ZEDEDA unveiled its Edge Intelligence Platform, what is said to be the industry’s first solution that can create, deploy and secure edge AI at scale. The platform was built for and together with leading distributed Fortune 500 enterprises worldwide that require edge intelligence wherever their business operates.
“We are entering a new era where AI must live in the physical world. For years, the 'cloud-first' model created a digital bottleneck, forcing physical operations to be tethered,” says Said Ouissal, CEO and founder of ZEDEDA. “ZEDEDA Edge Intelligence Platform flips this equation. By delivering intuitive, autonomous intelligence directly to the edge and physical world using AI, we’re removing the final barrier for enterprises to bring the full value of AI to where their business operates where it matters most, in real-time, securely and at scale.”
Key takeaways:
· With the release of the Edge Intelligence Platform, built on ZEDEDA’s edge orchestration platform, enterprises can now orchestrate the complete edge AI lifecycle in a single control plane and API, defining agent behavior, model versioning, inference optimization, and managing distributed edge infrastructure.
· ZEDEDA’s AI-powered platform is built to intelligently and autonomously implement the entire edge AI stack from inception to operation securely at scale.
· Beyond the platform, ZEDEDA is also introducing Edge Intelligence Labs, which curates and maintains pre-validated AI models, agents and blueprints, and Edge Intelligence Appliances, which are ready to deploy integrated, open and verified edge systems, built with leading partners, to deliver production-ready edge AI tailored to each customer's environment.
· Edge Inference Services enables organizations to deploy, validate and operate AI models across diverse edge hardware at the same speed, with the same governance and confidence they expect in centralized cloud environments.
· ZEDEDA Edge Inference Services accelerate edge AI deployment through automated deployment workflows that remove manual infrastructure setup and reduce dependence on specialized DevOps resources; real hardware benchmarking, enabling teams to validate model performance on actual edge devices; and enterprise-grade governance, including GitOps-based version control, approval workflows, audit trails and one-click rollback for automated, auditable, and reliable edge AI deployments across edge fleets.
