Your AI strategy starts here.
Most organisations have experimented with AI. Fewer have put it into production in a way that’s secure, adopted by intended users, and genuinely connected to how the organisation operates. QBot is Antares Solutions’ enterprise AI platform, built to close that gap. Deployed inside your own Azure environment. Connected to your real data. Governed from day one.
In production across Australian organisations
Capabilities & Features
QBot is not a general AI chatbot. It’s an enterprise AI platform — a suite of production-ready AI agents covering the use cases most organisations need first, combined with the capability to design and build custom agents and multi-agent workflows from scratch to suit specific organisational needs. Everything runs inside your own Azure environment. Connected to your data. Subject to your governance. Auditable from day one.
QBot is grounded in your data — your policies, your systems, your knowledge base. It answers using information you’ve given it access to, not the internet.
Every user gets the same quality answer or output, every time. No inconsistency based on who’s asking, what device they’re on, or when they submit the query.
You can see exactly what was asked and exactly what the AI provided, across your whole organisation. Not just an individual’s chat history. This is the audit and governance capability that most AI tools don’t offer.
AI that outputs and responds in your organisation’s brand, style, tone and voice — not like a generic AI assistant — from your colours and fonts to the language used.
QBot connects to systems outside the Microsoft ecosystem. No matter your ERP or CRM solution, any sector-specific platforms, or a mix of legacy tools, QBot works in your ecosystem.
Most AI tools are designed for knowledge workers. QBot is built to serve staff at every level – including frontline workers, volunteers, and non-permanent staff – as well as partners and customers. Different audiences, different agents, different access levels. All managed from a single platform.
Our Microsoft credentials
These aren’t self-certified designations. Advanced Specialisations are awarded following an independent audit of technical methodology and real production customer outcomes — the highest tier of Microsoft recognition, across AI, analytics, modern work, and cloud application development.
Every technology leader we speak to is thinking about AI, and most have already started somewhere. The challenge isn’t generating interest. It’s moving from an impressive proof of concept to something that actually works in production, respects your data governance, and gets used by your people. Here’s where things typically go wrong.
Building an AI demo with ChatGPT or Claude is accessible to most organisations. However, getting it into production, like connecting it to live data, within your security perimeter, with proper access controls, is a significantly harder problem. Many organisations are stuck between a working prototype and a platform they can actually deploy that will be accurate and reliable in its performance.
General-purpose AI tools process queries on shared infrastructure. For organisations handling sensitive data — student records, case notes, financial information, internal policy — this creates a genuine problem. Your data should stay in your environment, full stop.
There is no shortage of AI use cases. The problem is knowing which ones to prioritise, in what order, and how to make the case internally. Without a structured approach, organisations spend months debating rather than building — or build the wrong thing first.
Giving everyone a ChatGPT licence improves individual productivity. It does not create organisational intelligence. If your AI doesn’t know your policies, your processes, your data, or who is asking — you’re not realising anywhere near the full opportunity.
AI answers need to be accurate, auditable, and consistent — especially when staff are relying on them for policy, compliance, or customer-facing responses. Without proper grounding in your own data and content, AI systems will hallucinate. That is not a risk most organisations can accept in production.
Technology that people don’t use delivers no value. AI tools need to be accessible where your staff already work — inside Microsoft Teams, your SharePoint intranet, your CRM, or whatever platform they use day to day. Deploying a standalone AI portal and expecting adoption rarely works.
What makes QBot different
Your prompts, outputs, and interaction logs never leave your Azure environment. QBot applies to your data sovereignty policy the same way everything else in your tenancy does. This is a design principle, not an afterthought.
QBot connects to your SharePoint, your CRM, your ERP, your policies, and your proprietary knowledge base. It answers questions using your information — not generic internet content. No hallucinations from data it doesn’t know.
Different AI assistants for different audiences — staff, students, or customers — each with their own knowledge base, tone, and permissions. Deployed where people already work: Teams, your intranet, mobile, or your own applications.
Full audit logging, role-based access via Entra ID, content filtering, and responsible AI controls from day one. You can see who asked what, what answer was given, and which data sources were used. Compliance is not an add-on.
Pre-built connectors for SharePoint, Microsoft Graph, CRM, ERP, and third-party platforms. QBot can also integrate with systems outside Microsoft entirely — giving you AI that works across your whole technology landscape, not just inside M365.
