Gather AI builds an AI co-pilot for intralogistics that fuses real-time machine data with warehouse management systems. The tech stack reveals a mature ML infrastructure play: PyTorch, TensorFlow, ONNX, TensorRT, plus AWS/Azure/GCP and SageMaker/Vertex AI for model serving, paired with Kubernetes and PostgreSQL/MongoDB for production scale. Active projects around MLOps platforms, multi-region databases, and computer vision inventory systems signal a company scaling from initial deployments toward enterprise reliability and edge inference—a shift reflected in hiring velocity across senior and staff-level infrastructure roles.
Tech Stack 44 technologies
Core StackAWS Kubernetes PostgreSQL MongoDB PyTorch Python TensorFlow SageMaker Vertex AI Playwright Postman TypeScript JavaScript Swift SwiftUI Jenkins Docker GitLab Prefect Azure ONNX TensorRT GCP Newman pytest UIKit iOS Modal DJI Android+14 more
What Gather AI Is Building
◆Challenges
- Manual and error-prone workflows
- Digitizing manual workflows
- Configuration drift in cloud environments
- Enterprise-grade reliability and performance
- Complex infrastructure challenges
- Platform stability and scalability
- Faster deployment
- Easier operation
- Reliable at scale
- Lack of structured infrastructure-as-code
▲Active Projects
- Implement infrastructure-as-code patterns
- Build multi-tenant mlops platform
- Improve kubernetes platform reliability
- Data layer evolution to enterprise-grade reliability
- Design and scaling of relational and distributed databases
- Replication and multi-region read/write strategy implementation
- Drone-based inventory scanning system
- Strengthen ci/cd pipelines
- Computer vision systems for warehouse inventory scanning
- Model optimization and deployment across cloud and edge inference targets
Hiring Activity
10 roles · 5 in 30d
Department
Seniority
Company intelligence