Ilustratie Homomorfism

We enable physical retail to
capitalize on the rise of the AI

Infrastructure Based Indoor Behavior Tracking

Our hardware-agnostic indoor behavior tracking technology is using data signals from Wi-Fi and BLE sensors in order to detect and interpret shoppers’ localization & physical movement.   Our solution collects data from the indoor ambient connectivity already in place inside retail buildings in order to detect and to create a hyper-accurate positioning of individual people carrying mobile devices while shopping.   Devices like smartphones and smart wearables, as long as they don’t need to be connected to a guest Wi-Fi network or to another Bluetooth device, can represent the most valuable source of physical behavioral data for shopping insights.   We apply advanced Machine Learning algorithms to analyze shopping paths, interpret affinities, correlate preferences and visualize all these shopping insights. Thus, we generate meaning and knowledge for shopping centers’ management around very critical business performance questions like: What are my anchor stores and how much new traffic they bring to other stores? What is the cross-shopping behavior of the most loyal shoppers we have? Who are the first-time visitors?

Hyper-accurate infrastructure-free Indoor Positioning System

We use the indoor ambient connectivity and people movement to create hyper-accurate indoor positioning for commercial buildings.

Hyper-Accurate Indoor Positioning (1-1.5 m)

Hardware Agnostic

Quick Deploy & Maintenance

Our end-to-end solutions can support broad integration with high-tech providers

Hyper-Precise Device-Based Indoor Positioning

Surround Indoor Positioning System (IPS) is our proprietary sensor-fusion AI technology that uses data received by mobile device sensors from ambient connectivity to automatically locate & navigate people inside indoors.   Our advanced mobile IPS technology is based on triangulation principles and uses: ambient magnetic field data, steps detection & movement biometrics, your existing WI-FI infra, your existing BLE Beacons.   Our IPS technology can be embedded in any mobile app and can work seamlessly to enable your shopping center to allow visitors to navigate inside indoors and find their way towards shops, their car or towards other visitors.

Our advanced mobile IPS technology is based on triangulation principles and uses:

Ambiental Magnetic Field Data

Steps Detection & Movement Biometrics

Your Existing Wi-Fi Infrastructure

BLE Beacons

See how it works


Unprecedented AI for Offline to Online Data Fusion

Our UNIQUE PROPRIETARY AI FRAMEWORK solves the technological challenge to transform physical shopping behavioral data into actionable insights for both marketing and advertising campaigns.   We invested 5 years in developing and training an advanced deep neural network to classify subtle physical shopping behaviors into multi-dimensional clusters (we use up to 48 different behavioral data dimensions) and then match these clusters with their online “twins”.   We call this “homomorphism” and it represents our advanced AI framework that empowers our unique technology to have the capability to enrich any online database, at individual user level, with their physical shopping behavioral characteristics.   Physical behavioral data and affinity clustering is, by any means, the most valuable type of data to be used for Online Behavioral Advertising & Marketing Automation. This probabilistic attribution model generates unprecedented benefits for marketers and advertisers working with physical retail brands and objectives. It allows campaigns to be targeted towards very specific behavioral segments in order to Increase their Frequency of Visits, the Visit Duration, and the amount of Shops per Visit inside a certain Shopping Center, for example.

Offline & Online Affinity & Behavioral Profiling

Our fine-grained and high-resolution behavior and action recognition technology works both for online properties (like websites, mobile apps and social media channels) and for offline properties (shopping malls, hypermarkets and inside shops and other physical retail spaces).   We apply Machine Learning with advanced labeling, training and semantic algorithmic interpretation techniques to generate meaning around each individual shopper, refined affinity clusters, behavioral patterns and macro shopping habits.   For each individual retail business location, we collect data from a complete online & offline ecosystem of touchpoints with shoppers. And this data becomes business & shopper intelligence with precise insights into past & future behaviors that significantly improves the digital marketing and the customer relationship management capability.

AI-Powered Orchestrator for Campaign Automation

Whether in online content distribution, advertising, direct marketing or in offline shopper-oriented retail and marketing, our physical behavioral data based intelligent recommendation engine can be the key driving force to increase the click-through rate and conversion rate, and to decrease operational cost. Our intelligent Recommendation Engine integrates indoor location intelligence, shopper behavioral profiling, channel relevancy scoring and real-time multi-channel distribution.

Prediction Engine

Our automated machine learning platform includes Predictive Behavior Algorithms that mine data from a variety of offline and online sources, including some external factors like weather and macro socio-economic trends to calculate shopper-level indicators like Propensity to Visit and Propensity to Buy. Beside the direct benefits of such projections for business intelligence and operational planning, physical retail brands and business locations can use Priming to enhance the conversion rate of their advertising.   Through the capabilities of our Prediction Engine, a campaign can target shoppers 2 or 3 hours before they would be inside a shopping center and make sure that their product or service is top of mind on their purchase agenda before it’s too late.

Natural Language Understanding

Leveraging the latest machine learning technology and the semantic retrieval ability of cross-domain entity relationship databases, we have developed a framework to analyze and classify speakers’ real intention and current emotion.   The capability of more precise intention and emotion recognition makes the most important analysis and decision-making unit in the “brain” of the next generation of in-store shopper experience and online retail engagement for physical retail brands.

Unique proprietary Artificial Intelligence for offline to online fusion

Ilustratie Homomorfism

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