Let teams compile and verify models for embedded devices together, with full traceability across their workflow.
Unify development, verification and compliance in a platform designed for real-world deployment.
Quantize and compile models for target hardware using integrated compiler backends.
Run models on cloud or on prem device farms. Verify across hardware and runtimes.
Visual dashboards show latency, accuracy, and compatibility across all targets.
Every step is logged automatically. Export compliance artifacts for audits instantly.
Across automotive, defense, robotics and manufacturing.
Embedl Hub provides a secure, traceable MLOps platform for edge AI workflows. It centralizes model compilation, on-device testing, validation, and artifact tracking into a single system. This enables teams to deliver production-grade edge AI with full traceability and compliance support—especially in safety-critical environments.
Embedl Hub is designed for organizations building AI systems that run on embedded or edge devices. It is used daily by ML engineers, embedded engineers, and verification teams, and is typically purchased by compliance, validation, or engineering leadership responsible for release and audit readiness.
Every model build, transformation, test run, and deployment artifact is logged and versioned. The system tracks which compiler version was used, which quantization settings were applied, which runtime executed the model, and on which devices it was validated. This creates a reproducible audit trail across the entire compile → verify → analyze lifecycle.
The platform provides artifact lineage, experiment tracking, integration testing across devices, and structured metadata logging. It integrates with tools such as Jira, Jama, ClearML and MLflow to connect development work with formal requirements and verification processes. This reduces manual audit preparation and ensures documentation is continuously generated as part of normal development work.
No. The core platform is industry-agnostic and purpose-built for edge AI. It supports compliance-driven workflows common in sectors such as defense, automotive, aerospace, and industrial systems. Industry-specific integrations and add-on's are available.
No. Embedl Hub orchestrates and tracks workflows but does not replace your compiler stack. It integrates with external backends such as Embedl Next, ModelOpt, AIMET, TIDL and others. You continue using your preferred toolchain while gaining centralized orchestration and traceability.
Yes. Embedl Hub can be deployed inside your intranet using containerized or virtual machine installations. In this setup, all data, artifacts, and logs remain within your infrastructure. A SaaS option is also available for teams that prefer managed hosting.
Yes. Customers can build an internal on-prem device farm by connecting all their existing edge devices to the Embedl Hub. This creates an on-prem device farm useful for easy on-device development and automated testing behind the firewall. The same workflows can also run on supported cloud device farms when appropriate. The CLI and SDK remain consistent across deployment modes.
Embedl Hub is purpose-built for edge AI. It focuses on compilation, hardware compatibility, on-device validation, runtime performance benchmarking, and traceability in environments where updates are difficult and compliance requirements are strict. It is opinionated and designed to work out of the box for embedded workflows, rather than being a flexible but generic ML experiment tracker.
Yes. The platform supports cross-functional collaboration between ML engineers, embedded developers, verification teams, and compliance stakeholders. Stakeholders can access dashboards, model metadata, performance comparisons, and artifact history without needing direct access to development environments.
Embedl Hub includes a model repository that allows teams to publish, discover, and reuse optimized models for specific hardware targets and use cases. In SaaS deployments, the Embedl publish pre-optimized models. In on-prem deployments, organizations can maintain their own internal model catalog.
By enabling early and automated testing across multiple devices, runtimes, and model formats, compatibility and performance issues are detected during development rather than at final integration. This reduces costly rework late in the release cycle.
Yes. The platform is designed for production-grade edge AI systems where updates are infrequent or constrained. It emphasizes validation, reproducibility, and audit readiness before release.