Remote AI Automation Services for Australia A Dedicated AI Automation Engineer Hourly or Monthly

Two ways. By the hour: AUD $40 per AI automation engineer, time tracked to the minute, billed in AUD, with a weekly report and demo - best for a defined backlog or burst work, no monthly commitment. Or monthly: AUD $3,000 for 160-172 hours of a full-time engineer - the same flat rate as any Empiric engagement, with a 7-day risk-free trial - the better value when the backlog is steady. No Fair Work obligations or payroll tax either way, because the engineer is not your employee. Platform seats (n8n / Make / Zapier) and per-run model and OCR usage are billed to your own accounts at cost, not marked up.

Hourly (AUD $40/hr) if the work is a short, defined backlog - 'automate these five workflows', 'wire this into our CRM', 'add an approval step here' - or you want to start small before committing. Monthly (AUD $3,000/mo for 160-172 hours) if the automation backlog is steady enough to keep an engineer busy: it works out far cheaper per hour, it comes with a 7-day risk-free trial, and you get a full-time engineer rather than booked hours. You can start hourly and switch to monthly (or back) as the backlog grows or settles - no re-contracting.

Because AI automation is high-expertise, high-iteration work that usually comes in short bursts: it leans on people who can design a reliable LLM-in-the-loop pipeline, build the human-in-the-loop gates and the evals, and pick where a rule belongs versus where the model belongs - and a short engagement carries the ramp-up of any short engagement. So an hour of AI automation work is priced above a generic dev hour. The monthly plan, though, is the same flat AUD $3,000 as any Empiric engagement - once you commit to a month, you are not paying the short-engagement premium.

The manual, repetitive work that eats hours: invoice and form processing, email triage and routing, data entry and reconciliation, lead enrichment and routing, support-ticket triage and first-draft replies, onboarding workflows, report generation, system-to-system data sync, content and proposal drafting, contract first-pass review, recruiting screening. We map the workflow, build it with an LLM in the loop for the parts that need judgement, wire it into your systems, and keep a human in control of the decisions that matter.

Whichever fits the job. A no-code platform (n8n, Make, Zapier, Workato, Pipedream, Activepieces) when it is the right tool, and custom code (Node.js or Python) when the no-code path hits a wall on logic, scale, or cost. We pick with you and tell you honestly which one your workflow needs. If you are already committed to n8n specifically, see /services/n8n-workflow-automation - same model, n8n-focused, including self-hosting on AWS Sydney.

Approval gates where a decision matters, an exception queue for the cases the automation should not handle on its own, confidence thresholds that route low-confidence work to a person, an audit trail of every run and every model decision (a compliance review can read it), and an internal tool that gives your team the queue and the controls. The automation handles the volume; a person handles the judgement calls.

Models are not perfect, so we build for it: structured outputs with validation, confidence thresholds, retries and idempotency, an exception queue, a human approval gate on anything consequential, and run logs and alerting so a failure is visible immediately. We also pick where the model is used carefully - rules stay rules, and the model only handles the parts a rule cannot - which is the single biggest reliability lever.

Yes - that is most of the value. Salesforce, HubSpot, Pipedrive, Xero, MYOB, QuickBooks, your ERP, Zendesk, Intercom, Freshdesk, email, Slack or Teams, your databases, file storage, e-sign, and your internal APIs - read and write. For custom internal APIs, we wire them directly or via an MCP server (see /services/mcp-server-development).

You do - your GitHub or GitLab org, your automation platform accounts (n8n, Make, Zapier), your cloud account, your model accounts, from day one. Data runs in AWS Sydney if you want it there. We work inside your accounts, not ours, so there is nothing to claw back if the engagement ends and no usage spend hidden in our invoice.

We will tell you. Some workflows are too low-volume, too variable, or too cheap to do manually to be worth a build - and sometimes a point-solution SaaS already does the job better than a custom automation would. Part of the scoping call and the first stretch of work is sorting the workflows worth automating from the ones that are not; we do not build automations for the sake of billing hours.

AI automation is the manual work behind the scenes - documents, data, routing, reports - running on a schedule or a trigger with a human in the loop. An AI agent (see /services/ai-agent-development) takes multi-step actions against your systems, often interactively. A chatbot (see /services/chatbot-development) answers questions from your knowledge base. They overlap, and one engagement can do more than one; the scoping call sorts out which you actually need.