. Trax’s retail platform allows customers to understand what is happening on shelf, in every store, all the time so they can focus on what they do best – delighting shoppers. Many of the world’s top CPG companies and retailers use Trax’s dynamic merchandising, in-store execution, shopper engagement, market measurement, analytics, and shelf monitoring solutions at scale to drive positive shopper experiences and unlock revenue opportunities at all points of sale. As pioneers in computer vision, Trax continues to lead the industry in innovation and excellence through development of advanced technologies and autonomous data collection methods. Trax is a global company with hubs in the United States, Singapore and Israel, serving customers in more than 90 countries worldwide. To learn more about Trax, please visit http://www.traxretail.com.
Employee counts updated on a monthly basis.
|Executive Chairman||Serial Entrepreneur|
|Chief Commercial Officer|
Board and Advisors (0)
Funding Rounds (11)
M&A Events (0)
|Investment Firm||United States|
|Corporate Venture Capital||Japan|
|Private Equity||United States|
|Patent Title||Status||Date||Patent ID|
Research Publications (0)
AI Technology Stack
We use AI for improving our own decision-making processes, for improving our products, and as a driver for new products. Internally, we leverage Time Series analysis (mostly Holt Winters Triple Exponential Smoothing or ARIMA) to predict the expected workload (images, analysis times and costs as well as identifying system performance issues (anomaly detection using GMM). AI is also ingrained in our products: we use non linear regression to predict our customer sales performance, Auto Encoders and NMF to create dense representation of outlets that are then fed into unsupervised clustering methods (often density based such as DBScan), and classification to build profiles of these outlets. Prediction results are often fed into planning and optimizing tools to generate concrete actions. These include heuristic search (A*, AKA œA-Star), Genetic Algorithms, and local search with simulated annealing.
A*AgglomerativeARIMAAuto EncodersBoltzman MachinesBoostingClusteringDBScanDecision TreesDensity EstimationGenetic AlgorithmsHolt-WintersK-MeansLocal SearchNaive Bayes ClassifiersNearest NeighborNeural NetworksNon-LinearNon-Negative Matrix FactorizationSimulated AnnealingSupport Vector Machines (SVM)t-SNE