Azure OpenAI Consulting
Azure AI Foundry and Azure OpenAI consulting - we deploy models in your own Azure tenancy, grounded on your governed data, with private networking, identity and logging done properly.
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Secure AI on Your Azure: Australia's Azure AI Specialists
Secure AI on the Microsoft stack means models running in your own Azure tenancy, grounded on your governed data, with the networking, identity and logging done properly - not a public chatbot with your secrets pasted in. SureLogic builds exactly that. We design and implement Azure AI Foundry and Azure OpenAI architectures - the landing zone, private networking, content filters, grounding through Azure AI Search or Fabric, and cost controls - so your organisation gets the power of frontier models without sending data where it should not go. It is the pro-code path for teams that want models deployed on infrastructure they own and control, with governance built in.
In Your Own Tenancy
Models run in your own Azure tenancy, so prompts and data stay inside your environment under your controls, rather than going to a public service you do not govern.
Private by Design
Private networking, private endpoints and your identity model keep AI traffic and access inside your network, governed by the same controls that protect the rest of your Azure estate.
Grounded on Your Data
We ground the models on your governed sources through Azure AI Search or Fabric, so answers reflect your actual, current business rather than the model's general training.
Safety Built In
Content filters, abuse monitoring and deliberate model governance keep generated output appropriate and compliant, giving you frontier capability with the guardrails an enterprise needs.
Cost Under Control
Cost controls, quotas and monitoring make AI usage and spend visible and governed, so the platform stays affordable and accountable to finance as it scales across workloads.
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Experience Governed Data
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Our Azure AI & OpenAI Services
Azure AI Landing Zone
Stand AI up on a foundation built for the enterprise, not a quick experiment. We design and deploy an Azure AI landing zone - the secure, governed environment your models, agents and applications run in. Subscriptions, resource structure, policy and guardrails are set up to Microsoft's well-architected patterns, so your AI workloads start on solid ground. Instead of a sprawl of ad-hoc resources nobody can secure, you get a managed platform your team and ours can build on confidently.
Private Networking & Identity
Keep your AI traffic and identity inside your control. Public endpoints and shared keys are how AI projects leak. We configure private networking, private endpoints and your identity model so the models, data and applications communicate over your network with proper authentication, not the open internet. Access is governed through your existing identity provider, so the same controls that protect the rest of your Azure estate protect your AI. Your data and prompts stay where they belong.
Model Deployment & Content Safety
Deploy the models you need, with safety built in. We deploy Azure OpenAI and Azure AI Foundry models into your tenancy and configure content filters, abuse monitoring and the safety controls that keep generated output appropriate and compliant. Model choice, versioning and capacity are managed deliberately, so you run the right model for each workload under proper governance. You get frontier capability with the guardrails an enterprise actually needs, rather than an ungoverned connection to a public API.
Grounding on Your Governed Data
Ground the models on your data so answers are real. A model that cannot see your data can only generalise; we ground it on your governed sources through Azure AI Search, Microsoft Fabric or your retrieval layer, so answers reflect your actual business, current and access-controlled. This is where Azure AI stops being a clever demo and starts being useful - the same grounding discipline we apply across our RAG and semantic work, implemented on your Azure stack.
Cost Controls & Observability
Run AI you can afford and observe. AI costs surprise organisations that deploy without controls, and an unobserved system cannot be governed. We put cost controls, quotas and budgets in place, and the logging and monitoring that show what your models are doing, what they cost, and how they perform. You see usage and spend clearly, catch problems early, and can prove to security and finance that your AI is governed, accountable and under control as it scales.
190+ Australian organisations Choose Surelogic.
570+ Successful Projects
We bring deep architectural experience to every engagement, having engineered hundreds of secure, scalable data platforms and semantic models across Australian industries.
100% Australian Owned
We never farm your data assets out to offshore teams. Our team is entirely Australian-based, guaranteeing flawless communication, immediate timezone alignment, and strict data sovereignty.
