Solution · MCP integration
Connect Claude and ChatGPT to the systems you run.
Your people already ask AI assistants for help. We connect Claude, ChatGPT, Microsoft Copilot and your own agents to Salesforce, Dynamics 365, Jira, your ERP and in-house systems through the Model Context Protocol, with permissions, approvals and audit logs designed in.
What MCP is
One standard for connecting AI to everything else.
The Model Context Protocol is an open standard that lets AI assistants and agents use tools and data from other systems through one common interface. Anthropic created it, and in December 2025 donated it to the Agentic AI Foundation, a Linux Foundation fund co-founded by Anthropic, Block and OpenAI.
It is now supported by ChatGPT, Claude, Microsoft Copilot, Gemini and Visual Studio Code, and platforms you may already run ship their own MCP servers, including Atlassian's Rovo MCP Server for Jira and Confluence and Akeneo's MCP server for product data. In practice, it means an assistant can look up a customer, check an order or raise a ticket in your systems, instead of only answering from what it was trained on.
Why it needs an integrator
The protocol is simple. Your systems aren't.
Turning on a connector takes minutes. Deciding what an AI may see and change across your business takes design.
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Not every system has a server
Some platforms ship an MCP server. Older systems, custom builds and many ERPs don't, so someone has to build one that exposes the right data and actions safely.
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Permissions must carry through
An assistant should see only what the person using it is allowed to see. That means designing identity and access, not sharing one powerful account.
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Changes need approval
Reading is low risk. Creating records, sending messages or changing orders is not. Writes to systems of record need approval steps until results are proven.
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You need a record
Who asked, what the AI did and with which data: audit logs your compliance team can use, and Privacy Act obligations met, including the automated decision rules from 10 December 2026.
What we deliver
From one connection to a governed AI layer.
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Use cases and inventory
Which questions and tasks matter most, which systems hold the answers, and which already have MCP servers.
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MCP servers built and configured
Vendor servers such as Atlassian's and Akeneo's set up with the right scopes, and custom servers built for systems that have none.
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AI tools connected
Claude, ChatGPT and Microsoft Copilot Studio connected for your people, and your own agents built on the Claude and OpenAI APIs.
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Governance designed in
Least-privilege access, approvals for writes, audit logging, an approved-tools list and privacy review, documented for your risk team.
What it looks like
Questions your people could ask, answered from your systems.
Illustrative examples of what connected AI can do. The right first use case depends on where your team loses the most time.
| Who | Asks Claude, ChatGPT or Copilot | Systems behind the answer |
|---|---|---|
| Account manager | "Summarise this customer's open cases and any engineering work behind them before my meeting." | Salesforce or Dynamics 365, Jira |
| Service desk lead | "Raise a Jira item for this escalation, link the customer, and tell me which SLAs are at risk this week." | Jira Service Management, CRM |
| Finance | "Which orders shipped last month but haven't been invoiced?" | ERP |
| E-commerce team | "Which products in the new range are missing attributes for the website?" | Akeneo PIM, commerce platform |
| Operations | "Find the latest version of our onboarding policy and create the tasks for Monday's new starters." | Confluence, Jira, HR system |
How we start
Start with one connection, read-only first.
Prove value on one workflow before anything can change data in your systems.
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01
Pick one workflow
One question or task that needs data from two or more systems, and the people who'd use it. Scope and price agreed up front.
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02
Connect, read-only
MCP servers and AI tools connected with each user's permissions, logging on, and nothing able to change data yet.
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03
Add actions, then scale
Write actions added with approval steps once results are trusted, then the next workflow and the next system.
Questions
Frequently asked questions.
Do we have to choose between Claude and ChatGPT?
No. MCP is supported by both, and by Microsoft Copilot. A server built once can serve several AI tools, so you can use different assistants for different teams, or change later, without rebuilding the connections. Our Claude and ChatGPT for enterprise page covers how to choose.
Is it safe to let AI change data in our systems?
It can be, with the right design: each user's own permissions, least-privilege scopes, approval steps for writes to systems of record, and full logging. We start read-only and add write actions only once results are trusted.
What if our system doesn't have an MCP server?
We build one. A custom MCP server exposes only the data and actions you choose, through the system's existing APIs, with authentication and logging built in.
Where is our data processed?
That depends on the AI tool and how it's deployed. The MCP layer can run in your own environment, but prompts are processed wherever the AI model runs. We map this for each use case against your privacy and residency obligations. Our OpenAI for enterprise guide covers the options in Australia.
How does this relate to Atlassian Rovo, Agentforce or Copilot agents?
Those agents work best inside their own platform. MCP is how AI reaches across platforms: an assistant can see the Jira work behind a Salesforce case, or the ERP order behind a service request. We connect both, so each platform's AI and your cross-platform assistants work from the same data.
Let's connect your AI.
Tell us the question your team wishes AI could answer from your systems. We'll scope the first connection.