Bringing Agentic AI On-Premises Without Compromising Security
Focus Group Technologies designed a secure on-premises Agentic AI and RAG architecture, creating a structured pathway from Proof of Concept to production and future air-gapped operation.
Project Snapshot
The Challenge
Bringing AI on-premises involved more than selecting AI compute.
The organisation needed to move AI capabilities currently dependent on public cloud services into a controlled on-premises environment, supporting both AI-assisted software development and document-based knowledge access.
The architecture needed to maintain control over sensitive data, permissions and authentication, while also addressing audit logging, network segmentation, monitoring, backup and container security.
It also needed a way to validate model performance, retrieval quality, concurrent usage and infrastructure requirements against real workloads before sizing the final production platform.
AI Use Cases
AI Coding Assistant
Locally hosted AI capabilities to support software development.
Document Knowledge Assistant
Secure AI-assisted access to existing corporate information.
The Solution
A secure, locally hosted Agentic AI and RAG architecture.
Focus Group Technologies designed an on-premises Agentic AI and Retrieval-Augmented Generation (RAG) architecture using locally hosted language models and containerised services.
The solution combines local AI models with vector search, document ingestion and reranking, with permission-aware retrieval designed to maintain existing source-system access rights.
Security was incorporated into the architecture through authentication, audit logging, network segmentation, monitoring, backup and container security.
A staged Proof of Concept was developed to validate model selection, retrieval accuracy, security controls, concurrent usage and infrastructure performance before production sizing.
AI INFRASTRUCTURE DESIGN
AI infrastructure designed around the workload, security and operating requirements.
The on-premises AI infrastructure combined locally hosted AI models with retrieval, access controls, security and containerised services, with a staged approach to validating the infrastructure required for production.
AI Compute
Locally hosted language models
Knowledge
Retrieval-Augmented Generation
Search & Retrieval
Vector search, ingestion and reranking
Identity & Access
Authentication and permission-aware retrieval
Security & Governance
Network segmentation, audit logging and monitoring
Platform
Containerised architecture with a pathway to air-gapped operation
AI INFRASTRUCTURE READINESS
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A structured pathway from Proof of Concept to secure production.
The architecture combined locally hosted AI models with retrieval, access controls, security and containerised services, with a staged approach to validating the infrastructure required for production.
Local Control
Sensitive AI workloads brought under a locally controlled architecture.
Validate Before Production
A structured Proof of Concept to validate performance, retrieval, security and infrastructure requirements.
Path to Air-Gapped Operation
An architecture designed to support future fully air-gapped operation.
FULL CASE STUDY
Explore the project in more detail.
Download the full case study for a more detailed look at the challenge, solution, on-premises AI infrastructure design and outcome.
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