Custom Private AI and Edge AI Services for Enterprise
What Are Private AI and Edge AI Services?
Private AI services deploy AI on infrastructure you fully control, including on-premise hardware, air-gapped servers, and private cloud environments, ensuring your models, data, and outputs never leave your secure perimeter. Edge AI services run AI directly on cameras, sensors, industrial hardware, and edge devices, enabling real-time, offline processing with lower latency and bandwidth usage. Together, Private AI and Edge AI bring intelligence closer to where it's needed—inside your secure infrastructure or at the network edge. GenAI Protos delivers both as an integrated solution for enterprises that need secure, high-performance AI under complete control.
Start Your Private AI or Edge AI Project
Ready to Deploy AI in Your Enterprise?
Whether you need sovereign AI, an air-gapped deployment, or edge AI across your devices, we design and deliver secure AI systems from architecture to production.
Cloud AI sends your data to a third-party server for processing, which introduces data privacy risk, network latency, and ongoing per-token cost at scale. Private AI processes data on infrastructure you own and control, with no external transmission. Private AI is appropriate when data sensitivity, compliance requirements, offline operation, or long-term cost efficiency make public cloud AI unsuitable. For a detailed comparison, see our private AI vs cloud AI analysis.
Q1. What is the difference between private AI and cloud AI?
We deploy edge AI across NVIDIA DGX Spark, NVIDIA Jetson Orin, Google Coral Edge TPU, Intel Movidius with OpenVINO, AMD Kria, Qualcomm AI Stack, and TinyML platforms including Arduino and Raspberry Pi. Platform selection is based on your compute requirements, power budget, connectivity constraints, and the model complexity needed for your specific use case.
Q2. What hardware platforms do you deploy edge AI on?
Yes. Air-gapped AI deployment is a core capability of our private AI practice. We build AI systems that operate with zero internet dependency, where inference, data storage, and system management all happen within your controlled infrastructure. This is designed for clinical facilities, financial data centres, government environments, and any setting where network connectivity to external systems is prohibited or unavailable.
Q3. Can you deploy AI in an air-gapped environment?
Healthcare organisations with HIPAA data residency requirements, financial services firms under GDPR and FCA regulations, legal departments handling client-privileged documents, manufacturing facilities requiring offline AI at the production edge, and retail operations needing real-time on-device inference are the primary enterprise beneficiaries. The common requirement across all of them is AI that operates under their direct control rather than on shared public infrastructure.
Q4. Which industries benefit most from private AI and edge AI deployments?
A focused proof-of-concept covering one use case, one platform, and one deployment model typically takes three to six weeks from requirements assessment to working validation. A full production deployment covering system development, integration, security validation, and Edge MLOps infrastructure typically takes eight to sixteen weeks, depending on environment complexity, integration scope, and hardware availability. Multi-site or multi-platform deployments are scoped on a project basis.
Q5. How long does a private AI or edge AI deployment take?
Frequently Asked Questions About Private AI and Edge AI Services
Private AI. Edge AI. Fully Under Your Control.
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Deploy custom private AI and edge AI solutions on your own infrastructure, edge devices, or air-gapped environments. We design, build, and support secure AI systems that deliver real-time performance while keeping your data completely under your control.
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Custom Private AI and Edge AI Services for Enterprise
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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.
https://www.genaiprotos.com/industry/healthcare/
Healthcare
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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.
https://www.genaiprotos.com/industry/finance/
Financial Services
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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.
https://www.genaiprotos.com/industry/legal/
Legal
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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.
https://www.genaiprotos.com/industry/
Manufacturing and Industrial
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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.
https://www.genaiprotos.com/industry/software-engineering/
Software Engineering
Our Private AI and Edge AI Development Services
End-to-end delivery across the full private AI and edge AI lifecycle, from architecture design to production deployment and ongoing operation.
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.
GenAI Protos provides custom private AI and edge AI services that cover the full deployment spectrum. On one side, we build sovereign AI environments where sensitive data never leaves your controlled infrastructure. On the other, we deploy AI directly onto edge devices, from TinyML microcontrollers through to NVIDIA DGX Spark desktop AI supercomputers, for real-time inference without cloud dependency.
Whether your priority is private AI deployment for regulated environments, air-gapped inference for maximum data control, or on-device AI for low-latency edge operations, our engineering team designs, builds, and deploys the complete system from architecture through to production.
Deploy Private AI & Edge AI Services for Enterprise with GenAI Protos. We build secure, scalable on-premise and edge AI solutions tailored to your business.
Private AI & Edge AI Services for Enterprise
Build secure private AI and edge AI solutions with GenAI Protos. Deploy on-premise, air-gapped, or edge AI systems with complete data control.
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AI Built for Privacy, Performance, and Control
Private AI Deployment & Edge AI Services | GenAI Protos
Cloud
Local processing significantly cutting bandwidth usage and cloud storage fees.
High Cloud Costs
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On-device execution enables real-time responses by eliminating network transmission delays
Latency Issues
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Keeping sensitive data locally on-device minimizes exposure to external breaches
Privacy Concerns
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Operations continue autonomously without relying on constant, stable internet access.
Connectivity Gaps
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Running complex LLMs on battery-powered devices requires extreme optimization.
Hardware Constraints
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Bridging the gap between AI models and embedded firmware
Integration Complexity
Why Enterprises Are Moving AI Off the Public Cloud
Public cloud AI introduces cost, latency, compliance risk, and data exposure. Private AI and edge AI deployment eliminates all four.
