Azure AI Consulting Services · Microsoft-Specialist
The gap between a working demo and something your organisation actually runs on is wider than most expect.
Data isn't ready. Governance isn't defined. The integration is more complex than the prototype suggested. We've seen this pattern often enough that we've built a practice around solving it — strategy through to production, on Azure, for Australian organisations.
Azure gives you access to some of the most capable AI infrastructure available — Azure OpenAI, Azure AI Foundry, Azure AI Search, Document Intelligence. What it doesn't give you is a clear path from the demo to something your organisation depends on. That path is where most Azure AI projects run into trouble.
The Azure AI tooling is only as good as the data it connects to. In most organisations, the data is fragmented, inconsistently structured, or stored in systems that AI can't easily reach. Getting from "we have data" to "our AI can use this data reliably" is a significant piece of work — and it's often underestimated at the start.
Who can ask the AI what? What happens when it gets something wrong? How do you know what it said and to whom? These questions are easy to defer in a proof of concept. They become critical in production — especially in regulated industries where audit trails and access controls aren't optional.
Azure AI spans Azure OpenAI, AI Foundry, AI Search, Document Intelligence, Machine Learning, and more. Making the right architectural choices — which services to use, how to combine them, what to build versus what to buy — requires someone who works in this stack every day. Generic cloud consultants rarely bring that depth.
Deploying an AI agent and having people actually use it are two different outcomes. Without thoughtful persona design — the right tone, the right scope, the right integration into how people work — even technically sound deployments see poor uptake. Adoption has to be engineered as deliberately as the architecture.
Australia's Voluntary AI Safety Standard, evolving privacy regulations, and sector-specific frameworks create real obligations around how AI is designed and deployed. Building responsible AI controls in from the start is significantly cheaper than retrofitting them — but it requires knowing what to build in the first place.
Azure AI models and services update frequently. An AI deployment that works well at go-live needs active management to stay current — prompt tuning, model version upgrades, capability expansion as new services become available. Most organisations aren't equipped to do that in-house, and it doesn't fit neatly into a project model.
What we deliver
Every engagement we take on sits inside a practice that has been doing this on Azure for over two decades. We cover strategy through to managed service — and every piece of work is done by an Australian team, on the Microsoft stack, in your environment.
Service 01 of 06
Before you deploy anything on Azure AI, you need to know where to start — which use cases are worth prioritising, whether your data can support them, and what the sequencing looks like. Most organisations have strong intuitions on this. Few have a structured, evidence-based answer. The Discovery Workshop produces that answer in two weeks.
Structured workshops with business and technical stakeholders to identify, categorise, and prioritise AI use cases across your organisation — ranked by value, feasibility, and data readiness.
A review of your existing data sources, quality, structure, and accessibility — identifying which datasets can support AI immediately and what preparation is needed for the rest.
A concrete, sequenced deployment plan — with recommended Azure services, architecture patterns, data preparation steps, and estimated timelines for each stage. Fixed deliverable, fixed fee.
Alignment of your planned AI deployments with Australia's Voluntary AI Safety Standard, relevant sector frameworks, and Microsoft's Responsible AI principles — governance by design, not afterthought.
Service 02 of 06
Azure AI Foundry is the production environment where enterprise AI gets built and managed. Getting the foundation right — the right services selected, deployed correctly, with proper security controls and model routing in place — determines how much friction you encounter for everything that comes after. We design and build this foundation every engagement.
Architecture, deployment, and configuration of Azure AI Foundry as the enterprise AI platform inside your Azure tenancy — including project structure, connections, and model deployments.
Integration with Microsoft Entra ID for role-based access control, private endpoint configuration, network isolation, and key vault management — so your AI platform meets your security baseline from day one.
Orchestration layer design using Azure Prompt Flow and Microsoft Semantic Kernel — the infrastructure that connects models to your data, manages context, and handles multi-step AI workflows reliably.
Azure Monitor integration, content safety configuration, cost management guardrails, and AI evaluation pipelines — so you have visibility into performance and safety from go-live, not once something goes wrong.
Service 03 of 06
Azure OpenAI gives you access to GPT-4o and other frontier models inside your Azure environment — meaning your data doesn't leave your tenancy, and the service runs under your existing Azure governance controls. Getting value from it means connecting those models to the right data, in the right way, with the right guardrails. That's where most of the integration work sits.
Architecture and implementation of RAG pipelines connecting Azure OpenAI to your document libraries, knowledge bases, and structured data — so models answer from your content, not from generic training data.
Indexing your SharePoint libraries, document repositories, and structured datasets in Azure AI Search — creating the retrieval layer that keeps AI responses accurate, sourced, and current.
Ingestion, extraction, and structuring of PDF, Word, and scanned documents using Azure AI Document Intelligence — preparing unstructured content so it can be reliably queried by AI models.
