Solution

AI platforms, MLOps and edge

We design the architecture, deployment and operation of AI in the cloud, on-premise and at the edge.

The capability

We industrialize models with security, observability, governance and performance so they keep working when data, infrastructure or operations change.

01On-premise and classified deployment
02Edge and embedded AI
03Data platforms and integration
04MLOps and model operation

Offering matrix

Seven real solutions.

Each node adapts this capability to a specific sector problem and has its own page.

DefenseAI in classified environments (on-premise)LogisticsReal-time AI and WMS-TMS integrationTransportOnboard edge and mobility dataMedicine and pharmaValidated platforms (GxP) and federated dataEnergyAI in substations and industrial edgeIndustry and manufacturingOT-IT convergence and industrial MLOpsCivil and constructionConstruction data and IoT platform

How we build it

Stack and ecosystem.

We select tools based on data, environment, regulation and operating model.

Technologies

Kubernetes / DockerRed Hat OpenShift AINVIDIA Triton / NIMONNX RuntimeTerraformAWS / AzureNVIDIA JetsonDatadog

Partners

AWS ✱Microsoft ✱NVIDIARed HatDatadog ✱Confluent ✱

Related technologies

Target stack.

Technology Platform, MLOps and edge A production foundation to operate AI in the cloud, on-premise, at the edge and in regulated environments. View detail

From opportunity to operation

We build value in stages.

Governance and compliance accompany the whole cycle, from the first hypothesis to the system in production.

  1. 01
    Diagnosis

    Opportunity, data, feasibility and roadmap.

  2. 02
    Pilot

    Proof of value with clear business metrics.

  3. 03
    Industrialization

    Integration, security, MLOps and adoption.

  4. 04
    Operation

    Monitoring, support and continuous improvement.

Let's talk about your operation

Where can AI create value?

itfuturton@futurton.com