Trax
Image Recognition for Retail Analytics and Points of Sale
Business Overview
Active
Operating Status
Nov 2010
Founded
600
Employees
B2B
Business Model
Software
Offering Type
Description
Trax Image Recognition provides cutting-edge image-recognition technology and market-data services to tier-one manufacturers. The company assures tighter in-store execution controls and the ability to unlock revenue opportunities at all points of sale. Traxs technology can be integrated as a plug-in to existing merchandising or retail execution systems. The companys platform utilizes iOS, Android, and Windows smartphones and tablets to capture images of retail shelves. Analysis within the Trax cloud provides immediate, actionable mobile reports to sales reps and aggregated web reports to management teams. Traxs solutions replace manual auditing methods by up to 60%, allowing sales reps to focus more time on in-store sales development activities. Trax is a trusted OEM partner of more than 25 leading retail solution providers, and many top brands use Trax to manage their in-store execution and increase revenues, including Coca-Cola, AB InBev, Nestle, and Henkel.
Business Stage
Launched
Revenue Stage
Revenue Generating
Sectors
Funding Rounds
$1032.8 M
Total Funding
13
Funding Rounds
1.42x
Funding Growth
Series E
Last Funding Series
Startup Profile Funding Rounds
Date Announced
Funding Round
Amount Raised
Investors
AI Technology Stack
31
AI Team Size
3
AI Technologies
AI Description
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 Å“A-Star), Genetic Algorithms, and local search with simulated annealing.
ML Types
AI Algorithms
A* Agglomerative ARIMA Auto Encoders Boltzman Machines Boosting Clustering DBScan Decision Trees Density Estimation Genetic Algorithms Holt-Winters K-Means Local Search Naive Bayes Classifiers Nearest Neighbor Neural Networks Non-Linear Non-Negative Matrix Factorization Simulated Annealing Support Vector Machines (SVM) t-SNE
AI Tools
Coding
AI Cloud Provider
Patents
WO-2020051213-A1
- Application
In The News
More Startups
Startup Snapshot
Operating Status
Active
AI Team Size
31
Total Funding
$1032.8 Million
Founded
November 2010
Funding Stage
Series E
Time Since Last Funding Round
4 months
Valuation (Post-Money)
As of April 2021
$2250 Million