Choose the LLM that fits your requirements — ChatGPT (OpenAI), Claude (Anthropic), or any model available in Azure AI Foundry. You are not locked to one vendor’s model choices. Swap or upgrade models without rebuilding your platform.
Understanding your options
One of the most common questions we hear is: “We’re already looking at Copilot — how does QBot fit in?” These tools are not competitors. They solve different problems at different levels of complexity. Here’s an honest breakdown.
 Microsoft Copilot |
 Copilot Studio |
 QBot (by Antares) |
|
|---|---|---|---|
| Primary purpose | Individual productivity within M365 apps | Low-code agent building inside M365 ecosystem | Enterprise-grade AI platform for complex, multi-persona deployments |
| Best suited for | Helping individuals draft, summarise, and manage tasks in Teams, Word, Outlook | Building simple internal chatbots and workflow agents without code | Complex use cases, regulated environments, multi-audience deployments |
| Data sovereignty | Microsoft cloud — within M365 tenancy | Within M365 tenancy — limited control over data flows | Fully inside your Azure tenancy — complete data sovereignty |
| Integrations | Microsoft 365 apps only | M365 and basic third-party connectors | SharePoint, CRM, ERP, LMS, case management, custom systems, and more |
| Multiple AI personas | Single assistant per user | Limited to simple single-topic agents | Multiple agents for staff, students, customers — each with own knowledge base |
| Audit and compliance | Standard M365 audit logs | Basic logging — limited compliance depth | Full audit logging, content filtering, Entra ID RBAC, responsible AI controls |
| External system access | Microsoft 365 only | Limited — requires significant custom work for complex integrations | Pre-built connectors for CRM, ERP, case management, and custom APIs |
| Model flexibility | OpenAI’s latest models, including GPT-5 | OpenAI and Anthropic’s latest models including GPT-5, Claude 4.0 Sonnet, and Claude 4.1 Opus. | Any model in Azure AI Foundry — OpenAI, Anthropic Claude, Meta Llama, Mistral, DeepSeek, and more. Over 11,000 models in the catalogue. |
| When to use it | Improving individual productivity across Teams, Word, Outlook, Excel | Simple internal bots — FAQ agents, basic HR queries — where M365 data is sufficient | Complex, multi-system, regulated, or customer-facing AI deployments |
QBot and Copilot are complementary. Many of our clients run both — Copilot for individual productivity inside M365, and QBot for the organisation-wide AI use cases that need deeper integration, stronger governance, and multi-persona deployment.
Real-world use cases
These are the use cases we see working in production across Australian organisations right now — not experiments, not demos. Filter by function or sector to find the ones most relevant to you.
Answers staff questions about leave entitlements, onboarding steps, benefits, and policies — instantly. Reduces HR helpdesk load by handling the high-volume, repetitive queries your team fields every week.
Screens applications against job descriptions, scores candidates, and explains match rationale. Cuts initial screening time dramatically and helps hiring managers shortlist faster with consistent criteria.
Guides new starters through induction, answers questions about systems, tools, and procedures, and surfaces relevant policies and contacts — reducing the burden on managers and giving new staff a consistent first experience.
Helps managers with “how do I…” questions about performance reviews, pay changes, probation, and grievances — with links to the right forms and policies. Reduces manager dependency on HR for process guidance.
Supports frontline and field staff with quick answers about rosters, overtime, allowances, training requirements, and safety — specific to their role. Designed for staff who don’t sit at a desk or have easy access to HR systems.
Handles tier-1 IT queries — password resets, software access requests, device troubleshooting guidance — without a ticket. Routes complex issues to the right team with full context already captured.
Answers staff questions about acceptable use, data handling, and security procedures — drawing from your current policies, not general internet content. Consistent, accurate, and up to date with your actual documentation.
Answers “how do I do X in Teams, SharePoint, Salesforce, or Dynamics?” using your organisation-specific guides and documentation. Reduces ad-hoc questions to IT and training teams and helps staff self-serve faster.
Surfaces answers from document libraries, policy repositories, and internal knowledge bases in plain language. Staff stop searching through folders and SharePoint — they just ask. The agent cites the source so users can verify.
Reads, summarises, and extracts key information from lengthy reports, contracts, submissions, and PDFs. Outputs structured summaries, action items, or data extracts — saving hours of manual reading per document.
Triggers workflows, creates records, and updates CRM or ERP entries from conversational input. Cuts manual data entry and the friction between systems — AI that doesn’t just answer questions but takes action.