Azure AI Content Safety configuration, system prompt engineering, and output validation logic — ensuring AI responses stay within appropriate scope and meet your compliance obligations.
Service 04 of 06
Conversational AI that answers questions is one thing. Agentic AI that takes action — creates records, triggers workflows, retrieves live data, routes tasks — is a different level of capability. Building agents that work reliably in production, inside your systems, with appropriate human oversight, requires a different kind of engineering. This is where QBot sits, and where our deepest delivery experience lies.
Design and deployment of multiple AI agents with distinct personas, scopes, and data access — a staff assistant, a student-facing agent, a customer portal agent — each governed by role-based access controls via Microsoft Entra ID.
Connecting AI agents to your live operational systems — Dynamics 365, ServiceNow, Salesforce, SAP — so they can retrieve real data and take action, not just answer from static documentation.
Agent-triggered Power Automate flows that move tasks, create records, send notifications, and update systems — turning conversational interactions into real operational outcomes.
Deploying agents natively inside Microsoft Teams, SharePoint, and other M365 surfaces — where your people already work, without requiring them to adopt a separate tool.
Service 05 of 06
AI is only as accurate as the data it draws from. If your data is fragmented across systems, inconsistently maintained, or stored in formats that AI can't efficiently index — the AI will reflect that. Preparing your data platform for AI is often the most impactful work you can do before deploying models. We deliver this on Microsoft Fabric, Azure Data Factory, and Azure Synapse Analytics.
Architecture and implementation of a unified data platform on Microsoft Fabric — bringing together OneLake storage, data pipelines, and analytics into a single governed environment that AI can reliably query.
Azure Data Factory pipelines connecting your source systems — SharePoint, Dynamics, SQL databases, external APIs — to a centralised, AI-ready data layer with scheduled refresh and transformation logic.
Design and implementation of vector stores using Azure AI Search or Azure Cosmos DB — the retrieval layer that makes large document sets queryable by AI models at speed and with high relevance.
Data classification, lineage, and access policy configuration in Microsoft Purview — so your AI deployment inherits the governance controls that apply to your data, not a separate set of rules.
Service 06 of 06
An Azure AI deployment isn't a project that ends at go-live. Models update, new capabilities become available, usage patterns change, and your organisation's needs evolve. Managing that well requires an active, ongoing relationship with the platform. Our managed service is built around fortnightly sprints, a named Technical Lead, and a roadmap that keeps your AI platform ahead of your requirements — not chasing them.
Ongoing capability development in fortnightly sprints — new integrations, expanded knowledge bases, additional personas, prompt tuning — prioritised against your roadmap and delivered iteratively.
Active monitoring of AI agent accuracy, response latency, user adoption, and content safety metrics — with tuning cycles that keep performance high as usage grows and data changes.
Managed upgrades when Azure OpenAI model versions update — evaluating new models against your use cases, testing in a staging environment, and deploying with zero disruption to production.
Monthly sessions with your stakeholders to review usage data, progress against the roadmap, and emerging requirements — keeping your AI strategy connected to your operational reality.
How we work
The most common reason Azure AI projects stall is not technical. It's the absence of a structured, sequenced approach that moves from strategy to production in steps that are understandable to the business. We've designed our engagement model around that problem.
We map your AI use cases, assess data readiness, review your current Azure environment, and produce a sequenced AI deployment roadmap. You leave with a concrete plan — not a generic strategy document. Fixed deliverable, fixed fee, no obligation to proceed.
Architecture and deployment of the Azure AI platform inside your tenancy — AI Foundry, OpenAI model deployments, AI Search indexes, and the first production AI agent or application. Real deployment, not a pilot, at the end of this phase.
Additional agents, deeper system integrations, more complex agentic workflows, and data platform improvements — delivered in prioritised sprints as your requirements grow and your confidence in the platform builds.
Continuous monitoring, model updates, capability development, and roadmap reviews — delivered by a named Technical Lead who knows your platform. Your AI needs will keep evolving; this service is built around that reality.
In production at Australian organisations
Every client reference we make is a live production deployment — running on Azure, used daily, integrated into real operations. The distinction between a pilot and a production system matters significantly in how you assess a prospective partner.
Staff AI assistant deployed across NRMA's operations — connecting Azure OpenAI to internal policy documentation, product guides, and operational knowledge bases. Staff can access accurate, sourced answers without navigating complex document repositories. Built and running inside NRMA's own Azure tenancy.
Microsoft invited Antares to solve a problem in Dr David Kellermann's engineering course at UNSW's School of Mechanical Engineering. With 500+ students collaborating through Teams, questions were arriving faster than any tutor team could handle. Antares built QBot — an AI chatbot integrated into Microsoft Teams using Microsoft Cognitive Services — to intercede, learn, and answer student questions on behalf of tutors. Pass rates rose from 65% to 85%. Students reported a 99% satisfaction score. Dr Kellermann: "What we've done is totally unprecedented. Antares was fantastic."
