Category: Tutorials

AroFlo is one of Australia’s most comprehensive job management platforms for trade businesses. Used by electricians, plumbers, HVAC technicians, builders, and facility management companies across the country, it handles everything from quoting and scheduling to timesheets and invoicing. But even with AroFlo’s extensive feature set, there is still a significant amount of manual admin work happening between the gaps.

Every time a job comes in, someone needs to assign it. Every time a job finishes, someone needs to generate the invoice. Every time a quote gets accepted, someone needs to order materials. Every time a photo is taken on site, someone needs to attach it to the compliance report.

AI automation eliminates those “someone needs to” moments. By connecting AroFlo to intelligent workflows through Make.com, you create a system where the admin handles itself, accurately, instantly, and without anyone lifting a finger.

Setting Up AroFlo Webhooks and API Access

AroFlo provides a comprehensive REST API that allows external systems to interact with your job data. Here is how to set up the connection:

Step 1: Enable API Access

Log in to AroFlo as an administrator and navigate to your account settings. Under the integrations or API section, generate your API credentials. You will receive an API key and secret that authenticate your external connections. Store these securely; they provide full access to your AroFlo data.

Step 2: Configure Webhooks

AroFlo supports webhooks for key events including job creation, status changes, quote acceptance, task completion, and timesheet submissions. To set up a webhook:

1. Navigate to the webhooks configuration section in AroFlo
2. Create a new webhook and specify the event type you want to monitor
3. Enter the destination URL from your Make.com or n8n webhook module
4. Set any filters (e.g., only trigger for specific job types or locations)
5. Test the webhook by creating a test event in AroFlo

Step 3: Connect to Make.com

In Make.com, create a new scenario and add a “Custom Webhook” module as the trigger. Copy the webhook URL and paste it into AroFlo’s webhook configuration. When AroFlo sends a webhook notification, Make.com receives the event data and triggers your automation workflow.

Automation Recipe 1: Job Notification to Team Assignment

When a new job is created or a job is scheduled, this automation ensures the right person is assigned immediately and everyone is informed.

How It Works

AroFlo sends a webhook when a new job is created. The automation then:

1. AI analyses the job details: Job type, location, required skills, urgency level, and any special requirements noted in the description
2. Checks team availability: Queries AroFlo’s schedule to identify available team members with the right skills
3. Considers logistics: AI evaluates the location of each available technician’s current job to minimise travel time
4. Assigns the job: Updates AroFlo with the recommended assignment
5. Notifies the team: Sends a push notification and SMS to the assigned technician with job details, client contact information, and navigation directions
6. Alerts the office: Posts a summary to Slack or Teams so the office team has visibility

For electrical businesses with multiple teams in the field, this automation alone can save 1-2 hours per day in dispatch coordination.

Automation Recipe 2: Job Completion to Invoice Generation

The moment a technician marks a job as complete in AroFlo, the invoicing process should begin automatically. Here is the full workflow:

1. Trigger: Job status changes to “Complete” in AroFlo
2. Data collection: The automation pulls all job details including labour hours, materials used, travel time, and any variations from the original quote
3. AI review: AI checks the job data for completeness. Are all timesheet entries present? Are materials logged? Are photos attached? If anything is missing, it flags the gap and notifies the technician
4. Invoice generation: Using the verified job data, the automation creates an invoice in AroFlo (or directly in Xero/MYOB if you prefer)
5. Delivery: The invoice is emailed to the client with a payment link
6. Follow-up scheduling: A follow-up task is created for 7 days later to check if payment has been received

This workflow typically reduces invoice turnaround from 2-5 business days to same-day, significantly improving cash flow for trade businesses.

Automation Recipe 3: Quote Accepted to Materials Ordering

When a client accepts a quote in AroFlo, the automation handles the downstream logistics:

1. Trigger: Quote status changes to “Accepted” in AroFlo
2. AI extracts materials: AI reads the quote line items and identifies all materials required for the job
3. Checks inventory: Queries your inventory system (or AroFlo’s inventory module) to determine what is in stock and what needs ordering
4. Generates purchase order: For items that need ordering, the automation creates a purchase order with your preferred suppliers
5. Schedules the job: Based on material lead times and team availability, AI suggests optimal job scheduling dates
6. Notifies the client: Sends a confirmation message with the proposed start date and any preparation requirements

For plumbing businesses that frequently order specialised parts, this automation prevents the costly delays that happen when materials are not ordered until the morning of the job.

Automation Recipe 4: Timesheet to Payroll Processing

Manual timesheet processing is one of the most tedious admin tasks in the trades. This automation streamlines it:

1. Daily trigger: At end of each business day, the automation collects all timesheet entries from AroFlo
2. AI validation: AI checks timesheets for anomalies: unusually long shifts, missing break entries, overlapping jobs, travel time that does not match the distance between jobs
3. Flags and approvals: Normal timesheets are approved automatically. Flagged entries are sent to a manager for review with the specific issue highlighted
4. Payroll export: Approved timesheets are formatted and exported to your payroll system (Xero Payroll, KeyPay, or others)
5. Cost allocation: Labour costs are automatically allocated to the correct jobs in AroFlo, keeping your job costing accurate

Automation Recipe 5: Job Photos to Compliance Documentation

For trades that require compliance documentation, especially electrical and plumbing, this automation converts job photos and notes into completed compliance documents:

1. Trigger: Job form or photos uploaded in AroFlo
2. AI analysis: AI processes the photos to identify what they show (before/after conditions, equipment installed, test results, safety features)
3. Document population: The compliance document template is automatically populated with job details, technician information, site address, and descriptions extracted from the photos
4. Quality check: AI reviews the completed document against regulatory requirements to ensure all required sections are filled
5. Distribution: The completed document is stored in AroFlo, sent to the client, and filed in your compliance archive

This recipe is particularly valuable for electrical businesses that need to produce Certificates of Compliance (CoC) for every job. Instead of spending 15-20 minutes manually completing each certificate, the process takes seconds.

Advanced: Connecting AroFlo to Your Full Business Stack

The real power of AroFlo automation comes when you connect it to your entire business ecosystem:

• Accounting: AroFlo to Xero or MYOB for automatic invoice syncing, payment reconciliation, and financial reporting
• Communication: AroFlo to Twilio or MessageMedia for automated SMS notifications to clients and team members
• Customer management: AroFlo to HubSpot or Pipedrive for lead tracking and client relationship management
• Document storage: AroFlo to Google Drive or SharePoint for automatic filing of job documents, photos, and compliance certificates
• Reviews: AroFlo to Google Business Profile for automated review requests after completed jobs

If you are also using ServiceM8 for some operations, you can create cross-platform automations. See our ServiceM8 AI integration guide for details on connecting both platforms to a unified automation layer.

Implementation Timeline

Here is a realistic timeline for implementing AroFlo AI automation:

• Week 1: API setup, webhook configuration, and first automation (job notification to team assignment)
• Week 2: Invoice automation and accounting integration
• Week 3: Materials ordering and timesheet processing
• Week 4: Compliance documentation and testing of all workflows
• Week 5: Monitoring, refinement, and team training

Frequently Asked Questions

Does AroFlo support webhooks natively?

AroFlo provides a comprehensive API that supports both polling and webhook-style integrations. The exact webhook configuration depends on your AroFlo plan and version. Contact AroFlo support or your account manager to confirm webhook availability for your specific setup.

Can I use this with AroFlo’s Field version?

Yes. The automation recipes work with data from both AroFlo Office and AroFlo Field. Technicians continue using AroFlo Field on their devices exactly as they do now; the automations run in the background triggered by their actions.

What happens if an automation fails?

Make.com logs all workflow executions and sends notifications when a step fails. The system can be configured to retry failed steps, skip non-critical steps, or alert a team member for manual intervention. No data is lost when a workflow fails.