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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.
Requirements Assessment
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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.
Architecture Design
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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.
Model Optimisation & Platform Validation
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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.
Development & Integration
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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.
Testing & Validation
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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.
Deployment & MLOps Support
A structured process designed for enterprise private AI and edge AI delivery, from initial assessment through to monitored production.
Private AI and Edge AI Deployment Process
Custom Private AI and Edge Solutions
Architecture
5G
ROI
Define high-impact edge AI applications, hybrid edge-cloud architectures, and 5G MEC deployment strategies for optimal ROI.
Strategic Edge AI Architecture & Use Case Design
NVIDIA
IoT
Development
Custom industrial edge AI, autonomous systems, IoT edge AI development across NVIDIA DGX Spark, Jetson, Google Coral, and TinyML platforms.
Edge AI Application Development (Remote & On-Site)
Setup
POC
Research
Platform selection, proof-of-concept development, and feasibility assessment for NVIDIA, Google, Intel, AMD, and Qualcomm edge AI platforms.
Edge AI Setup & Research Support
Testing
Validation
Hardware
Performance testing, accuracy validation, and hardware compatibility verification for real-time AI inference across edge device
Edge AI Testing & Validation
Integration
Hybrid
Seamless integration with IoT infrastructure, industrial systems, 5G MEC networks, and hybrid edge-cloud architectures.
Edge AI Integration Services
Deployment
MLOps
OTA
Production deployment using containerized edge AI, Edge MLOps automation, fleet management, and OTA model updates.
Edge AI Deployment & Maintenance
Data Pipeline
Privacy
Storage
Efficient data pipelines for edge AI with real-time ingestion, preprocessing, edge caching, and privacy-preserving architectures.
Edge AI Data Pipeline & Storage Engineering
Training
TinyML
Optimization
Team training on edge AI development, on-device optimization, TinyML, Edge MLOps, and platform-specific SDKs.
Edge AI Training & Enablement
Applications We Have Built
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LLM Inference
Edge AI
NVIDIA DGX Spark
Local Processing
Privacy-First
Containerized AI
Real-time Generation
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.
Inferencing 120B GPT OSS on NVIDIA DGX Spark.
Voice AI
Multilingual
Speech-to-Text
https://www.genaiprotos.com/solutions/local-multilingual-voice-agent/
NVIDIA Riva
Local Inference
LiveKit
Enterprise Automation
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.
Fully Local Multilingual Voice Agent
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Enterprise Search
Medical AI
https://www.genaiprotos.com/solutions/spark-vault-enterprise-search/
Vector Database
On-Premises
Document Search
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.
Spark Vault
Each one demonstrates a different combination of private AI and edge AI capability, from on-premise LLM inference at scale to fully air-gapped voice AI and private enterprise document intelligence.
Schedule a Solution Consultation
Build a Secure AI Environment That Fits Your Business
Every enterprise has unique security, infrastructure, and compliance needs. Whether you need private AI, edge AI, or a hybrid solution, our engineers will design and deploy a system that aligns with your business goals.
Custom Private AI and Edge AI Services for Enterprise
Deploy custom private AI and edge AI solutions on your own infrastructure, edge devices, or air-gapped environments. We design, build, and support secure AI systems that deliver real-time performance while keeping your data completely under your control.
Custom Private AI and Edge AI Services for Enterprise
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.
GenAI Protos provides custom private AI and edge AI services that cover the full deployment spectrum. On one side, we build sovereign AI environments where sensitive data never leaves your controlled infrastructure. On the other, we deploy AI directly onto edge devices, from TinyML microcontrollers through to NVIDIA DGX Spark desktop AI supercomputers, for real-time inference without cloud dependency.
Whether your priority is private AI deployment for regulated environments, air-gapped inference for maximum data control, or on-device AI for low-latency edge operations, our engineering team designs, builds, and deploys the complete system from architecture through to production.
What Are Private AI and Edge AI Services?
Private AI services deploy AI on infrastructure you fully control, including on-premise hardware, air-gapped servers, and private cloud environments, ensuring your models, data, and outputs never leave your secure perimeter. Edge AI services run AI directly on cameras, sensors, industrial hardware, and edge devices, enabling real-time, offline processing with lower latency and bandwidth usage. Together, Private AI and Edge AI bring intelligence closer to where it's needed—inside your secure infrastructure or at the network edge. GenAI Protos delivers both as an integrated solution for enterprises that need secure, high-performance AI under complete control.
Why Enterprises Are Moving AI Off the Public Cloud
Public cloud AI introduces cost, latency, compliance risk, and data exposure. Private AI and edge AI deployment eliminates all four.
Our Private AI and Edge AI Development Services
End-to-end delivery across the full private AI and edge AI lifecycle, from architecture design to production deployment and ongoing operation.
We Build On The World's Best Platforms
Our expertise spans the full spectrum of edge AI hardware.
Applications We Have Built
Each one demonstrates a different combination of private AI and edge AI capability, from on-premise LLM inference at scale to fully air-gapped voice AI and private enterprise document intelligence.
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.
Our Private AI and Edge AI Development Services
End-to-end delivery across the full private AI and edge AI lifecycle, from architecture design to production deployment and ongoing operation.
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 and Industrial
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.
Private AI and Edge AI Deployment Process
A structured process designed for enterprise private AI and edge AI delivery, from initial assessment through to monitored production.
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.
