Deploy AI at the Edge

We help companies leverage open-source models and edge computing to build faster, more cost-effective AI solutions. From model optimization to production deployment.

Edge First
90%
Reduction in latency
Cost Efficient
70%
Lower infrastructure costs
Open Source
100%
Based on proven frameworks
Production Ready
24/7
Monitoring & support

About us

Blue Keys Studio specializes in deploying AI at the edge using open-source models and cutting-edge MLOps practices. We help organizations leverage small-scale, efficient models that run on edge devices, reducing latency and enabling real-time decision-making.

Our approach combines DevOps excellence with edge computing expertise to create robust deployment pipelines. We focus on open-source frameworks like TensorFlow Lite, ONNX, and Hugging Face models, optimized for resource-constrained environments.

From model selection and optimization to deployment and monitoring, we provide end-to-end solutions that enable your AI to run where it matters most—at the edge. Based in Miami, Florida, we work with companies across industries to bring the power of edge AI to production.

Edge-first architecture.
Designed for performance on resource-constrained devices.
Open source powered.
Leveraging proven frameworks and community-driven innovation.
Production ready.
Built for scale with proper monitoring and deployment strategies.

Our services

We specialize in deploying AI at the edge using open-source models and production-grade MLOps pipelines.

01
Edge AI Deployment

Deploy small-scale, efficient AI models at the edge using open-source frameworks. Optimize models for edge devices, reduce latency, and enable real-time inference close to data sources with our specialized MLOps pipelines.

02
DevOps for Edge Computing

Build robust CI/CD pipelines for edge deployments. Infrastructure as code, automated testing, and orchestration solutions designed for distributed edge architectures. Seamlessly deploy and manage applications across edge nodes.

03
Open Source Model Integration

Leverage open-source AI models optimized for edge computing. We help you select, fine-tune, and deploy models from Hugging Face, ONNX, and TensorFlow Lite, ensuring efficient performance on resource-constrained devices.

Technology Stack

Open Source Tools We Use

Leveraging proven open-source frameworks and tools to deliver production-ready edge AI solutions.

TensorFlow Lite

Edge AI Framework

Optimized models for mobile and edge devices. Model quantization, delegate acceleration, and runtime optimization for resource-constrained environments.

ONNX

Model Interoperability

Open Neural Network Exchange for cross-framework model deployment. Convert and optimize models from PyTorch, TensorFlow, and other frameworks for edge inference.

Hugging Face

Open Source Models

Access to thousands of pre-trained models. Model fine-tuning, optimization, and deployment pipelines for NLP and vision tasks at the edge.

Edge Orchestration

Distributed DevOps

K3s, KubeEdge, and edge-native orchestration tools. Automated deployment, monitoring, and updates across distributed edge infrastructure.

Our work

We've helped organizations deploy AI at the edge, achieving dramatic improvements in latency, cost, and reliability.

Edge AI Deployment

Real-time inference requirements with limited connectivity

Optimized TensorFlow Lite models deployed across 500+ edge devices

90% reduction in latency

Open Source MLOps

Proprietary ML platforms too expensive at scale

Open-source MLOps stack with automated model deployment

80% cost reduction

Distributed Edge Pipeline

Managing updates across thousands of edge nodes

GitOps-based CI/CD with automated rollback capabilities

Zero downtime updates

Why Choose Us

Built for Edge. Powered by Open Source.

We combine deep expertise in edge computing, open-source AI frameworks, and production-grade MLOps to deliver solutions that perform where traditional cloud deployments can't.

Blog

Latest Insights

Stay updated with our latest thoughts on edge AI, open-source models, and MLOps best practices.

Coming Soon

We're working on exciting content about Kubernetes, DevOps, and MLOps. Check back soon for our latest insights!