How much does this cost to run monthly?

Make.com plans suitable for AroFlo automation start from $12/month. AI API costs depend on volume but are typically $10-30/month for a medium-sized trade business. Total cost is usually under $100/month for automations that save 40-60 hours of admin time.

Can I customise these recipes for my specific business?

Absolutely. The recipes described here are starting templates. Every trade business has unique processes, and the automations should be tailored to match. The AI modules, in particular, can be configured with your specific business rules, pricing structures, and compliance requirements.

If you are an Australian tradie running your business on ServiceM8, you already know the platform handles jobs, quotes, and scheduling brilliantly. But here is the thing: ServiceM8 on its own still leaves you doing a mountain of repetitive admin. Copying job details into your CRM. Manually sending invoices after a job wraps up. Chasing clients for reviews. Responding to missed calls hours later.

What if all of that happened automatically, triggered the instant something changes in ServiceM8? That is exactly what happens when you connect ServiceM8 to AI-powered workflows using tools like Make.com or n8n. In this step-by-step guide, we will walk through the entire process, from setting up webhooks to building automation recipes that actually work for trade businesses.

Why Connect ServiceM8 to AI Workflows?

ServiceM8 is one of the most popular field service management platforms in Australia, used by electricians, plumbers, HVAC technicians, and general builders. It handles job cards, scheduling, quoting, and invoicing. But it was not built to think for you. It will not automatically decide what to do when a job status changes, a call goes unanswered, or a client has not left a review after two weeks.

AI workflows fill that gap. By connecting ServiceM8 to an automation platform, you create a system where every event in ServiceM8 triggers an intelligent response. A completed job does not just sit there waiting for you to invoice it. A missed call does not disappear into voicemail purgatory. A new lead does not get forgotten because you were up a ladder.

The businesses we work with at Loudachris AI Automation typically see these results after connecting ServiceM8 to AI workflows:

• 8-12 hours saved per week on admin tasks
• Invoice turnaround drops from 3 days to 30 minutes
• Review request response rates increase by 40-60%
• Missed call follow-up drops from hours to seconds
• Zero manual data entry between ServiceM8 and your CRM

Step 1: Understanding ServiceM8 Webhooks

Webhooks are the foundation of every ServiceM8 integration. A webhook is a notification that ServiceM8 sends to an external URL whenever something specific happens, like a job being created, a status changing, or a form being completed.

Think of it like this: instead of you checking ServiceM8 every five minutes to see what changed, ServiceM8 taps you on the shoulder the instant something happens and hands you all the relevant data.

Setting Up Your First Webhook

To enable webhooks in ServiceM8, you need to register your application through the ServiceM8 Developer Portal. Here is the process:

1. Go to the ServiceM8 Developer Portal and register a new application.
2. Set your callback URL to the webhook endpoint provided by Make.com or n8n. In Make.com, you create a new scenario and add a “Custom Webhook” module to get this URL.
3. Select your webhook events. ServiceM8 supports webhooks for job creation, status changes, job completion, form submissions, and more.
4. Authenticate your connection using OAuth 2.0. ServiceM8 uses a standard OAuth flow that both Make.com and n8n handle natively.
5. Test the webhook by creating a test job in ServiceM8 and confirming the data arrives in your automation platform.

The webhook payload from ServiceM8 includes the job UUID, which you then use to pull full job details via the ServiceM8 API. This two-step process (webhook notification, then API call for details) is standard practice and both Make.com and n8n handle it smoothly.

Step 2: Choose Your Automation Platform

You have two main options for building AI workflows with ServiceM8:

Make.com (Recommended for Most Tradies)

Make.com has a visual drag-and-drop builder that makes it easy to create complex workflows without coding. It has native ServiceM8 modules, built-in AI capabilities through OpenAI and Claude integrations, and handles error scenarios gracefully. For most trade businesses, Make.com is the right choice.

n8n (For Technical Users)

n8n is an open-source alternative that you can self-host. It offers more flexibility for complex logic, custom code nodes, and advanced data transformations. If you have a developer on your team or want full control over your data, n8n is excellent. However, it requires more technical knowledge to set up and maintain.

Step 3: Build Your Automation Recipes

Here are the five most valuable automation recipes we build for ServiceM8 users. Each one eliminates a specific piece of repetitive admin work.

Recipe 1: New Job Created → CRM Update + Team Notification

When a new job is created in ServiceM8, this workflow automatically:

• Creates or updates the client record in your CRM (HubSpot, Pipedrive, or even a Google Sheet)
• Sends a Slack or Teams notification to the relevant team member with job details
• Creates a calendar event for the scheduled date
• Sends a confirmation SMS to the client with the appointment details

The AI component here can enrich the client data by looking up the address to determine the service area, checking if they are a returning customer, and flagging any notes from previous jobs.

Recipe 2: Job Completed → Invoice + Review Request

This is the highest-value automation for most tradies. The moment a job is marked as complete in ServiceM8:

• AI reviews the job details and generates an itemised invoice
• The invoice is sent through ServiceM8 or your accounting software (Xero, MYOB, QuickBooks)
• 48 hours later, an automated review request is sent via SMS with a direct link to your Google Business Profile
• If no review is received after 5 days, a gentle reminder is sent

The review request timing and wording are optimised using AI. We have found that personalised messages mentioning the specific service performed get 3x more reviews than generic requests.

Recipe 3: Missed Call → Instant SMS Response

Missed calls are one of the biggest revenue leaks for trade businesses. This workflow catches every missed call and responds within seconds:

• ServiceM8 logs the missed call
• The webhook fires and triggers an AI-powered SMS response
• The SMS acknowledges the call, provides basic availability information, and offers a booking link
• If the caller responds, the AI continues the conversation to capture their job details
• The lead is automatically created in ServiceM8 with all captured information

Recipe 4: Quote Accepted → Job Scheduling + Materials Ordering

When a client accepts a quote in ServiceM8, the workflow:

• Converts the quote to a job automatically
• Checks team availability and suggests the next available slot
• Sends a scheduling confirmation to the client
• Generates a materials list from the quote line items
• Sends the materials list to your preferred supplier or creates a purchase order

Recipe 5: Job Form Submitted → Compliance Documentation

For trades that require compliance documentation (electrical certificates, plumbing compliance, safety reports), this recipe:

• Captures the completed job form from ServiceM8
• AI formats the data into the required compliance document template
• Attaches photos from the job to the appropriate sections
• Stores the completed document in your cloud storage (Google Drive, Dropbox, SharePoint)
• Sends a copy to the client and files one for your records

Step 4: Add AI Intelligence to Your Workflows

The recipes above become significantly more powerful when you add AI processing. Here is how AI enhances each step:

Smart categorisation: AI reads the job description and automatically assigns the right job type, priority level, and team member based on the skills required.

Intelligent scheduling: AI considers travel time between jobs, team member availability, skill requirements, and client preferences to suggest optimal scheduling.

Communication drafting: Instead of sending template messages, AI generates personalised communications that reference the specific job, client history, and relevant details.

Data extraction: AI can read handwritten notes from job photos, extract measurements from images, and convert voice memos into structured data entries.

Step 5: Testing and Monitoring

Before going live with any automation, follow this testing process:

1. Create test jobs in ServiceM8 that cover each scenario your automations handle
2. Run each workflow manually in your automation platform to verify every step works
3. Check the output of AI-generated content (invoices, messages, documents) for accuracy
4. Set up error notifications so you are alerted if any workflow fails
5. Run in shadow mode for one week, where automations run but all outputs go to you for review before being sent to clients

Common Integration Challenges (and Solutions)

Challenge: ServiceM8 webhook payloads only include the job UUID, not full job details.
Solution: Add an API call module immediately after the webhook trigger to fetch complete job data using the UUID.

