Build your AI workforce. Speak it into existence.

An AI digital employee is a software worker built from a plain-language job description. It handles real interactions — phone calls, chats, text messages — makes decisions within the rules you set, and completes actions in your CRM and business systems.

The difference from a tool is ownership of an outcome. A tool gives your staff a faster way to do the work. A digital employee does the work: it answers, it decides, it acts, and it confirms. You brief it the way you would brief a new hire, and it is on shift in minutes.

A chatbot answers questions in one channel. It retrieves information and replies with text, and when the conversation needs something to actually happen, it hands off to a person.

An AI agent takes action. It works across voice, chat and SMS, retrieves what it needs from your systems, decides what to do next, executes the task — booking the appointment, updating the record, raising the ticket — and confirms the result to the customer. ExpertEase AI began as a chatbot platform and the product has moved well past that.

Yes. ExpertEase AI voice agents handle inbound and outbound calls on Australian numbers, including local, 1300 and 1800. Callers speak naturally — there are no “press 1 for accounts” menus — and the agent understands intent, handles interruptions and completes the task while still on the call.

Typical uses are after-hours reception, appointment booking and confirmation, enquiry qualification, payment reminders and outbound follow-up. When a call needs a person, it transfers with the full context attached so the caller never repeats themselves.

Roles start from a few dollars a day. The comparison most businesses make is against a salary: equivalent human coverage typically starts around $60,000 a year, before superannuation, leave and penalty rates — and that buys eight hours a day, five days a week.

A digital employee works nights, weekends and public holidays at the same rate. Pricing scales with the number of roles, call volume and depth of integration, and you can build and test a role for free before speaking to anyone.

No. You describe the role in plain language, the way you would brief a new hire — speak it, type it, or upload an existing job description. There is no canvas to open, no nodes to connect and no prompt engineering to learn.

The intended user is an operations or practice manager, not an engineer. The platform does the build work: it writes the role specification, configures the worker, authors the workflow, wires the system connections, tests the result and deploys it.

Those are canvases. You, or a developer, build the workflow node by node — every step, every condition, every error path — and then maintain it as the business changes. The tool gives you the building blocks; the construction is your problem.

ExpertEase AI authors the workflow itself from your description: the steps, the branching, the conditions, the retries, the error paths and the API wiring. You review the outcome, not the plumbing. That authoring step is the hard part of building an agent, and it is the part the platform does for you.

A first role can be live in minutes, and connecting a supported CRM takes about a minute. There is no proof-of-concept project, no implementation phase and no procurement theatre before you see it working.

Deeper enterprise integrations, custom API connections and on-premise deployments are introduced progressively — alongside a role that is already doing useful work, not before it.

Cloud deployments run in Microsoft Azure Australia East and Australia Southeast. Customer data is siloed per organisation and is never used to train other customers’ workers.

For organisations with stricter obligations, fully on-premise and air-gapped deployment is available on infrastructure ExpertEase AI owns and operates — with no data leaving your environment at all, and no request reaching the public internet.

Yes — that is a large part of why the architecture exists. Australian data residency, IRAP-aligned architecture, siloed customer data, role-based access controls, full conversation logging and audit trails, and on-premise or air-gapped deployment where data cannot leave the building.

ExpertEase AI does not currently hold ISO 27001 or SOC 2 certification. If that is a procurement requirement, raise it early and we will be straight with you about where it sits and what we can evidence instead.

Live today: Microsoft Dynamics 365, Teams, Outlook, SharePoint, Power Automate, Salesforce, Zoho, Cliniko, Sandwai, Shiftware and Midwinter. Clio, Rex, Nookal, Reapit, Smokeball and LEAP are in build.

Anything with an API can be connected. ExpertEase AI is also listed on the Microsoft Azure Marketplace as a transactable offer, and on Zoho, Shopify and Zapier.

It escalates to a human — by warm transfer, message or task — with the full conversation context attached, so nobody has to ask the customer to start again.

Escalation thresholds are part of every role’s specification. You decide what the worker must never attempt, what it must always hand over, and who it hands over to. Low-confidence responses escalate rather than guess.

Answers are grounded in the knowledge you approve — your documents, policies, price lists and systems — using retrieval-augmented generation, so the worker answers from your sources rather than from the open internet.

Every conversation is logged and reviewable, responses can be traced back to the source material they came from, and anything outside the approved scope escalates to a person instead of being improvised.

Over 100 languages across voice and chat, with automatic detection of the caller’s language mid-conversation.

Japanese is in production, including native voice and a full Japanese-language management dashboard — which is the evidence that adding a language is a build task rather than a research problem.