Transforming US Government Operations with Private LLM: Secure, Scalable, and Future Ready
Discover how we enabled a pioneering provider of tax and workers' compensation solutions in the United States to adopt a private LLM hosted in Azure, paving the way for secure and efficient AI-driven operations across local government solutions.
Maintain data sovereignty
Keep sensitive information under your control—no external API dependencies, complete compliance governance.
Performance proven
CPU vs GPU trade-offs tested and documented.
Ready to scale
Blueprint and framework roadmap for multi-agent deployment.
At a glance
Private, secure LLM adoption — tax and workers' compensation solutions provider
Single POC engagement (8 weeks), with a documented roadmap for scaling to multi-agent AI deployment across local government workflows
Enabling AI-powered services without relying on external GPT services like OpenAI, which posed compliance and data security risks
Microsoft Azure, Llama 3 (8B), GPU-enabled Azure VMs, RAG, Azure AI Search, Python
POC completed in 8 weeks; validated technical feasibility, optimized infrastructure decisions, and ensured future readiness
A pioneering provider of tax and workers' compensation solutions in the United States
From API dependency to private, secure AI operations
Local government technology providers face growing pressure to bring AI into citizen-facing and internal workflows, but doing so through external GPT services introduces real exposure. For a provider handling tax and workers' compensation data, that exposure isn't hypothetical—compliance obligations and data security requirements make outside API dependency a genuine operational risk.
At the same time, going private isn't a given either. Hosting a large language model in-house raises its own open questions: whether a private LLM can actually match the scalability, security, and performance the use cases demand, and whether the infrastructure cost of doing so—particularly GPU-based hosting—can be justified without a clear, benchmarked cost-benefit picture.
- Compliance and data security risks from relying on external GPT APIs
- Uncertainty around private LLM performance in Azure vs. public alternatives
- Lack of clarity on infrastructure costs—unclear GPU cost-effectiveness at scale
- No benchmarking data comparing CPU and GPU workload performance
- Missing deployment strategy for securely hosting LLMs across local government clients
- Regulatory constraints requiring data residency and governance control
- Limited roadmap for agentic AI integration across analytics and workflows
A secure, benchmarked Proof of Concept
Techment collaborated with the client to develop a Proof of Concept (POC) that demonstrated the feasibility of hosting a private Llama 3 (8B) model within a secure Azure environment. This initiative laid the foundation for the client's long-term AI strategy, ensuring private, secure, and scalable LLM integration across their analytics framework and beyond.
GPU-Enabled Azure VMs
Deploying the model on GPU-enabled Azure VMs.
RAG Chatbot
Building a Retrieval-Augmented Generation (RAG) chatbot using the client's domain-specific knowledge base.
Controlled Tests
Running controlled tests to capture response quality, speed, and cost.
CPUs vs GPUs Benchmarking
Benchmarking the performance of queries on CPUs vs GPUs to guide infrastructure planning.
Technical feasibility, cost clarity, and strategic validation
The POC delivered tangible outcomes that validated technical feasibility, optimized infrastructure decisions, and ensured future readiness.
Deployment
Successful deployment of Llama 3 (8B) model in Azure with private hosting.
Chatbot
Developed a chatbot integrated with the client's knowledge base for contextual Q&A.
Benchmarks
Detailed comparison of response times between CPU and GPU workloads.
Blueprint
Private LLM Deployment Strategy with a security-first approach.
Roadmap
Documented roadmap for deploying multiple AI agents to enhance local government workflows.
Cost
Clear insights into infrastructure costs and scalability requirements.
Validation
Confirmed private LLM adoption as both technically feasible and strategically valuable.
How we did it
Techment partnered with Microsoft Azure teams to provision GPU-enabled infrastructure and deploy the Llama 3 (8B) model in a secure environment.
Why this matters beyond this project
Local government technology providers are under growing pressure to bring AI into their services without exposing sensitive citizen, tax, and workers' compensation data to external GPT providers. This POC shows that a private LLM hosted in Azure can deliver the scalability, security, and performance local government use cases demand.
Beyond the prototype chatbot itself, the engagement produced a deployment blueprint covering hosting strategies and security safeguards, plus a documented roadmap for deploying multiple AI agents across local government workflows—giving the client a repeatable, security-first path to scale private LLM adoption rather than a one-off proof of concept.
Tech stack
Frequently asked questions
Relying on external GPT services posed compliance and data security risks for a provider handling tax and workers' compensation data. The POC validated that a private Llama 3 (8B) model hosted in Azure could deliver the scalability, security, and performance required for local government use cases, while keeping data within a secure, private environment.
Because hosting LLMs on GPUs is resource-intensive, the team ran a clear cost-benefit analysis comparing CPU vs GPU performance to determine infrastructure requirements for future rollouts. This produced a benchmarked estimate of roughly $4,200 per month in ongoing GPU infrastructure costs, on top of the $10,000 POC execution cost.
The POC deployed the Llama 3 (8B) model on GPU-enabled Azure VMs, built a Retrieval-Augmented Generation (RAG) chatbot using the client's domain-specific knowledge base, ran controlled tests to capture response quality, speed, and cost, and benchmarked CPU vs GPU query performance to guide infrastructure planning.
The POC was successfully completed in 8 weeks.
Alongside the working RAG chatbot and performance benchmarks, the client received a deployment blueprint covering hosting strategies, security safeguards, and provisioning guides, plus a documented roadmap for deploying multiple AI agents to enhance local government workflows.
The initiative laid the foundation for the client's long-term AI strategy, ensuring private, secure, and scalable LLM integration across their analytics framework and beyond—giving other local government technology providers a validated, security-first pattern for private LLM adoption.
Read the full story
Discover how we enabled a pioneering provider of tax and workers' compensation solutions in the United States to adopt a private LLM hosted in Azure, paving the way for secure and efficient AI-driven operations across local government solutions.
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