Challenge: Duplicate webhook fires causing duplicate actions.
Solution: Implement idempotency checks using the job UUID and timestamp. Store processed job IDs in a data store and skip any duplicates.

Challenge: Rate limits on the ServiceM8 API.
Solution: Add delays between API calls and use batch processing for bulk operations. Make.com handles this automatically with its built-in rate limiting.

If you want to explore what AI automation can do for your trade business beyond ServiceM8, check out our complete AI for tradies guide covering everything from lead generation to job costing.

Frequently Asked Questions

How much does it cost to connect ServiceM8 to AI workflows?

Make.com plans start from around $12/month for basic automations. The AI processing (OpenAI API) typically costs $5-15/month depending on volume. Most tradies spend under $50/month total for a full automation setup that saves them 8+ hours per week.

Do I need coding skills to set this up?

Not with Make.com. The visual builder handles everything. However, the initial setup and configuration of webhooks does require some technical understanding. Many of our clients at Loudachris AI Automation engage us for the initial setup and then manage ongoing adjustments themselves.

Will this work with my existing ServiceM8 setup?

Yes. Webhooks and API integrations work alongside your existing ServiceM8 configuration without changing anything. Your current workflows, templates, and settings remain untouched.

How long does the setup take?

A basic integration (one or two recipes) can be set up in a single day. A comprehensive setup covering all five recipes typically takes 3-5 business days including testing and refinement.

What happens if an automation fails?

Both Make.com and n8n have built-in error handling. Failed workflows are logged, and you receive a notification. The system can be configured to retry failed steps automatically or fall back to a manual process.

Why Cliniko Practices Need AI Automation

Cliniko is the practice management software of choice for thousands of Australian allied health practices — physiotherapists, chiropractors, osteopaths, psychologists, podiatrists, and more. It’s excellent at what it does: managing appointments, patient records, invoicing, and telehealth.

But Cliniko isn’t an automation platform. It handles the core clinical workflow, but the surrounding communication — intake processing, reminder sequences, recall campaigns, follow-up care plans — still requires manual effort. And in busy practices, that manual effort either consumes reception staff hours or simply doesn’t happen.

By connecting Cliniko to AI automation through Make.com, you can automate the communication workflows that wrap around your clinical care, as we explore in our AI for healthcare industry guide. This means better patient experience, fewer no-shows, higher recall rates, and more time for your team to focus on what matters — patient care.

Automating Patient Intake

Patient intake is one of the most time-consuming admin tasks in any practice. New patients need to fill out health history forms, consent documents, Medicare/private health details, and emergency contacts. Many practices still handle this with clipboards in the waiting room or PDFs emailed back and forth.

Digital Intake Forms to Cliniko

AI automation creates a seamless digital intake flow. When a new patient books (or is booked), the system automatically sends them a digital intake form via email or SMS. The form is mobile-friendly and can be completed before they arrive. When submitted, Make.com processes the form data and creates or updates the patient record in Cliniko — filling in demographics, health history, consent records, and any relevant clinical notes.

For practices that need it, AI can analyse intake form responses and flag potential concerns. If a patient reports specific symptoms, allergies, or contraindications, the system can alert the treating practitioner before the appointment. This means the practitioner walks into the consult already aware of key information, rather than discovering it during the session.

Insurance and Medicare Verification

The system can pre-process insurance and Medicare information submitted in the intake form, verifying details and setting up billing correctly in Cliniko before the patient’s first appointment. This reduces billing errors and the awkward conversation at reception when details don’t match.

Appointment Reminders That Reduce No-Shows

No-shows cost Australian healthcare practices an estimated $2 billion annually. For individual practices, even a few no-shows per week significantly impact revenue and staff utilisation. AI-powered appointment reminders dramatically reduce no-show rates.

Multi-Channel Reminder Sequences

A typical automated reminder sequence works like this: 72 hours before the appointment, the patient receives an email confirmation with appointment details, practitioner name, what to bring, and parking/access information. 24 hours before, they get an SMS reminder with a confirm/cancel option. 2 hours before, a final SMS reminder.

The key difference with AI-enhanced reminders is personalisation. Instead of “Reminder: you have an appointment tomorrow at 10am,” the system generates messages like: “Hi Sarah, just a reminder about your follow-up physio session with Dr James tomorrow at 10am. Remember to wear comfortable clothing and bring the exercise diary we discussed last visit.” This personalised approach reinforces the value of the appointment and reduces the likelihood of cancellation.

Smart Rescheduling

When a patient cancels, the system automatically offers alternative times and can manage a waitlist for popular practitioners. If the cancelled slot opens up, the next patient on the waitlist is notified and can claim the spot — all without reception staff involvement. This keeps your schedule full and reduces the revenue impact of cancellations.

Recall Sequences for Overdue Patients

Patient recall is critical for ongoing care outcomes and practice revenue, but it’s one of the most neglected admin tasks. When reception is busy with today’s patients, calling people who haven’t been in for months drops to the bottom of the priority list.

Automated Recall Workflows

AI automation monitors patient visit history in Cliniko and triggers recall sequences when patients become overdue. The sequences are customised by treatment type and practitioner recommendations. A physiotherapy patient on a maintenance plan who hasn’t visited in 6 weeks gets a different recall message than a dental hygiene patient overdue for their 6-monthly check-up.

The sequence typically includes: an initial friendly reminder, a follow-up highlighting the importance of ongoing care, and a final message offering flexible booking options. AI personalises each message based on the patient’s treatment history, using appropriate clinical language without being alarmist.

Practitioner-Specific Recalls

The system can trigger recalls based on practitioner-set intervals in the patient’s treatment plan. If a chiropractor notes “review in 4 weeks” in the patient’s Cliniko record, the system automatically sends a booking reminder at the 3.5-week mark. This ensures treatment plan compliance without relying on patients to remember their next visit. Our healthcare AI guide covers this in greater detail.

Treatment Plan Follow-ups

Between appointments, patients often need guidance on exercises, medication, self-care, or lifestyle modifications. AI automation delivers this care guidance automatically.

Post-Appointment Care Instructions

After each appointment, the system can automatically send the patient relevant care instructions based on the treatment provided. A physio patient who received dry needling gets aftercare advice specific to that treatment. A podiatry patient who was fitted with orthotics gets a wearing-in schedule. These messages reinforce the practitioner’s verbal instructions and improve treatment outcomes.

Exercise and Homework Reminders

For practitioners who prescribe home exercises, automated reminders can prompt patients to do their exercises between sessions. These can be timed based on the treatment plan — daily reminders for the first week, then every other day, then weekly check-ins. Some practices include links to exercise demonstration videos, creating a comprehensive at-home care experience.

Practitioner Notifications and Workflow

AI automation isn’t just patient-facing. It also streamlines internal practitioner workflows.

Pre-Appointment Briefings

Before each clinic session, the treating practitioner can receive an automated summary of upcoming patients: last visit summary, treatment plan progress, outstanding notes or flags, and any intake form changes since the last visit. This pre-briefing means practitioners walk into each consult prepared and informed, improving both the patient experience and clinical outcomes.

Referral and Report Management

When a referral is received or a report is due, automation ensures nothing falls through the cracks. The system can track referral expiry dates, reminder practitioners about outstanding reports, and even draft report templates pre-populated with patient data from Cliniko.

Setting Up Cliniko + Make.com + AI

Cliniko has a robust API that Make.com connects to easily. The setup typically involves creating a Make.com account and connecting it to your Cliniko account via API key, then building workflows for each automation you want.

Common integrations include: Cliniko to Twilio (for SMS reminders), Cliniko to Mailchimp or ActiveCampaign (for email sequences), Cliniko to Google Sheets (for reporting), and Cliniko to AI APIs (for personalised message generation and intake analysis).

