How we work
How much does AI automation cost? How we scope and run every engagement
How much AI automation costs depends on what's being automated: the number and complexity of processes, the systems to connect, the state of your data, and how much human review is needed. We don't quote generic figures. We scope each engagement in four steps, and the roadmap sets out effort and costs before you commit.
Vermilion Intelligence is an AI and automation consultancy helping mid-to-large organisations in Australia and New Zealand put AI agents to work on repetitive tasks.
Our four steps
Assessment, roadmap, implementation, ongoing support
Step 1: AI Readiness Assessment
We review your processes, systems and data to find where AI and automation will make the biggest difference, and where they won't.
Step 2: Roadmap
You get a clear, prioritised plan: what to automate first, with the effort, costs and expected outcomes for each piece.
Step 3: Implementation
We build the AI agents and automations and integrate them into the tools you already use, with people approving what matters.
Step 4: Ongoing support
We monitor what's live, improve it as your business changes, and expand automation to the next priorities on your roadmap.
Cost
What determines the cost of AI automation?
These are the factors we look at when scoping. Each one is assessed in the readiness assessment and costed in the roadmap.
Number of processes
One well-defined task is a smaller piece of work than several connected processes across teams.
Process complexity
Predictable, rules-based steps are simpler than work involving varied inputs, exceptions and judgement.
Systems and integrations
Modern systems with good APIs are quicker to connect than older systems that need workarounds.
Data readiness
Clean, accessible data keeps effort down; scattered or inconsistent data needs preparation first.
Risk and review
Customer-facing or regulated work needs more testing, approval steps and governance.
Running costs
AI model usage, hosting and software licences are ongoing costs, set out separately in the roadmap.
Timeline
What determines how long it takes?
- How many processes and teams are in scope
- How quickly system access and approvals can be arranged
- How much data preparation is needed
- How long agents run under close review before their scope is widened
- Your team's availability for interviews, testing and feedback
Before you commit
What you'll know before anything is built
- What will be automated, and what won't
- The effort and costs of each piece of work
- The expected outcome and how it will be measured
- What we need from your team, and when
- Who owns each agent once it's live
FAQ
Questions about cost and timelines
How much does AI automation cost?
There's no honest single figure. Cost depends on the number and complexity of processes, the systems to integrate, data readiness, the level of human review and testing, and ongoing running costs such as AI model usage. We scope each engagement, and the roadmap shows the effort and costs of every item before you commit to building.
How long does it take to implement AI automation?
It depends on scope, system access, data preparation and how long agents run under close review. A single, well-defined task with easy system access is quicker than several connected processes across teams. We set out a realistic timeline for each piece of work in the roadmap, before anything is built.
How do we measure whether AI automation is worth it?
Agree the measures before building, such as hours of manual work removed, turnaround time, error and rework rates, or backlog size, and record a baseline. Then track them once the agent is live, alongside running costs. If an opportunity doesn't stack up on paper, we'll say so in the roadmap.
What are the ongoing costs after an agent goes live?
Ongoing costs typically include AI model usage, which varies with volume, any software or hosting the agent relies on, and support time for monitoring, updates and improvements. The roadmap sets these out separately from build effort, so you can see the full cost of running each agent.
The services behind each step
AI readiness assessment
A structured review of your processes, systems and data that shows where AI is likely to pay off, and what to automate first.
AI strategy and roadmap
A clear, prioritised plan with the effort, costs and expected outcomes for each piece of AI and automation work.
AI agent development
Custom AI agents that read, decide and act across your tools to complete repetitive tasks, with people approving what matters.
Ongoing support
Monitoring, improvement and expansion of the AI agents and automations already working in your business.
Tell us about the repetitive work you'd like to hand over
Send us a short description of the tasks, the team and the systems involved. We'll reply with an honest view of whether AI agents are a good fit, and what an assessment would look at.
Or email raj@vermiliong.com.au