Custom Private AI & Edge Solutions for Enterprise
Keep your data secure, sovereign, and under your control. We design, build, and deploy custom private AI applications and edge AI systems that process sensitive data on-premise or on-device with zero cloud dependency.
What Are Private AI and Edge AI Services?
GenAI Protos delivers both as an integrated solution for enterprises that need secure, high-performance AI under complete control. To learn more about our architectural frameworks and deployment models, explore our Private AI expertise.
The global demand for private AI and edge AI is accelerating. Enterprises across healthcare, financial services, legal, and industrial sectors are moving AI out of public cloud environments and into infrastructure they own and control, whether that means on-premise servers, air-gapped facilities, edge devices at the point of operation, or hybrid architectures spanning all three.
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Why Enterprises Are Moving AI Off the Public Cloud
Public cloud AI services offer fast setup, but introduce compliance risks, unpredictable API costs, latency bottlenecks, and zero control over your proprietary data.
High Cloud Costs
Local processing significantly cutting bandwidth usage and cloud storage fees.
Latency Issues
On-device execution enables real-time responses by eliminating network transmission delays.
Privacy Concerns
Keeping sensitive data locally on-device minimizes exposure to external breaches.
Connectivity Gaps
Operations continue autonomously without relying on constant, stable internet access.
Hardware Constraints
Running complex LLMs on battery-powered devices requires extreme optimization.
Integration Complexity
Bridging the gap between AI models and embedded firmware.
Our Private AI & Edge AI Services
End-to-end engineering capabilities covering architecture, model compression, firmware integration, and on-premise deployment.
Strategic Edge AI Architecture & Use Case Design
Define high-impact edge AI applications, hybrid edge-cloud architectures, and 5G MEC deployment strategies for optimal ROI.
Edge AI Application Development (Remote & On-Site)
Custom industrial edge AI, autonomous systems, IoT edge AI development across NVIDIA DGX Spark, Jetson, Google Coral, and TinyML platforms.
Edge AI Setup & Research Support
Platform selection, proof-of-concept development, and feasibility assessment for NVIDIA, Google, Intel, AMD, and Qualcomm edge AI platforms.
Edge AI Testing & Validation
Performance testing, accuracy validation, and hardware compatibility verification for real-time AI inference across edge devices.
Edge AI Integration Services
Seamless integration with IoT infrastructure, industrial systems, 5G MEC networks, and hybrid edge-cloud architectures.
Edge AI Deployment & Maintenance
Production deployment using containerized edge AI, Edge MLOps automation, fleet management, and OTA model updates.
Edge AI Data Pipeline & Storage Engineering
Efficient data pipelines for edge AI with real-time ingestion, preprocessing, edge caching, and privacy-preserving architectures.
Edge AI Training & Enablement
Team training on edge AI development, on-device optimization, TinyML, Edge MLOps, and platform-specific SDKs.
Applications We Have Built
Inferencing 120B GPT OSS on NVIDIA DGX Spark
JetsonLLM deploys containerized LLM inference on NVIDIA Jetson Orin Nano, enabling real-time text generation and analytics locally without cloud dependency, showcasing privacy-centric, ultra-efficient edge AI capabilities.
Fully Local Multilingual Voice Agent
Real-time voice AI deployed on NVIDIA DGX Spark with LiveKit orchestration, Whisper speech recognition, multilingual Riva text-to-speech, and local GPT-OSS 120B inference for enterprise speech automation.
Spark Vault
Spark Vault is a secure, on-premises enterprise search solution for medical documents on NVIDIA DGX Spark, combining containerized AI models and vector databases for rapid, private searches without cloud dependency.
Rack-Inspector: AI Pallet-Rack Safety Inspection
Discover how on-device AI vision models were engineered for edge deployment on ruggedized handhelds, enabling real-time structural defect detection in disconnected warehouse environments.
Want to test these edge and private models in real-time?
Explore interactive demonstrations deployed on physical Jetson & DGX Spark hardware.
Industries We Serve
Healthcare
Healthcare organisations deploying AI for clinical documentation, medical record intelligence, and diagnostic assistance require private AI systems where patient data never leaves the facility. We build HIPAA-aware private AI deployments on NVIDIA Jetson and DGX Spark hardware for clinical environments with strict data residency requirements.
Financial Services
Financial institutions processing customer transaction data, regulatory documents, and internal policy content require private AI environments where no data transits public infrastructure. We build on-premise AI systems with full audit logging and data isolation for financial services organisations operating under GDPR, FCA, and EU AI Act frameworks.
Legal
Law firms and legal departments handling client-privileged documents require air-gapped AI systems where professional liability obligations prohibit data from leaving controlled infrastructure. We build fully local AI environments for contract review, legal research, and document intelligence workflows.
Manufacturing
Manufacturing facilities requiring predictive maintenance, quality control, and process monitoring without internet connectivity use our edge AI deployments on ruggedised hardware designed for industrial environments.
Software Engineering
Software engineering organisations embedding private AI capabilities into their development infrastructure use our on-premise AI deployments for code intelligence, automated testing, and secure internal tooling where proprietary codebase data cannot leave their infrastructure.
We Build On The World's Best Platforms
Need to deploy private or edge AI?
Talk to our AI engineering team about building on-premise, air-gapped, or edge AI solutions tailored to your infrastructure.
How We Deliver Private AI & Edge AI Projects
Requirements Assessment
We begin by understanding your data sensitivity requirements, compliance obligations, connectivity constraints, and operational environment. This stage determines whether your use case is best served by a private on-premise AI deployment, an edge AI deployment on target hardware, a hybrid architecture, or an air-gapped system with no external network dependency. Output: a deployment model recommendation and initial architecture brief.
Architecture Design
We design the full system architecture covering hardware selection, model selection, data pipeline design, security boundaries, access controls, and integration points with your existing infrastructure. For private AI environments, this stage defines the sovereign perimeter and data residency rules. For edge AI deployments, this stage defines the device fleet architecture and Edge MLOps strategy.
Model Optimisation & Platform Validation
We select and optimise AI models for your target deployment environment. For private AI systems, this includes on-premise LLM configuration and private RAG pipeline design. For edge AI systems, this includes model compression through quantisation, pruning, and knowledge distillation to match the compute profile of the target hardware. A proof-of-concept validation confirms performance before full development begins.
Development & Integration
We build the complete private AI or edge AI system, including application logic, data pipelines, APIs, security controls, and all integration points with your enterprise infrastructure. All code is delivered with full documentation, version control, and production-ready build configurations.
Testing & Validation
Comprehensive testing covers model accuracy, inference latency, data isolation, access control enforcement, and system reliability across the deployment environment. For private AI systems, this includes verification that no data leaves the defined sovereign perimeter. For edge AI deployments, this includes offline reliability testing and on-device performance validation.
Deployment & MLOps Support
We deploy to your target environment and implement the operational layer: Edge MLOps pipelines, model monitoring, over-the-air update infrastructure for edge device fleets, and ongoing support covering model updates and performance optimisation as your requirements evolve.
Frequently Asked Questions
Everything you need to know about custom private AI, edge deployments, hardware selection, and data privacy.
What is the difference between private AI and cloud AI?
What hardware platforms do you deploy edge AI on?
Can you deploy AI in an air-gapped environment?
Which industries benefit most from private AI and edge AI deployments?
Ready to deploy private AI or edge AI on your terms?
Protect your sensitive data and operate with complete sovereignty. Partner with engineers who understand private models, edge hardware, and enterprise security.
