Public-pattern deployments
Azure OpenAI is deployed without the private networking an enterprise workload needs.
Microsoft AI
The same models, inside your security boundary.
Azure OpenAI gives you leading models with enterprise controls — private networking, no training on your prompts, and Azure-native identity. We deploy it so your data stays governed, costs stay visible (token usage adds up fast), and the whole thing fits your existing security model instead of being a shadow-IT side door.
Discuss Azure OpenAI
Architecture
flowchart LR
ID[Entra ID] -. auth .-> APP
subgraph VNET[Private VNet - no public egress]
APP[Your App] --> PE[Private Endpoint]
end
PE --> AOAI[Azure OpenAI]
AOAI --> DATA[Your Data - RAG]
MON[Cost and Usage Monitor] -. watches .-> AOAI
0 tokens
Est. cost: $0.000
Business challenges
Azure OpenAI challenges we solve.
Every recommendation starts with business pressure, technical risk and the operating model required after launch.
Ungrounded responses
Without RAG foundations, responses are not grounded in the organization's own data.
No content safety layer
Generated content is not filtered for policy or safety before reaching users.
Solution overview
Azure OpenAI designed for production readiness.
We deploy it so your data stays governed, costs stay visible (token usage adds up fast), and the whole thing fits your existing security model instead of being a shadow-IT side door.
Private AI architecture
Private Link and Key Vault keep traffic and secrets inside your approved network boundary.
RAG foundations
Ground responses in your own data instead of generic model knowledge.
Content safety controls
Apply policy-aligned filtering before generated content reaches users.
Operational monitoring
Track token cost, latency and usage with Application Insights.
Architecture model
A practical delivery architecture before implementation begins.
We define the target operating model, controls, integration points and ownership path before building, so the solution can be supported after launch.
CloudevTech
Enterprise delivery model
01
Discover
02
Architect
03
Implement
04
Validate
05
Operate
Our approach
Structured delivery from discovery to operational handover.
Every engagement is shaped around the service goal, current constraints and the operating model your team needs after launch.
Scope the use case
Define what the model needs to answer, generate or automate, and what data it should draw from.
Architect privately
Design Private Link, Key Vault and network boundaries so traffic and secrets stay contained.
Ground with RAG
Connect the model to your own data so responses are relevant, not generic.
Add safety and monitoring
Apply content filtering and Application Insights monitoring before go-live.
Business benefits
Outcomes designed for decision makers and delivery teams.
Benefits are framed around measurable improvement, operating confidence and reduced delivery risk.
Private AI architecture
Private Link and Key Vault keep Azure OpenAI traffic and secrets inside approved boundaries.
Grounded, relevant responses
RAG foundations connect outputs to the organization's real data.
Content safety controls
Policy-aligned filtering reduces the risk of unsafe or off-policy output.
Technology stack
Implemented with proven platforms and tools.
Technology choices are confirmed during discovery, with a preference for reliable, maintainable platforms your team can support.
Azure OpenAI
Azure AI Search
Azure Key Vault
Private Link
Application Insights
Enterprise AI agents needed access to production tools. Security needed to sleep at night.
Controlled AI-agent tool access with least-privilege scoping, mediated APIs and BigQuery audit logging.
Read the story ->
FAQ
Common questions before engagement.
Short answers to common planning questions for Azure OpenAI.
Is our data used to train models?
No. Your prompts and data aren't used to train the underlying models.
How do we control cost?
We set up usage monitoring and limits so a runaway integration can't quietly burn your budget.
Enterprise consultation
Book a consultation