Most practices start with appointment reminders (immediate impact on no-shows) and expand to intake automation and recall sequences. A phased approach lets you measure results and refine before adding complexity.

Frequently Asked Questions

Is patient data safe with AI automation?

Patient data security is paramount. When using AI APIs, avoid sending identifying health information. Instead, use anonymised prompts (“generate a follow-up message for a physiotherapy patient who had shoulder treatment”) rather than including patient names or specific health details in AI calls. Make.com uses encrypted connections and can be configured to comply with Australian Privacy Act requirements.

Does this work with Cliniko’s existing reminders?

Yes. Cliniko has basic built-in reminders, and the AI automation works alongside or replaces these depending on your preference. Many practices disable Cliniko’s basic reminders and use the more sophisticated AI-powered sequences instead.

How much does this cost for a typical practice?

A typical setup costs $100-300/month (Make.com subscription + SMS costs via Twilio + AI API usage). For a practice that reduces no-shows by even 2-3 per week at $100+ per consultation, the ROI is clear within the first month.

Can this work with other practice management software?

Yes. The same principles apply to other platforms like Halaxy, Nookal, Power Diary, and others. Make.com has integrations for many practice management systems, and those without native integrations can often be connected via API.

Do I need technical skills to set this up?

Basic setups (appointment reminders, simple recall sequences) can be built by anyone comfortable with Make.com’s visual interface. More complex setups (AI-powered intake analysis, dynamic treatment follow-ups) benefit from specialist help. We offer setup packages specifically for Cliniko practices.

How long does setup take?

A basic appointment reminder automation can be set up in a few hours. A comprehensive system (intake automation + reminders + recall sequences + practitioner notifications) typically takes 2-4 weeks to build, test, and refine.

Why Most Businesses Struggle with Proposals

Writing business proposals is one of those tasks that everyone knows is important but nobody actually enjoys. You spend hours crafting a detailed proposal, tailoring it to the client’s needs, getting the pricing right, making it look professional — and then you wait. Sometimes you win the work. Sometimes you never hear back. Either way, those hours are gone.

For Australian small businesses, the proposal process is often a bottleneck. You can only write so many proposals per week, which means you can only pursue so many opportunities. And when you’re busy with actual work, proposals get pushed to evenings and weekends. It’s not sustainable.

AI changes this equation dramatically. Using tools like ChatGPT and Claude combined with automation platforms like Make.com, you can create professional, personalised proposals in minutes rather than hours. Let’s walk through exactly how to set this up.

Step 1: Create Your Proposal Templates

Before any AI magic happens, you need solid templates. These aren’t generic one-size-fits-all documents — they’re structured frameworks that AI can populate intelligently.

Template Structure

A good proposal template has clearly defined sections that map to specific information sources:

• Executive Summary: What the client needs and how you’ll deliver it (generated from client brief)
• Understanding of Requirements: Demonstrating you understand their specific situation (generated from discovery call notes or brief)
• Proposed Solution: Your approach, methodology, and deliverables (generated from your service offerings matched to their needs)
• Timeline and Milestones: Project phases and key dates (generated from your standard timelines adjusted for scope)
• Investment: Pricing and payment terms (generated from your pricing calculator)
• About Us / Why Choose Us: Relevant case studies and credentials (selected from your library based on industry match)
• Terms and Conditions: Standard legal terms (static)

Each section should have a clear prompt template that tells the AI what to generate and what tone to use. Store these templates somewhere your automation can access them — Google Docs, Notion, or even a simple database.

Step 2: Build Your Client Brief Extraction System

The quality of your AI-generated proposal depends entirely on the quality of the input. Garbage in, garbage out. So you need a systematic way to capture client requirements.

Structured Intake Forms

Create a client brief form (using Typeform, Google Forms, or a custom form on your website) that asks the right questions. For a marketing agency, this might include: business type, target audience, current marketing channels, budget range, goals, timeline, and specific challenges. For a trades business, it might be: job type, property details, access requirements, preferred timeline, and budget expectations.

The key is asking enough questions to give the AI good material to work with, without making the form so long that prospects abandon it. Five to eight questions is usually the sweet spot.

Discovery Call Notes

If you prefer discovery calls over forms (or use both), AI can extract structured information from call transcripts. Record the call (with permission), run the transcript through ChatGPT or Claude with a prompt like: “Extract the following information from this discovery call transcript: client name, business type, key requirements, budget, timeline, specific pain points, decision-making process.” The AI pulls out the relevant details in a structured format that feeds into your proposal automation.

Step 3: Connect AI to Your Proposal Workflow with Make.com

This is where the magic happens. Using Make.com automation, you connect your intake form or CRM to AI, which generates the proposal content, which then populates your proposal document.

The Workflow

Here’s a typical Make.com workflow for AI proposal generation:

1. Trigger: New form submission or CRM deal stage change
2. Data extraction: Pull client brief data into variables
3. AI generation: Send each proposal section to ChatGPT/Claude API with the relevant template prompt and client data
4. Document creation: Populate a Google Docs or PDF template with the AI-generated content
5. Review notification: Send you a Slack/email notification with the draft proposal for review
6. Client delivery: After your approval, automatically email the proposal to the client

The entire process from form submission to draft proposal ready for review takes about 2-3 minutes. Compare that to the 2-3 hours most businesses spend writing proposals manually.

Step 4: Integrate Pricing Calculators

Pricing is often the trickiest part of a proposal, and it’s where many businesses hesitate to use AI. The solution is to separate pricing logic from AI content generation.

Build your pricing calculator as a spreadsheet or database with your rates, package options, and pricing rules. The automation pulls the relevant pricing based on the client’s requirements and inserts the calculated figures into the proposal. AI doesn’t decide your prices — it just presents them professionally within the generated proposal text.

For more complex pricing (like custom AI agent solutions or multi-phase projects), the system can generate a pricing range and flag it for your manual review before the proposal goes out.

Step 5: Using ChatGPT and Claude Effectively for Proposals

Not all AI prompts are created equal. Here’s how to get the best results from ChatGPT and Claude for proposal writing.

Prompt Engineering for Proposals

Your prompts should include: the role (you are a professional proposal writer for [your industry]), the context (client brief data), the output format (specific section with word count and tone guidelines), and constraints (don’t make claims we can’t back up, use Australian English, maintain professional but approachable tone).

A good prompt for an executive summary might look like: “You are writing a proposal executive summary for an Australian [your industry] business. The client is [client name], a [their business type] based in [location]. Their key challenges are [pain points from brief]. They need [requirements]. Write a 150-200 word executive summary that demonstrates understanding of their situation and briefly introduces our solution. Use professional but warm Australian English. Do not make specific ROI claims.”

Claude vs ChatGPT for Proposals

Both work well for proposal content. ChatGPT (via the API) is generally faster and cheaper for high-volume proposal generation. Claude tends to produce slightly more nuanced, context-aware writing and is better at maintaining a consistent tone across long documents. For many businesses, as discussed in our ChatGPT for business guide, using both for different sections can yield the best results.

Step 6: Automated Follow-up Sequences

A proposal without follow-up is a proposal forgotten. AI automation handles this beautifully.

Post-Proposal Follow-up Workflow

After sending the proposal, your automation kicks in with a timed follow-up sequence. Day 2: “Just checking you received the proposal — any questions?” Day 5: “I’d love to discuss the proposal and answer any questions. Here are a few times I’m available this week.” Day 10: “Following up on our proposal. I’ve also attached a case study from a similar project that might be helpful.” Day 14: Final follow-up offering to revise the proposal if anything doesn’t quite fit.

If the client opens the proposal (tracked via your proposal platform), the system can notify you in real time, so you know the best moment to pick up the phone. If they open the pricing section multiple times, that’s a buying signal the system flags for you.

