Local & Private LLM Infrastructure

Run large language models in your own environment - local or private hosting, controlled access, predictable costs, and integration-ready architecture for business use cases.

Not every organisation can - or should - send sensitive prompts and documents to public LLM endpoints by default. Regulatory requirements, client confidentiality, data residency constraints, and commercial risk can all drive the need for a controlled LLM runtime. A private LLM approach focuses on where the model runs, how it is accessed, how data is handled, and how you maintain reliability and governance over time.

LW IT Solutions designs and implements local and private LLM infrastructure that is technically viable and operationally supportable. We can run open-source models locally (for example using tools such as Ollama for local model orchestration) or deploy private inference services (for example using high-performance serving frameworks such as vLLM) depending on your requirements. We focus on the full platform: hardware sizing, model selection, access control, observability, cost discipline, and safe integration patterns for RAG, agents, and tool access.

Talk through your requirements and leave with a clear next-step plan.

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Service Overview

Highlights

Support for open-source model hosting using tools such as Ollama and vLLM
Clear separation of network, identity, and data access boundaries
GPU and hardware sizing aligned to real workload expectations
API-first design suitable for RAG, agents, and internal applications
Operational focus with monitoring, logging, and lifecycle guidance

Business Benefits

Keep sensitive prompts and documents within your controlled environment
Meet data residency, regulatory, and client confidentiality requirements
Achieve predictable inference costs compared to usage-based public APIs
Apply clear access control and auditability for internal AI usage
Provide a stable internal platform for AI-enabled applications and workflows

Typical use cases

Organisations with strict data residency or confidentiality requirements
Internal knowledge assistants over sensitive document sets
AI drafting and analysis for regulated or client-owned data
Teams evaluating open-source models before wider rollout
Engineering groups building AI features without public API dependency

Objectives & deliverables

What Success Looks Like

Enable AI use cases where data sensitivity or regulatory constraints require controlled execution
Improve predictability of cost by running models in an environment you govern
Reduce risk through access control, auditability, and a clear data-handling model
Provide a reliable internal AI capability for knowledge assistants, drafting, and automation
Create an integration-ready platform for agents, workflows, and internal applications

What You Get

Private LLM architecture pack: reference design, security boundaries, and operational ownership model
Implemented runtime environment (local or private-hosted) aligned to your constraints and hardware profile
Access controls and integration endpoints (API) for approved apps/workflows
Monitoring and operational runbooks for reliability and ongoing maintenance
Model lifecycle guidance: versioning, evaluation, and controlled rollout approach
Backlog of enhancements: RAG integration, tool integrations, agent orchestration, and optimisation opportunities

How It Works

Discovery - confirm constraints (data, network, compliance), target use cases, and success measures.
Design - define architecture, serving approach, access model, monitoring, and operational ownership.
Build - deploy the runtime, implement access controls, and configure the serving layer.
Validate - test performance, concurrency, and failure modes; confirm data-handling expectations.
Integrate - expose APIs and integrate into target applications and workflows as scoped.
Operate - handover runbooks and establish a roadmap for continuous improvement.

Engagement Options

Local Deployment - single-node or small-cluster LLM runtime for controlled environments
Private Cloud Hosting - isolated inference service with defined access boundaries
Pilot Platform - limited-scope build to validate models, cost, and performance
Platform Scale-out - expand capacity, resilience, and integration after pilot

Common Bundles

Customers who use this service often bundle with these services

RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.

Data Strategy & Architecture
Define a clear data strategy and target architecture that aligns platforms, governance, security and cost with measurable business outcomes.

Architecture Documentation (HLD/LLD)
Produce clear HLD and LLD documentation that records architecture decisions, diagrams, security considerations, and operating assumptions for aligned delivery.

Agentic AI & Orchestrated Workflows
Design and deliver agentic AI workflows with multi-step orchestration, approvals, monitoring, and guardrails for controlled execution across business systems.

API & System Integrations
Design and implement API integrations connecting business systems with secure authentication, retries, logging, and supportable middleware patterns operations.

MCP Server Builds & Tool Integrations
Build secure MCP servers and tool integrations that expose data and actions to AI agents with governed access and deployment.

Backend API Development (FastAPI/Node)
Design and build backend APIs with clear contracts, secure authentication, observability, and cloud-ready deployment using FastAPI or Node.js.

SSO & Enterprise App Integrations
SSO and enterprise application integrations using Microsoft Entra ID, standardising access, authentication, and user lifecycle management across SaaS platforms.

Secure API Development Workshop
Practical developer workshop covering secure API design, authentication, authorisation, OWASP API risks, logging, rate limiting, and secrets management.

n8n Workflow Automation
Design and build n8n workflows with secure self-hosting, secrets management, governance, and production-ready automation across integrated systems platforms.

Information Protection & Sensitivity Labels
Design and deploy Microsoft Purview sensitivity labels to classify data, apply protection controls, and support safer collaboration across Microsoft 365.