Business Overview
Active
2010
858
-1.7%
$1.08B
Debt
11
14
0.1 years
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 Count
-1.7%
-4.2%
-7.6%
Employee counts updated on a monthly basis.
Lists
News
November 13, 2023 |
Stock Chart
Founders (2)
| Name | Position | Military | Classification | Contact |
|---|---|---|---|---|
hiddenDror Feldheim | Executive Chairman | Serial Entrepreneur | ||
hiddenJoel Bar-El | Chief Commercial Officer |
Board and Advisors (0)
Revenues (0)
Funding Rounds (11)
$1.03B
$50M
| Round | Date | Capital Raised | Valuation | Investors |
|---|---|---|---|---|
| April 2021 | hidden$640M | $2.25B | 4 | |
| June 2019 | hidden$100M | $1.1B | 4 | |
| June 2018 | hidden$125M | $1B | 2 | |
| May 2017 | hidden$64M | 1 | ||
| January 2017 | hidden$19.5M | 1 | ||
| May 2016 | hidden$40M | 1 | ||
| November 2014 | hidden$15M | 1 | ||
| January 2014 | hidden$15.7M | |||
| July 2013 | hidden$6.6M | |||
| May 2011 | hidden$7M | |||
| November 2023 | hidden$50M | 1 |
M&A Events (0)
Investors (14)
| Investor | Investor Type | Location |
|---|---|---|
| Investment Firm | Japan | |
| Venture Capital | Japan | |
| Investment Firm | United States | |
| Corporate Venture Capital | Japan | |
| Pension | Canada | |
| Government | Singapore | |
| Government | Singapore | |
| Company | South Africa | |
| Private Equity | United States | |
| Company | Scotland | |
| Company | Scotland | |
| Investment Firm | Israel | |
| Investment Firm | Israel | |
| Venture Capital | Israel |
Patents (17)
| Patent Title | Status | Date | Patent ID |
|---|---|---|---|
| Application | August 2019 | US-2019236531-A1 | |
| Application | July 2015 | WO-2015101979-A1 | |
| Application | March 2014 | IL-229806-D0 | |
| Application | June 2015 | WO-2015083170-A1 | |
| Application | June 2019 | US-2019197561-A1 | |
| Application | July 2017 | US-2017200068-A1 | |
| Granted | US-10402777-B2 | ||
| Granted | US-10122915-B2 | ||
| Application | September 2020 | WO-2020181066-A1 | |
| Granted | US-10521645-B2 | ||
| Application | March 2020 | US-2020074402-A1 | |
| Granted | US-10387996-B2 | ||
| Granted | US-10368662-B2 | ||
| Application | November 2014 | WO-2014181323-A1 | |
| Application | March 2020 | WO-2020051213-A1 | |
| Application | May 2019 | US-2019149725-A1 | |
| Application | March 2019 | WO-2019048924-A1 |
Research Publications (0)
Certifications (0)
AI Technology Stack
31
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