This kind of intelligent automation ensures no proposal falls through the cracks, which alone can increase your win rate significantly.

Real-World Results

Australian businesses using AI proposal automation typically see proposal creation time drop from 2-4 hours to 15-30 minutes (including review time). Response time to enquiries improves dramatically — instead of promising a proposal “by end of week,” you can often deliver same-day. And because you can produce more proposals with less effort, you can pursue more opportunities without working longer hours.

The quality of AI-generated proposals also tends to be more consistent than manually written ones. When you’re writing your fifth proposal of the week at 10pm, the quality inevitably drops. AI doesn’t get tired, and every proposal starts from the same high-quality baseline.

Frequently Asked Questions

Won’t AI-generated proposals sound generic?

Only if you use generic prompts. When you feed AI detailed client information and use well-crafted prompt templates, the output is personalised and specific. Always review and add personal touches before sending — AI creates the draft, you add the human element.

Which proposal tools integrate with Make.com?

Popular options include Google Docs, PandaDoc, Proposify, Better Proposals, and Qwilr. All have APIs or Make.com integrations that allow automated document creation and delivery.

How much does AI proposal automation cost?

A typical setup costs $100-300/month for Make.com plus AI API costs (usually $10-50/month depending on volume). Proposal platforms add $20-100/month. Total cost is typically $150-400/month, which pays for itself if it helps you win even one extra project per month.

Can I use this for government tenders?

AI can help draft sections of tender responses, but government tenders typically have strict formatting and compliance requirements. Use AI to generate draft content, then have someone experienced in tender writing review and finalise. AI is excellent for the writing-heavy sections but shouldn’t be solely relied upon for compliance-critical elements.

What about confidentiality of client information?

When using AI APIs, be mindful of data handling. OpenAI and Anthropic’s API data is not used for training. Avoid sending highly sensitive information (financial details, personal data) through AI prompts. Use anonymised or generalised descriptions where possible, and add specific details manually during review.

How do I get started if I’m not technical?

Start with the simplest version: create a proposal template in Google Docs, use ChatGPT directly (not the API) to generate draft sections by pasting in client brief information, then copy-paste the content into your template. Once you see the time savings, invest in automating the process with Make.com or work with a specialist like Loudachris to set it up properly.

HubSpot Is Powerful — AI Makes It Brilliant

HubSpot is already one of the most capable CRM and marketing platforms available to Australian businesses. But here’s what most users don’t realise: they’re using maybe 30% of HubSpot’s potential. Not because they’re lazy, but because getting the most out of HubSpot requires sophisticated automation that goes beyond what the native tools offer.

That’s where AI comes in. By connecting HubSpot to AI models through automation platforms like Make.com, you can add intelligence layers that transform HubSpot from a powerful database into a predictive, proactive sales and marketing engine.

This guide walks through the most impactful ways to combine HubSpot with AI automation, with practical setups you can implement today.

AI-Enhanced Lead Scoring

HubSpot’s native lead scoring is useful but limited. You assign points for actions (visited pricing page: +10, downloaded ebook: +5, opened email: +2) and demographic criteria (right industry: +15, decision-maker title: +20). It works, but it’s static and requires constant manual adjustment.

How AI Improves Lead Scoring

AI-enhanced lead scoring analyses patterns across your entire contact database to identify which behaviours and attributes actually predict conversion — not which ones you think predict conversion. The AI examines your closed-won deals and works backwards: what did these contacts have in common? What sequence of actions did they take? How quickly did they move through stages?

This analysis often reveals surprising insights. Maybe downloading your pricing guide isn’t actually a strong buying signal, but visiting your case studies page three times in a week is. Maybe the time between first touch and second touch matters more than what they actually do. AI spots these patterns in your data and creates scoring models that reflect reality rather than assumptions.

Implementation with Make.com

A practical implementation works like this: Make.com monitors HubSpot contact activity via webhooks. When significant events occur (page visits, email engagement, form submissions), the data is sent to an AI model (ChatGPT or Claude API) along with the contact’s history. The AI returns a refined score and a brief explanation of why this lead is hot, warm, or cold. Make.com updates the HubSpot contact record with the AI score and reasoning.

Your sales team then sees not just a number, but a context-rich assessment: “Score: 85/100. This contact has visited the pricing page 4 times in 2 days, matches your ideal customer profile (marketing agency, 10-50 employees, Sydney), and their engagement pattern matches your typical 14-day sales cycle.” That’s infinitely more useful than “Score: 85.”

Automated Nurture Sequences That Adapt

HubSpot’s email sequences are good, but they’re essentially predetermined paths. Contact enters sequence, gets email 1, waits 3 days, gets email 2, and so on. AI-enhanced nurturing adapts the journey based on what’s actually working.

Dynamic Content Selection

Instead of sending the same email to everyone at step 3 of your nurture sequence, AI can select the most relevant content based on the contact’s behaviour, industry, and engagement history. A contact who’s been reading your technical blog posts gets a detailed case study. A contact who’s been watching your videos gets an invitation to a webinar. A contact who’s been browsing your pricing page gets a consultation offer.

This is set up through workflow automation: when a sequence step triggers, Make.com sends the contact’s profile and history to the AI, which recommends the best content piece, and the appropriate email variant is sent from HubSpot.

Optimal Timing

AI can also optimise when nurture emails are sent. Rather than fixed delays (send email 2 three days after email 1), the system analyses when each contact is most likely to engage. If a contact typically opens emails at 7am on Tuesdays, that’s when they get their next nurture email. This kind of send-time optimisation can improve open rates by 15-25%.

Deal Pipeline Automation

Managing deals through the pipeline is where many sales teams lose efficiency. Deals stall, follow-ups get missed, and by the time someone notices a deal has gone cold, it’s too late.

AI-Predicted Close Probabilities

AI can analyse deal data (size, stage, velocity, contact engagement, competitive landscape) and predict the likelihood of each deal closing. More importantly, it can flag deals at risk before they stall. “Deal X hasn’t progressed in 8 days, which is 3x longer than your average at this stage. Recommended action: schedule a check-in call.”

Automated Pipeline Hygiene

AI automation can enforce pipeline discipline without manual oversight. Deals that have been in the same stage too long get flagged. Missing fields (like expected close date or decision-maker identified) trigger reminders to the deal owner. When a deal moves to a new stage, the system automatically creates the tasks and reminders appropriate for that stage.

Chatbot Integration with HubSpot

HubSpot has native chatbot capabilities, but connecting an AI-powered chatbot takes the experience to another level. Instead of rigid decision trees, visitors get conversational, context-aware responses that can answer complex questions, qualify leads in real time, and seamlessly hand off to human sales reps when appropriate.

The AI chatbot can access the visitor’s HubSpot history (if they’re a known contact) and personalise the conversation accordingly. “Welcome back, Sarah. Last time you were looking at our enterprise plan — would you like to pick up where you left off?” This level of personalisation drives significantly higher engagement and conversion rates.

Smart Reporting Dashboards

HubSpot’s reporting is comprehensive, but interpreting the data still requires human analysis. AI can add an intelligence layer that surfaces insights automatically.

Automated Insight Generation

AI analyses your HubSpot data weekly (or daily) and generates a summary of key findings: which campaigns are outperforming, which lead sources are delivering the best quality, where deals are stalling in the pipeline, and what trends are emerging. Instead of spending an hour reviewing dashboards, you get a concise brief highlighting what matters.

Anomaly Detection

AI can spot unusual patterns that humans miss in busy dashboards. A sudden drop in email open rates, an unexpected spike in form submissions from a new geography, or a change in average deal velocity — these anomalies are flagged automatically so you can investigate and respond quickly.

These insights can be delivered via Slack, email, or even as comments on a shared Google Doc, making them accessible to the entire team without requiring everyone to log into HubSpot.

