Chiperos provides ChiperOS, a unified operating system that abstracts and orchestrates AI workloads across on‑premise, edge, and cloud environments. The platform offers hardware‑agnostic APIs, automated scheduling, and real‑time performance monitoring, enabling developers to deploy models without rewriting code for each target device while optimizing cost and performance.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers often face challenges integrating and managing AI workloads across heterogeneous hardware and cloud environments, leading to increased complexity, higher operational costs, and slower time‑to‑market for AI‑driven applications.
Solution
Chiperos offers ChiperOS, a unified operating system designed to abstract and orchestrate AI workloads across on‑premise, edge, and cloud resources. The platform provides a consistent runtime environment, automated resource allocation, and hardware‑agnostic APIs that enable developers to deploy models without rewriting code for each target device. ChiperOS includes built‑in monitoring and optimization tools that dynamically adjust compute and memory usage to maximize performance while minimizing cost. By centralizing AI workload management, ChiperOS reduces integration effort and accelerates deployment cycles for data‑intensive applications.
Target Audience
Primary customers are AI development teams, data science platforms, and enterprise IT departments that need to deploy and manage machine‑learning models across diverse infrastructure.
Features
- Hardware‑agnostic AI runtime that supports CPUs, GPUs, TPUs, and specialized accelerators
- Automated workload scheduling and scaling across edge, on‑premise, and cloud nodes
- Unified API layer for model deployment, inference, and training workflows
- Real‑time performance monitoring with auto‑tuning of resource allocation
- Integrated security sandbox to isolate AI processes and protect data integrity
- Compatibility layer for popular AI frameworks such as TensorFlow, PyTorch, and ONNX