Your Tenant, Your IP
We build directly inside your cloud environment so the infrastructure is entirely yours - including semantic models, custom code, and IP. You maintain absolute control without vendor lock-in.
Partnering with SureLogic was a game-changer for Cupid Media. They turned our outdated reporting process into a modern, data-driven strategy that delivered real-time insights and cost savings we didn’t think possible. Their expertise in BigQuery, Tableau and machine learning brought immediate value to our business.
Ben Snart
Chief Product Officer, Cupid Media
TRUSTED BY
Our Proven Azure AI Framework
Our senior architects build a secure Azure AI landing zone, deploy your models grounded on governed data, and keep cost and risk under control.
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Architecture & Landing Zone Design
We design your Azure AI landing zone - subscriptions, networking, identity, policy and guardrails - to Microsoft's well-architected patterns, and define how models will be deployed, secured and grounded. You receive a landing-zone reference architecture and a deployment plan, so AI starts on a governed foundation rather than a sprawl of ad-hoc resources.
Deploy, Secure & Ground
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We build the landing zone, configure private networking and identity, deploy Azure OpenAI and AI Foundry models with content safety, and ground them on your governed data through AI Search or Fabric. Each layer is secured and tested, so your models run in your tenancy on your data, with the guardrails an enterprise needs.
Control Cost, Observe & Handover
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We put cost controls, quotas and monitoring in place so usage, spend and performance are visible and governed, then hand over the architecture and operating model. Many clients extend the platform across more workloads on the same foundation. You leave with secure, grounded AI on infrastructure you own, accountable to security and finance.
Azure AI & OpenAI FAQs
What is Azure AI Foundry and how does it relate to Azure OpenAI?
Azure OpenAI provides frontier models like GPT inside your Azure tenancy, while Azure AI Foundry is Microsoft's broader platform for building, grounding, evaluating and operating AI applications and agents around them. Together they let you deploy powerful models on infrastructure you own, grounded on your data and governed by your security. We architect and implement both, so you get enterprise AI on the Microsoft stack rather than an ungoverned connection to a public API.
Grounding rests on governed data - see our trusted data architecture and governance page.
Is Azure OpenAI more secure than using public ChatGPT?
Yes, when it is architected properly - Azure OpenAI runs in your own tenancy, so your prompts and data stay within your Azure environment rather than a public service. With private networking, your identity model, content filters and logging, the same controls that protect the rest of your Azure estate protect your AI. The security comes from the architecture around the model, which is exactly what we design and build, not from the model alone.
How do we keep our data private when deploying AI in Azure?
By configuring private networking, private endpoints and your identity provider, so models, data and applications communicate over your network with proper authentication rather than public endpoints and shared keys. Grounding is done on your governed sources inside your tenancy, and every interaction is logged. Your data and prompts stay where they belong, under the same controls as the rest of your estate, which is the whole point of deploying AI in your own Azure environment.
Should we use Azure AI Foundry or Copilot Studio?
It depends on the build - Copilot Studio is the low-code path for custom copilots over Microsoft 365, while Azure AI Foundry and Azure OpenAI are the pro-code path for full control over architecture, models and grounding. Many organisations use both, for different needs. We assess your requirements, team and governance and route you to the right one, rather than forcing a single tool onto every use case.
Confirm your data is ready either way - see our AI Data Readiness Audit page.
How do we control the cost of running AI in Azure?
With cost controls, quotas and budgets configured from the start, plus monitoring that shows what your models are doing and what they cost. AI spend surprises organisations that deploy without these, because token usage scales quietly. We put the controls and observability in place so usage and cost are visible, problems are caught early, and you can prove to finance that your AI platform is governed and under control as it scales.
78% of new clients engage us for recurring data partnerships
Establish a robust data infrastructure. Complete the form to request a confidential strategy session, or inquire about our fixed-fee $5,000 AI Data Readiness Audit to expose structural risks.
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