Getting Started: HubSpot + AI Integration

The best starting point depends on your biggest pain point. If your sales team is struggling with lead prioritisation, start with AI lead scoring. If your nurture sequences feel generic, start with dynamic content selection. If deal management is your bottleneck, start with pipeline automation.

The technical setup typically involves HubSpot (any tier, though Professional or Enterprise gives you more API access), a Make.com account, and API access to ChatGPT or Claude. Most integrations can be built in a few days and refined over a few weeks as you tune the AI models to your specific data, much like the approach we detail for AI-powered lead follow-up.

Frequently Asked Questions

Do I need HubSpot Professional or Enterprise for AI integration?

You can do basic AI integration with HubSpot Free or Starter using Make.com and the HubSpot API. However, Professional and Enterprise tiers give you more workflow capabilities, custom properties, and API call limits that make advanced integrations smoother.

How does this compare to HubSpot’s built-in AI features?

HubSpot has been adding AI features (content assistant, predictive lead scoring on Enterprise). These are convenient but limited in customisation. Custom AI integration via Make.com gives you complete control over the AI models, prompts, and logic, allowing far more sophisticated and tailored automation.

What’s the cost of adding AI to HubSpot?

Typical monthly costs: Make.com ($30-100), AI API usage ($20-100 depending on volume), plus any one-time setup costs if you work with a specialist. Total ongoing cost is usually $50-200/month on top of your HubSpot subscription.

Will this break my existing HubSpot workflows?

No. AI integrations run alongside your existing workflows, not instead of them. They add intelligence layers via custom properties and external triggers. Your existing automation continues to function, and AI enhancements are additive.

How long does it take to see results?

AI lead scoring typically shows value within 2-4 weeks as the model learns from your data. Nurture sequence optimisation takes 4-6 weeks to generate meaningful performance comparisons. Pipeline automation shows immediate efficiency gains from day one.

Can I do this myself or do I need a developer?

Basic setups (like AI-generated lead summaries or automated insights) can be built by anyone comfortable with Make.com. More complex integrations (custom scoring models, dynamic content selection) benefit from specialist expertise. We’re happy to help you assess what’s realistic as a DIY project vs what needs professional setup.

Why Shopify Store Owners Need AI Automation

Running a Shopify store in Australia is more competitive than ever. With over 100,000 Australian businesses on Shopify, standing out requires more than great products — it requires exceptional operations and customer experience. And that’s nearly impossible to deliver manually once you grow past a handful of orders per day.

AI automation connected to your Shopify store handles the operational heavy lifting: processing orders, managing inventory, recovering abandoned carts, segmenting customers, and delivering personalised follow-ups. The result? You spend less time on admin and more time on strategy, product development, and growth.

This tutorial walks through how to set up these automations using Shopify, Make.com (or n8n), and AI tools. For the broader e-commerce AI picture, check out our AI for e-commerce industry page.

Order Processing Automation

Manual order processing is the first thing that should go when you automate your Shopify store. Every step that doesn’t require human judgment can be handled by automation.

Automated Order Routing

When an order comes in, automation can route it based on rules: domestic vs international, standard vs express shipping, in-stock vs backorder items, single-item vs multi-item orders. Each route triggers different workflows — domestic orders go straight to your fulfilment team or 3PL, international orders trigger customs documentation, backorder items send a customer notification with estimated delivery.

AI-Enhanced Order Review

AI can flag orders that might need human attention: unusually large orders (potential fraud or wholesale enquiry), orders with conflicting shipping information, repeat customers ordering different items than usual (potential gift purchases needing different packaging). This intelligent flagging means you only spend time on orders that genuinely need your attention.

Automatic Status Updates

As orders progress through your fulfilment process, automation sends personalised status updates to customers. Not just “your order has shipped” but contextual messages: “Your organic coffee beans have been freshly roasted and are on their way! Here’s your tracking number.” AI generates these personalised messages based on the products ordered and the customer’s purchase history.

Inventory Alert Automation

Running out of stock is a conversion killer. AI-powered inventory automation goes beyond simple low-stock alerts.

Predictive Stock Alerts

AI analyses your sales velocity, seasonal patterns, and supplier lead times to predict when you’ll run out of each product. Instead of getting a “low stock” alert when you’re down to 5 units (and it takes 3 weeks to restock), you get a “reorder now” alert 4 weeks before predicted stockout. The system can even auto-generate purchase orders for your suppliers.

Demand Forecasting

By analysing historical sales data, seasonal trends, marketing campaign schedules, and even external factors (weather, school holidays, public events), AI can forecast demand and help you optimise stock levels. This reduces both stockouts and overstock situations, improving cash flow and customer satisfaction simultaneously.

Abandoned Cart Recovery with AI

The average cart abandonment rate is around 70%. Recovering even a fraction of those abandoned carts can significantly boost revenue. AI makes recovery more effective than traditional approaches.

Personalised Recovery Messages

Instead of generic “you left something in your cart” emails, AI generates personalised recovery messages based on the specific products, the customer’s browsing history, and their relationship with your store. A first-time visitor gets a different message than a loyal customer. Someone who abandoned at checkout (price sensitivity) gets a different approach than someone who abandoned during browsing (not ready to buy).

Multi-Channel Recovery

Automation can trigger recovery attempts across multiple channels: email, SMS, and even retargeting ads on social media. The timing and channel selection is optimised based on what works best for each customer segment, as explored in our e-commerce automation guide.

Smart Discounting

AI can determine whether a discount is necessary for recovery or if a simple reminder is enough. Loyal customers might not need a discount — just a reminder. Price-sensitive first-time shoppers might need a 10% incentive. The system optimises discount usage to maximise revenue rather than training customers to expect discounts every time they abandon a cart.

Customer Segmentation with AI

Effective marketing requires knowing your customers, and AI-powered segmentation goes far beyond basic demographics.

Behavioural Segmentation

AI analyses purchase history, browsing behaviour, email engagement, and review activity to create dynamic customer segments. These might include: “frequent buyers” (buy monthly+), “seasonal shoppers” (only buy around Christmas), “deal seekers” (only purchase during sales), “brand loyalists” (consistently buy the same products), and “at-risk” (purchase frequency declining).

Predictive Segments

More powerfully, AI can create predictive segments: customers likely to make their next purchase within 7 days, customers at risk of churning, customers likely to respond to cross-sell offers. Each segment receives tailored communication through automated workflows, maximising engagement and revenue per customer.

Personalised Post-Purchase Follow-ups

The post-purchase experience is where customer loyalty is built or lost. AI automation ensures every customer gets a thoughtful follow-up journey.

Product-Specific Follow-ups

AI generates follow-up content based on what the customer purchased. Bought skincare? Receive usage tips and application guides. Bought a complex product? Get setup instructions and a “need help?” check-in. Bought consumables? Get a replenishment reminder when they’re likely running low.

Review Requests

Automated review requests are timed based on product type and delivery time. The messaging is personalised and includes specific questions relevant to the product purchased. AI can even analyse incoming reviews to identify common themes and flag negative reviews for immediate response.

Returns Handling Automation

Returns are painful for both customers and store owners. AI automation makes the process smoother for everyone.

An AI chatbot handles initial return requests, collecting reason codes and determining whether the item is eligible for return based on your policy. For straightforward returns, the system generates a return label, provides instructions, and processes the refund or exchange automatically. For edge cases, it escalates to your team with all the relevant information already collected.

The system tracks return patterns to identify problematic products (high return rate on a specific size or colour) and can even predict which orders are likely to result in returns based on historical patterns, allowing you to proactively improve the experience.

Setting Up Shopify + AI with Make.com

The technical setup connects Shopify to Make.com via Shopify’s API and webhooks. Key triggers include: new order, order fulfilled, customer created, cart abandoned, inventory level changed, and product review submitted.

