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RetailNext Tech Stack

Real-time in-store analytics from video, sensors, and point-of-sale data

IT Services and IT Consulting Campbell, California 201–500 employees Founded 2007 Privately Held

RetailNext operates a distributed sensor and computer-vision platform across nearly 100,000 retail locations, processing over one billion shopping trips annually. The tech stack—Go, Cassandra, Redis, Kafka, TensorFlow Lite, OpenVINO, and GPU acceleration—reflects a company scaling edge ML inference and real-time data pipelines. Active projects on edge CV/ML deployment optimization, Terraform adoption, and sensor telemetry collection signal infrastructure maturation; hiring is concentrated in engineering (senior roles) across six countries, suggesting edge deployment and inference are core scaling blockers.

Tech Stack 32 technologies

Core StackGo Cassandra Redis Elasticsearch gRPC Kafka Ruby Node.js Java C++ AWS Linux Docker Python Google Cloud Pub/Sub AWS SQS GCP TensorFlow Lite OpenVINO ONNX OpenCL OpenMP OpenCV FFmpeg CVAT Label Studio SIMD GPU DSP CI/CD

AdoptingTerraform GPU DSP

ReplacingPrometheus

What RetailNext Is Building

◆Challenges

Deployment challenges for edge cv/ml
Maintaining robust data foundations
Transition prototypes to production
Migrating monitoring stack
Managing cloud resources
Collecting telemetry from sensors
Reactive product strategy

▲Active Projects

Retailnext product implementation
Bringing cloud resources under terraform management
Migrating from statsd to prometheus
Re-writing how our application collects diagnostic telemetry from deployed sensors
Edge cv/ml deployment optimization
Hardware acceleration implementation
Annotation infrastructure improvement
Traffic product area
Asset protection product area
Deploy cv/ml systems at scale

Hiring Activity

Accelerating6 roles · 3 in 30d

Department

Engineering

5

Product

1

Seniority

Senior

3

Mid

2

VP

1

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About RetailNext

RetailNext provides analytics for brick-and-mortar retail by collecting data from video, Wi-Fi detection, on-shelf sensors, and point-of-sale systems. The platform generates insights on customer traffic, conversion, and in-store behavior, integrating with promotional calendars, staffing systems, and external data like weather to model how factors drive shopping patterns. RetailNext operates in over 40 countries and processes trillions of data points annually from its sensor network. The company serves mid-to-large retailers and manufacturers seeking to identify growth opportunities and measure operational changes.

HeadquartersCampbell, California

Company Size201–500 employees

Founded2007

Hiring MarketsSpain, Netherlands, United States, Bulgaria, Canada, Mexico

Frequently Asked Questions

What does RetailNext's tech stack look like?

RetailNext uses Go, Cassandra, Redis, Elasticsearch, Kafka, and GCP/AWS for infrastructure. For computer vision, it deploys TensorFlow Lite, OpenVINO, and ONNX for edge inference, with OpenCV and FFmpeg for video processing. Python, Java, Node.js, and Ruby support backend services; Docker handles containerization.

What is RetailNext currently working on?

Active projects include edge CV/ML deployment optimization, hardware acceleration implementation, migrating monitoring from Prometheus, Terraform cloud resource management, sensor telemetry collection redesign, and scaling CV/ML systems across the store network.