Multi-persona AI platform serving staff, students, and parents with distinct agents — each with different data access, tone, and scope. The student-facing agent handles curriculum and admin queries; the staff agent surfaces internal knowledge and policy. Fully contained inside Haileybury's Azure tenancy.
AI-powered staff assistant deployed across Mission Australia's national operations — connected to internal policy, HR knowledge, and service delivery documentation. Supports a workforce spread across sites and service types with consistent, accurate information access, reducing the administrative burden on central teams.
Choosing the right partner
There are several types of organisations you could engage for Azure AI consulting. Each has genuine strengths. Being clear about those differences is more useful to you than claiming to be the best at everything.
| Antares Solutions Microsoft-specialist SI |
Avanade / Accenture Global SI / joint venture |
KPMG / Big 4 Management consulting |
|
|---|---|---|---|
| Microsoft focus | Microsoft-only practice | Deep Microsoft focus | Platform-agnostic advisory |
| Azure AI engineering depth | Deep — AI Foundry, OpenAI, Semantic Kernel in production | Deep at senior levels; delivery quality varies by team | Strategic advisory focus; engineering delivered by subcontractors |
| Australian delivery | 100% Australian team — no offshore handoff | Australian lead; significant offshore delivery | Predominantly Australian |
| Mid-market and specialist sectors | Core market — education, NFP, health, financial services | Optimised for large enterprise programs; limited mid-market fit | Focus on regulated enterprise; not sector-specialist for education or NFP |
| Engagement size and speed | Fixed-fee Discovery Workshop in 2 weeks; first production deployment in weeks | Large programs; long scoping and contracting cycles | Advisory engagements typically run for months before build begins |
| Ongoing managed service | Fortnightly sprints, named Technical Lead, monthly roadmap reviews | Full managed service capability — suits large enterprise AMS | Advisory — managed delivery is not a core offering |
| Best for | Mid-market and sector-specialist orgs that want fast, production-grade Azure AI from a dedicated Microsoft team | Large enterprise programs spanning multiple countries, business units, and platforms | Organisations where AI governance, risk, and regulatory compliance are the primary requirement |
If you're a large multinational enterprise running a global AI transformation programme, Avanade and Accenture will serve you better than we will. If you need your AI governance positioned for a board audit or regulatory submission, KPMG brings genuine depth we don't match. Where Antares excels is in getting Azure AI into production, at the right scope, for organisations that don't want to run a 12-month engagement before something works.
Why Antares
There is no shortage of organisations willing to talk about AI strategy. The difference that matters is whether they've actually built enterprise AI on Azure and kept it running in production — at organisations with real governance requirements, real data complexity, and real users.
Over 20 years of Microsoft partnership and a practice built entirely on the Microsoft stack. Our enterprise AI consulting is anchored in three Microsoft Specialisations that form the core pillars of every engagement: Microsoft Copilot for AI-assisted work across Microsoft 365, Microsoft Fabric for the unified data platform that AI depends on, and Azure AI Foundry for production AI deployment and agent orchestration. These aren't areas we dabble in — they're the architecture we build on every day.
Everything we build runs inside your Azure tenancy. Your prompts, your data, your interaction logs — none of it leaves your environment. This isn't a feature we can toggle on; it's the architecture we use by default, because it's the only approach that satisfies data governance requirements in regulated sectors.
QBot is the clearest evidence of what we're capable of on Azure AI — running in production at NRMA, UNSW, Haileybury, Mission Australia, and others. We've navigated the governance approvals, the integration challenges, and the go-live complexity. The deployments are live, not in a sandbox.
We understand what AI governance looks like in an independent school, what a not-for-profit's data landscape typically resembles, and what APRA-regulated financial services organisations need before they can deploy AI in production. That sector knowledge changes how the architecture gets designed from the start.
A two-week Discovery Workshop to understand your situation. Four to eight weeks to a first production deployment. We've designed the engagement model specifically to reduce the time between first conversation and something your organisation actually uses — because that's where value gets created.
Based in Sydney and Melbourne. Every engagement is led and delivered by an Australian team — no offshore handoff, no timezone friction, no ambiguity about who is responsible for what. The team you meet in the first conversation is the team that does the work.
Sectors we work with
The underlying challenges of data readiness, governance, and production reliability are consistent across sectors. What differs is the specific compliance environment, data landscape, and user requirements each one brings. We've built that sector knowledge into how we design from the start.
Common questions
Start with a fixed-fee Discovery Workshop — two weeks, a named team, and a concrete deployment roadmap at the end. Or book a 30-minute conversation to talk through your situation before committing to anything. There's no obligation, and no pitch — just an honest discussion about what's realistic for your organisation.
Enquiries are reviewed within one business day. Sydney-based team available for in-person sessions across NSW and VIC.
