Computer vision AI for offline location analytics and people behavior
Indivd uses 2D cameras and a patented computer vision engine to analyze human movement and interaction patterns in physical spaces. The tech stack—Python, FastAPI, Go, Kubernetes, AWS/GCP/Azure, TensorRT, and React—reflects a production-grade computer vision backend paired with real-time data processing. Current hiring is entirely engineering-focused (7 roles across senior and junior levels), concentrated on scaling test coverage and backend architecture, which aligns with stated pain points around quality assurance and real-time data pipelines at scale.
Indivd analyzes how people move and interact in real-world locations using camera feeds and AI vision processing. Founded in 2017 and based in Stockholm, the company serves retail, venue, and facility operators seeking offline customer insights—tracking foot traffic, dwell patterns, and behavioral interactions without storing personal identifiers. The platform consists of an AI vision backend (built on TensorRT and CUDA), real-time data processing pipelines, and a React-based analytics frontend. The team is 11–50 people, entirely engineering-led, with active expansion into Turkey, Philippines, Ukraine, and Poland.
Python, FastAPI, Go, Node.js, Kubernetes, AWS/GCP/Azure, TensorRT, CUDA, React, and TypeScript. Real-time pipelines built on Prometheus and Elasticsearch for metrics and log aggregation.
Scaling test coverage and QA automation; building real-time data processing services; architecting backend systems for scalability and security; and strengthening CI/CD pipelines around computer vision model serving.
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