Queries your BI layer and delivers plain-language summaries, trend highlights, and exception reports on demand. Leaders get the answer without waiting for a prepared slide deck or a data analyst to run a query.
Guides staff through the correct safety procedures and incident response steps — surfacing relevant checklists, escalation contacts, and reporting requirements at the moment they’re needed.
Supports frontline and branch staff with quick, consistent answers to customer questions — using internal product, policy, and knowledge content. Reduces escalations and improves response accuracy across the team.
Assists contact centre agents in real time during calls and chats — suggesting responses, summarising previous interactions, and surfacing relevant internal knowledge. Reduces handling time and improves first-contact resolution.
Helps staff guide customers through complex forms or applications — clarifying requirements, pre-empting common errors, and explaining eligibility and evidence requirements at each step.
Helps staff understand and apply finance policies for expenses, travel, procurement, and delegations — including which cost codes or approvals are required. Reduces incorrect submissions and load on the finance team.
Assists budget owners by explaining templates, timelines, and rules — and answering questions about historical spend, cost centres, and forecast assumptions. Speeds up the budget cycle and reduces central finance team queries.
Reads contracts, extracts key clauses, flags risk items, and summarises obligations in plain language. Speeds up initial document review for legal, procurement, and finance teams handling high document volume.
Gives legal teams a single interface to past advices, precedents, and matter notes — making it easier to reuse existing work, maintain consistency, and answer questions without starting from scratch each time.
Maps regulatory obligations to your internal policies and controls — and answers staff questions like “what applies to me in this scenario?” Reduces inconsistent interpretation and load on compliance and legal teams.
Summarises regulatory or standards changes — explaining the impacts in plain language and pointing to the affected internal policies and processes. Keeps your teams current without reading every legislative update themselves.
Answers student queries about enrolment, assessment requirements, and campus services — 24/7, consistently, without overburdening admin staff. In production across universities and independent schools.
Helps educators generate lesson plans, quizzes, and surveys aligned to curriculum requirements. Analyses student interaction patterns to surface insights that support more targeted teaching decisions.
Transcribes clinical conversations using Azure Speech with medical terminology accuracy, then structures output into clinical case reports covering patient history, symptoms, and treatment. Reduces manual documentation burden for clinical staff.
Handles common queries from patients, residents, or their families about services, schedules, and care plans — consistently and at scale. Frees care staff to focus on direct care rather than answering repetitive information requests.
Provides clinicians and care staff with fast access to local guidelines, protocols, and clinical pathways — with answers grounded in approved content and links back to the source document.
Guides clients or participants through intake questions, captures relevant information, and routes them to the right service or case worker. Reduces wait time and administrative load at the front end of service delivery.
Walks volunteers through compliance requirements, induction steps, and role-specific information. Answers questions consistently without placing ongoing demand on coordination staff. In production at leading Australian not-for-profit organisations.
Helps staff find grant opportunities, understand criteria, and prepare applications — and supports reporting by summarising outcomes and evidence. Reduces the time burden of grants administration on small teams.
Start with a 20-minute live demo to see QBot in action — or book a Discovery Workshop and leave with a concrete AI deployment roadmap for your organisation. Both are low-commitment ways to understand what’s possible for your specific situation.
No obligation. No sales pitch. An honest conversation about what AI can do in your organisation.How we get you to production
The most common reason AI initiatives stall is not the technology — it’s the absence of a clear, sequenced approach. We’ve structured our AI consulting services to take the uncertainty out of the first decision and move you from strategy to production in a way that’s measurable and low-risk.
We map your AI use cases, assess your data readiness, and produce an Agentic AI deployment roadmap. Concrete, prioritised, and scoped — so you know exactly what you’re committing to before you commit.
We deploy the core QBot platform inside your Azure tenancy, connect it to your priority data sources, and configure your first AI agent. You have something working in production — not a demo, the real thing.
Additional AI personas, deeper system integrations, agentic workflows that take action rather than just answer questions. We work in prioritised sprints aligned to your roadmap — expanding capability as your confidence grows.
Ongoing monitoring, optimisation, model updates, and new capability development — delivered in fortnightly sprints by a dedicated team that knows your platform. Your AI needs will keep evolving; the managed service is built around that.
Sectors we work with
Enterprise AI is not sector-specific — the underlying challenges of data governance, adoption, and production-grade reliability are consistent. We have deployed QBot in the following sectors, and we understand the specific compliance, data, and user requirements each one brings.
