Secure, Localised LLM Hosting & Deployment
Public, multi-tenant AI APIs route your prompts through infrastructure you do not control. NetEvolution deploys open-weight large language models inside your own Azure tenant or on-premise hardware — keeping proprietary data within agreed boundaries.
01 / problem
Keep your data inside the boundary you choose
Public cloud AI APIs can offer strong enterprise privacy terms, but they still create an external processing dependency. For organisations handling personal data, commercially sensitive documents or regulated information, private deployment can provide a clearer boundary and greater architectural control.
Localised deployment removes the third-party dependency. Modern open-weight models now handle knowledge retrieval, summarisation, drafting and reasoning tasks to a standard that makes private deployment genuinely practical — not a compromise.
The engineering challenge is real though: model selection, GPU capacity, retrieval pipelines, access control and monitoring all have to be designed and operated properly.
02 / delivered
What we deliver
- Model selection and benchmarking against your actual workloads — not leaderboard scores.
- Deployment inside your Azure tenant or on-premise hardware, sized to your usage and budget.
- Retrieval-augmented generation pipelines connecting the model to your knowledge bases, with source attribution.
- Custom Model Context Protocol (MCP) servers exposing your tools and data to the model under controlled permissions.
- Access control, audit logging and usage monitoring integrated with your existing identity stack.
- Operational documentation and handover so your team can run, update and extend the platform.
03 / use cases
Common deployments
Private knowledge retrieval
Staff query policies, procedures, contracts and project history in plain language — with answers grounded in your documents and cited back to source.
Document drafting & analysis
Bid responses, reports and correspondence drafted against internal templates and precedent, inside your boundary.
Agent backends
Local models can power the agentic workflows described in our automation service, with external model APIs removed from the loop where the architecture requires it.
04 / controls
Controls engineered in
- Data remains within your tenant or hardware boundary — architectures are designed to reduce third-party exposure.
- Retrieval is permission-aware: users only receive answers drawn from documents they are entitled to see.
- Every prompt, retrieval and response is logged for audit and quality review.
- Model updates are versioned and tested against regression suites before deployment.
05 / process
How an engagement runs
01
Assess
We review your data sensitivity, workloads and infrastructure to determine the right model class and hosting pattern.
02
Deploy
The model and retrieval stack are provisioned inside your boundary with access controls and logging from day one.
03
Ground
Your knowledge bases are connected through governed retrieval pipelines and MCP tooling.
04
Operate
Monitoring, evaluation and update procedures are established, with full handover to your team.
06 / evidence
Related delivery snapshot
See the bespoke observability and control tooling built for a university's integration layer — the same pattern we apply to internal LLM deployments: instrumentation and recovery controls your team can run.
Read the University of London case studyExplore what private AI could do with your data
An architecture review identifies which workloads suit a localised deployment, what infrastructure it needs and what it would cost to run — before you commit.
Request an architecture review