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RetailNext Tech Stack

Real-time retail analytics from in-store sensors and video across physical locations

IT Services and IT Consulting Campbell, California 201–500 employees Founded 2007 Privately Held
retailnext.net LinkedIn

RetailNext aggregates data from nearly 100,000 sensors deployed across physical retail stores to track over one billion shopping trips annually. The tech stack reflects a mature, scale-first engineering operation: Cassandra and Redis for time-series storage, Kafka and Google Cloud Pub/Sub for stream processing, and a computer-vision pipeline (OpenCV, FFmpeg, TensorFlow Lite, OpenVINO, ONNX) for video analytics. Current hiring priorities signal operational friction — product, ops, and finance roles dominate recent postings, while pain-point data reveals deployment complexity (bulk uploads, multi-store rollouts, scope creep) and data-quality validation as persistent scaling challenges.

Tech Stack 37 technologies

Core StackNetSuite Expensify Go Cassandra Redis Elasticsearch gRPC Kafka Ruby Node.js Java C++ AWS Docker Python Pendo Google Cloud Pub/Sub AWS SQS GCP TensorFlow Lite OpenVINO ONNX OpenCL OpenMP OpenCV FFmpeg CVAT Label Studio SIMD GPU+2 more

What RetailNext Is Building

◆Challenges

  • Scaling product organization
  • Managing complex deployments
  • Ensuring data accuracy in bulk uploads
  • Logistics coordination for high-volume projects
  • Onboarding new customers
  • Troubleshooting store-level setup issues
  • Monitoring infrastructure health
  • Scope shifts mid-project
  • Deployments on time and on budget
  • Protecting project margins

▲Active Projects

  • Named account deployment support
  • Quote processing for large-scale deployments
  • Bulk upload processing for multi-store rollouts
  • Product usage tracking
  • Feature adoption
  • Customer rollouts on retailnext platform
  • Data quality validation workstreams
  • Product launches end-to-end
  • Upgrade campaigns
  • Website engagement strategies

Hiring Activity

Accelerating7 roles · 7 in 30d

Department

Ops
2
Product
2
Finance
1
Marketing
1
Support
1

Seniority

Mid
3
VP
2
Junior
1
Staff
1
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About RetailNext

RetailNext is a global retail analytics platform serving brick-and-mortar retailers, brands, and mall operators across 40+ countries. The product ingests video, Wi-Fi data, on-shelf sensors, and point-of-sale systems to generate insights on customer behavior, store traffic, and conversion patterns. The company processes trillions of data points annually to surface actionable patterns in footfall, dwell time, and product interaction. Sales motions center on large named accounts requiring multi-location deployments, supported by infrastructure to handle bulk configuration uploads and coordinated store-level rollouts.

HeadquartersCampbell, California
Company Size201–500 employees
Founded2007
Hiring MarketsSouth Africa, Philippines, Belgium

Frequently Asked Questions

What is RetailNext's tech stack?

RetailNext uses Cassandra, Redis, Kafka, and Google Cloud Pub/Sub for data pipelines; OpenCV, TensorFlow Lite, and OpenVINO for computer vision; Go, Java, Python, and Node.js for application logic; and Docker for containerization across GCP and AWS.

What are RetailNext's main challenges?

Managing complex multi-store deployments, ensuring data accuracy in bulk uploads, coordinating high-volume customer onboarding, troubleshooting store-level configuration issues, and maintaining project margins amid scope changes.

How many employees does RetailNext have?

RetailNext has 201–500 employees, headquartered in Campbell, California, with hiring activity across South Africa, Philippines, and Belgium.

How this profile is built

RetailNext's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.