Why Antares
There is no shortage of vendors willing to talk about AI strategy. The distinction that matters in practice is whether the platform has actually been built, deployed, and is running in production at organisations like yours.
With over 20 years of Microsoft partnership, three Solution Partner Designations, and four Advanced Specialisations – the highest tier of Microsoft recognition – Antares goes deep on the Microsoft stack. QBot is built on Azure AI Foundry – the same technology investment Microsoft is making. You benefit from that alignment.
QBot is live at a number of Australia’s most recognisable organisations. These are not just proof of concepts, pilot projects, or case studies in progress. They are production deployments handling real queries from real users every day.
Most AI tools are built for simplicity. Enterprise organisations are not simple. They have multiple data sources, complex permission structures, varying audiences, and real compliance obligations. We build AI that works within that reality, not despite it.
We start every engagement with a fixed-fee Discovery Workshop. You get a clear use case map, a data readiness assessment, and a sequenced deployment roadmap — before you commit to a full build. That’s how we take the risk out of the first decision.
AI platforms need ongoing attention — model updates, new integrations, evolving use cases, and performance tuning. Our managed AI service means we are still actively improving your platform well beyond the initial deployment. We don’t walk away at the finish line.
If your situation calls for Copilot Studio rather than QBot, or if your data isn’t ready for a production AI deployment yet, we’ll tell you — and we’ll help you figure out what needs to happen first. That’s how the Discovery Workshop is structured: scoped before quoted, honest about what’s possible.
Frequently Asked Questions
An enterprise AI platform is a governed, secure environment for deploying AI assistants and agents across an organisation — connected to your own data and systems, not general internet content. General-purpose AI tools like ChatGPT are built for breadth and individual use. They have no knowledge of your organisation, your data, or your policies, and they do not enforce your access controls. An enterprise AI platform like QBot is grounded in your SharePoint, your CRM, your ERP, and your knowledge base. It respects your Entra ID permissions, keeps all data within your Azure tenancy, and provides full audit logging for compliance purposes.
Copilot and QBot solve different problems, and many of our clients use both. Microsoft Copilot is excellent at improving individual productivity within Microsoft 365 — helping staff draft content, summarise meetings, and manage tasks in Teams, Word, and Outlook. QBot is designed for the organisation-wide use cases that Copilot cannot address: connecting to systems outside M365, deploying multiple AI personas for different audiences (staff, students, customers), enforcing role-based access to different knowledge bases, and providing the compliance and audit depth that regulated environments require. If your requirements go beyond individual productivity, QBot fills that gap.
Copilot Studio is a great low-code tool for building straightforward internal agents within the Microsoft 365 ecosystem — it works well for simple FAQ bots, basic HR queries, and single-topic agents where M365 data is sufficient. QBot is the right choice when the use case is more complex: multiple AI personas serving different audiences, deep integration with systems outside M365, regulated data environments, or customer-facing deployments where consistency and audit logging are critical. QBot and Copilot Studio are not competitors. We’d be happy to help you understand where each one fits in your specific situation.
No. QBot is deployed entirely inside your Microsoft Azure tenancy. Your prompts, your outputs, your interaction logs, and your source documents never touch a shared platform or leave your environment. Your data sovereignty policy applies to QBot in exactly the same way it applies to everything else running in your Azure environment. This is not a feature we added after the fact — it is a fundamental design principle that applies to every deployment we build.
A first production AI agent can typically be deployed in a matter of weeks. The limiting factor is rarely the technology, it’s having clarity on use cases and having your data sources in a state where they can be connected. We can typically deploy the use cases from initial idea to UAT and then production in 3 to 6 weeks.
Antares provides AI consulting services across the full deployment lifecycle for enterprise organisations: AI strategy and use case discovery workshops, data readiness assessments, enterprise AI platform deployment (QBot), AI persona design and knowledge base configuration, systems integration across Microsoft and third-party platforms, AI governance and compliance framework design, and ongoing managed AI services. We are a Microsoft Specialist Systems Integrator with over 20 years of experience, holding three Microsoft Solution Partner Designations and four Advanced Specialisations — the highest tier of Microsoft recognition.
QBot is in production across education, not-for-profit and community services, insurance, and a number of other organisations across financial services, legal, public sector, and healthcare. The underlying platform and architecture are consistent across sectors, what varies is the data sources, the AI personas, and the compliance requirements. We have experience navigating all of these.
