EdgeKube.ai is a platform for organizations that require rapid iteration, transparent governance, and scalable AI deployment. Built on open-source foundations, it delivers secure, compliant production pipelines.
Versioned data pipelines supporting sensors, simulations, time series, and imagery.
Event-driven retraining, performance tracking, and automated deployment workflows.
Model interpretability and traceability for audit-ready AI operations.
Governance, observability, and version control aligned with responsible AI standards.
On-prem, cloud, or hybrid with full containerization.
Full source code and documentation, so operational sovereignty is real rather than promised.
Problem, deployment model, licensing, and how this relates to our consulting work, stated plainly.
The gap between a model that works in a notebook and a model that runs in production is where most AI programs stall. Retraining is manual, nobody can say which version is live, and the audit trail is reconstructed after the fact. Meanwhile the deployment target may be a cloud region, your own datacentre, or a device at the edge, and the tooling usually assumes only one of those.
The infrastructure layer for the model lifecycle: versioned data, automated retraining and deployment, and the observability needed to answer what is running and why.
On-prem, cloud, or hybrid, fully containerized. Horizontally scalable, with CI/CD and enterprise integration built in.
Platform and MLOps teams; organizations with data-residency or sovereignty requirements; programs that must show governance evidence for the models they run.
EdgeKube is the platform our own deployment and MLOps engagements run on, which is why it exists. Engage us for the deployment work and EdgeKube may be the infrastructure underneath; licence it directly and your platform team runs it.
Licensed per deployment, including full source code and documentation. Contact us for a demo or the documentation set.
Used as the deployment substrate for Lemay.ai production engagements, including environments with on-prem and data-residency constraints.
Custom engagements. Clients own project-specific code, models, and documentation as defined in the applicable agreement. That is the default for consulting work and it does not change because a project touches one of our products.
Lemay.ai products. Pre-existing Lemay.ai platforms and intellectual property are licensed separately where selected. A licence covers the platform; it does not claim anything you build on top of it.
Tell us what you are trying to accomplish and we will tell you whether this is the right fit, including when it is not.