Make.com processes these triggers through AI modules (ChatGPT or Claude API calls for content generation and analysis) and connects to your other tools: email platform (Klaviyo, Mailchimp), SMS service (Twilio), helpdesk (Zendesk, Gorgias), and accounting software (Xero).

The beauty of this approach is modularity. Start with one automation (like abandoned cart recovery), measure the impact, and add more workflows over time. Each new automation builds on the data and infrastructure you’ve already set up.

Frequently Asked Questions

Does this work with Shopify Basic or do I need Shopify Plus?

Most automations work with any Shopify plan, including Basic. Shopify Plus offers additional API access and checkout customisation, but the core order, customer, and inventory APIs are available on all plans.

How does this compare to Shopify Flow?

Shopify Flow is Shopify’s built-in automation tool (available on Shopify and Advanced plans). It’s great for simple Shopify-internal automations but limited in connecting to external tools and AI models. Make.com complements Shopify Flow — use Flow for simple internal triggers and Make.com for complex, cross-platform, AI-enhanced workflows.

What’s the ROI of Shopify AI automation?

The biggest ROI typically comes from abandoned cart recovery (5-15% recovery rate improvement) and personalised post-purchase follow-ups (increased repeat purchase rate). For a store doing $50K/month, even a 5% improvement in these areas can add $30-60K annually.

Can I automate Shopify with n8n instead of Make.com?

Absolutely. n8n has Shopify integration and supports all the same automation patterns. The choice between Make.com and n8n comes down to your preference for visual interface (Make.com) vs code flexibility (n8n). Both work well with AI integration.

How do I handle high-volume stores?

For stores processing hundreds of orders daily, ensure your Make.com plan has sufficient operations, use webhook-based triggers (more efficient than polling), and consider batching AI calls to manage API costs. n8n self-hosted can be more cost-effective at very high volumes.

Is customer data safe with AI automation?

When using AI APIs (OpenAI, Anthropic), API data is not used for model training. However, be mindful of what data you send — avoid passing sensitive payment information through AI prompts. Use anonymised customer segments rather than individual customer data for AI analysis where possible.

To automate MYOB with AI, connect your MYOB Business account to Make.com using MYOB’s API, then build workflows that handle expense categorisation, bank reconciliation, payroll processing, BAS preparation, supplier payments, and cash flow reporting automatically. This eliminates hours of repetitive bookkeeping each week while reducing errors and improving financial visibility.

Why Australian Businesses Should Automate MYOB

MYOB remains one of Australia’s most popular accounting platforms, used by hundreds of thousands of small and medium businesses. It’s solid software, but let’s be honest—nobody starts a business because they love data entry. Manually categorising expenses, reconciling bank feeds, processing payroll, and preparing BAS statements eats up hours every week that could be spent on actual revenue-generating work.

The good news is that MYOB’s API has matured significantly, and when you connect it to AI-powered workflow automation, you can eliminate most of the manual grunt work. This isn’t about replacing your bookkeeper or accountant—it’s about freeing them to focus on advisory work rather than data entry.

Expense Categorisation With AI

One of the most tedious bookkeeping tasks is categorising expenses. Every bank transaction needs to be matched to the right account code, and when you’re doing this manually for hundreds of transactions per month, mistakes are inevitable.

AI-powered expense categorisation works by analysing the transaction description, amount, and your historical categorisation patterns. Here’s how to set it up with MYOB and Make.com:

The Workflow

1. Trigger: New bank feed transaction appears in MYOB.
2. AI Analysis: Make.com sends the transaction details to ChatGPT (or Claude) with your chart of accounts and categorisation rules.
3. Categorisation: The AI suggests the correct account code based on the merchant name, amount, and description.
4. Confidence Check: If the AI is 90%+ confident, it auto-categorises. If below 90%, it flags the transaction for manual review.
5. Learning: Over time, the AI learns from your corrections and becomes more accurate.

The result is that 70–85% of your transactions are auto-categorised correctly, and the remaining 15–30% are flagged for quick human review. What used to take 3 hours per week now takes 20 minutes.

Automated Bank Reconciliation

Bank reconciliation in MYOB can be partially automated by combining the bank feed with AI matching logic. The AI compares incoming bank transactions against outstanding invoices and expected payments, then suggests matches. For recurring expenses (rent, subscriptions, utilities), the system learns the pattern and auto-reconciles after the first few months.

The key is building rules that handle your specific business patterns. A tradesman might have regular Bunnings purchases that always go to “Materials.” A professional services firm might have monthly SaaS subscriptions that always go to “Software Expenses.” The AI identifies these patterns and creates rules automatically.

Payroll Automation

MYOB’s payroll module handles the calculations, but there’s still manual work involved in collecting timesheets, checking leave balances, and processing each pay run. AI automation can streamline this significantly:

• Timesheet collection: Automatically pull timesheet data from your rostering system (Deputy, Tanda, or even a shared Google Sheet) and feed it into MYOB.
• Leave management: When staff submit leave requests via email or a form, the automation checks their balance in MYOB, approves or flags the request, and updates the roster.
• Pay run preparation: The automation prepares the pay run data, including any allowances, deductions, or variations, and presents it for a single-click approval.
• Payslip distribution: After processing, payslips are automatically emailed to each employee from MYOB.
• Super lodgement reminders: Automated reminders before super guarantee deadlines, with the data pre-populated for quick lodgement.

For businesses with 5–50 employees, this reduces payroll processing from a half-day task to 15 minutes of review and approval.

BAS Preparation Automation

BAS preparation is one of those quarterly tasks that causes anxiety for many business owners. Getting the GST figures wrong can mean ATO penalties, and manually calculating everything is error-prone. Here’s how AI automation helps:

Automated BAS Prep Workflow

1. Pre-BAS review: Two weeks before the BAS deadline, the automation runs a health check on your MYOB data—looking for uncategorised transactions, missing GST codes, and unreconciled items.
2. Alert notifications: You (or your bookkeeper) receive a summary of items that need attention before the BAS can be prepared.
3. GST calculation verification: The AI cross-references your MYOB GST report against your bank statements to catch discrepancies.
4. Draft BAS generation: Once the data is clean, the automation generates a draft BAS summary for review.
5. Lodgement reminder: Automated reminders at 14 days, 7 days, and 2 days before the deadline.

If you’re already using Xero with AI automation, the principles are identical—only the API connections differ. Many of the same Make.com scenarios work with both platforms.

Supplier Payment Scheduling

Cash flow management is critical for Australian SMEs, and supplier payment timing is a key lever. AI automation can help you optimise when you pay suppliers:

• Due date tracking: The automation monitors all outstanding supplier invoices in MYOB and creates a payment schedule.
• Early payment discount detection: If a supplier offers a discount for early payment (e.g., 2% for payment within 7 days), the AI flags these opportunities and calculates whether the discount is worth taking based on your current cash position.
• Payment batch preparation: On your chosen payment day, the automation prepares a batch of supplier payments for single-click approval.
• Cash flow forecasting: By analysing payment patterns, receivables, and upcoming obligations, the AI provides a rolling 30/60/90-day cash flow forecast.

Cash Flow Reporting

Most business owners check their bank balance and hope for the best. AI-powered cash flow reporting gives you actual visibility:

• Daily cash position: An automated morning email or Slack message showing your current cash position across all accounts.
• Weekly P&L snapshot: A simple profit and loss summary pulled from MYOB and formatted for quick reading.
• Monthly dashboard: A Google Sheet or dashboard that auto-updates with key financial metrics from MYOB.
• Anomaly alerts: The AI monitors for unusual transactions (unexpectedly large expenses, duplicate payments, missing expected income) and alerts you immediately.

Setting Up MYOB + Make.com Integration

The technical setup involves connecting MYOB’s API to Make.com. Here’s the high-level process:

1. MYOB API access: Register for MYOB API access through the MYOB Developer portal. You’ll receive API credentials (Client ID and Client Secret).
2. Make.com connection: In Make.com, use the HTTP module to connect to MYOB’s API endpoints. You’ll authenticate using OAuth 2.0.
3. Build scenarios: Create individual scenarios for each workflow (expense categorisation, payroll prep, BAS checks, etc.).
4. Test thoroughly: Run each scenario with test data before going live. Pay particular attention to GST handling and account code mapping.
5. Monitor and refine: Watch the automations for the first month and adjust rules based on accuracy.

For professional services firms, these automations are particularly valuable because they free up billable hours that were previously consumed by internal admin work.

Common MYOB Automation Pitfalls to Avoid

A few things to watch out for when automating MYOB workflows:

• GST handling: Always verify that automated categorisations apply the correct GST code. A mistake here flows through to your BAS.
• API rate limits: MYOB’s API has rate limits. Design your automations to batch requests rather than making individual calls for every transaction.
• Multi-currency: If you deal with foreign suppliers, ensure your automation handles currency conversion correctly.
• Year-end processes: Some automations need to be paused or adjusted during financial year-end processes. Build in awareness of your financial year dates.

Frequently Asked Questions

Does this work with MYOB Business or just MYOB AccountRight?

These automations work with both MYOB Business (the cloud version) and MYOB AccountRight Live. The API endpoints differ slightly, but Make.com can connect to both. MYOB Business is easier to integrate because it’s fully cloud-based.

How accurate is AI expense categorisation?

In our experience, AI categorisation achieves 75–85% accuracy out of the box, improving to 90%+ within 2–3 months as it learns your specific patterns. The confidence threshold (we recommend 90%) ensures low-confidence categorisations are flagged for human review.

Will my accountant be okay with automated bookkeeping?

Most accountants welcome automation because it means cleaner data when they receive your file. The automation doesn’t replace accountant oversight—it eliminates the tedious data entry so your accountant can focus on strategic advice. We recommend involving your accountant in the setup to ensure account codes and GST rules are mapped correctly.

How much does MYOB API access cost?

MYOB API access is included with your MYOB subscription at no additional cost. You’ll need a Make.com subscription (from $9/month for basic plans) and potentially a ChatGPT API key (pay-per-use, typically $5–20/month for a small business).

Can I automate MYOB invoicing as well?

Absolutely. You can create automations that generate and send invoices based on completed jobs, time entries, or scheduled billing. This is particularly useful for businesses that invoice regularly for recurring services.

What happens if the automation makes a mistake?

Every automated action is logged, so mistakes are easy to trace and correct. The confidence threshold system means uncertain categorisations are always flagged for review. For critical processes like BAS preparation, the automation generates a draft for human review rather than lodging directly.

To build an AI email assistant for your business, connect Gmail or Outlook to Make.com, route incoming emails through ChatGPT for categorisation and response drafting, and set up automated follow-up sequences and meeting scheduling triggers. A well-built AI email assistant can save 1–2 hours per day for busy professionals who deal with high email volumes.

Why Email Is Still the Biggest Time Sink in Business

Despite the rise of Slack, Teams, and every other messaging platform, email remains the backbone of business communication in Australia. The average professional spends 2.5 hours per day on email, and for business owners, it’s often more. The challenge isn’t just reading emails—it’s the mental overhead of deciding what needs attention, drafting responses, following up on unanswered messages, and extracting action items.

An AI email assistant doesn’t replace you in conversations. It handles the repetitive overhead: sorting, drafting, reminding, and scheduling. Think of it as having a capable executive assistant who pre-processes your inbox before you ever see it.

What an AI Email Assistant Can Do

Before building anything, let’s map out the capabilities. A well-designed AI email assistant handles these tasks through workflow automation:

• Auto-categorisation: Incoming emails are sorted into categories (urgent, client enquiry, invoice, newsletter, spam, internal) without you touching them.
• Draft responses: For common email types (meeting requests, quote enquiries, information requests), the AI drafts responses for your review.
• Follow-up reminders: If you send an email and don’t receive a reply within a set timeframe, the assistant reminds you or sends a follow-up automatically.
• Meeting scheduling: When someone requests a meeting, the AI checks your calendar availability and suggests times, or sends a booking link.
• Sentiment analysis: The AI flags emails that contain negative sentiment, urgency, or frustration so you can prioritise them.
• Data extraction: Pull key information (phone numbers, addresses, project names, dollar amounts) from emails and log them in your CRM.

Architecture: Gmail/Outlook + Make.com + ChatGPT

The technical stack is straightforward. Here’s how the pieces fit together:

Email Provider (Gmail or Outlook)

Both Gmail and Microsoft 365 Outlook work well. Gmail has slightly better integration with Make.com out of the box, but Outlook works fine too. You’ll connect your email account to Make.com so it can read incoming messages and send responses on your behalf.

Automation Platform (Make.com)

Make.com is the orchestration layer. It watches your inbox for new emails, routes them through AI processing, and executes actions based on the results (label emails, create draft responses, send notifications, update your CRM).

AI Layer (ChatGPT API)

OpenAI’s ChatGPT API (or Claude API) handles the intelligence: understanding email content, categorising intent, drafting appropriate responses, and extracting structured data from unstructured email text.

Step-by-Step: Building Your AI Email Assistant

Step 1: Connect Gmail/Outlook to Make.com

In Make.com, create a new scenario and add a Gmail or Outlook trigger module set to “Watch Emails.” Configure it to check for new emails every 5–15 minutes. Apply a basic filter to exclude obvious spam and newsletters (you can refine this later).

Step 2: Set Up Email Categorisation

Add a ChatGPT module that receives the email subject, sender, and body. Provide a system prompt like:

“You are an email assistant for [Business Name], an Australian [industry] business. Categorise this email into exactly one category: URGENT, CLIENT_ENQUIRY, QUOTE_REQUEST, INVOICE, MEETING_REQUEST, FOLLOW_UP_NEEDED, NEWSLETTER, INTERNAL, or SPAM. Also provide a one-sentence summary and a sentiment score (positive, neutral, negative). Respond in JSON format.”

The AI returns structured data that Make.com can use in subsequent steps.

Step 3: Route Based on Category

Use Make.com’s router module to create different paths for each category. For example:

• URGENT: Send an immediate push notification to your phone via Slack, SMS, or Pushover.
• CLIENT_ENQUIRY: Draft a response and save it in your drafts folder. Also create a CRM entry.
• QUOTE_REQUEST: Extract project details and create a task in your project management tool.
• MEETING_REQUEST: Check calendar availability and draft a response with available times or a Calendly link.
• INVOICE: Forward to your bookkeeper or accounts payable workflow.

Step 4: AI Response Drafting

For categories that warrant a response, add another ChatGPT module with a prompt tailored to your business tone. For example:

“Draft a professional but friendly reply to this email on behalf of [Your Name] at [Business Name]. Use Australian English. Keep it concise (3–5 sentences). Don’t make commitments or promise specific timelines. Sign off with ‘Cheers, [Your Name]’. If you’re unsure about anything, note it for human review.”

The draft is saved to your email drafts folder so you can review, edit if needed, and send with one click. This is much faster than writing from scratch. For more on using ChatGPT in Australian business contexts, see our detailed guide.

Step 5: Follow-Up Reminder System

Create a separate Make.com scenario that monitors sent emails. If a sent email doesn’t receive a reply within your defined timeframe (e.g., 48 hours for client emails, 5 days for supplier emails), the system either sends you a reminder or automatically sends a polite follow-up.

The follow-up message should reference the original email and be brief:

“Hi [Name], just bumping this to the top of your inbox in case it got buried. Happy to chat whenever suits. Cheers, [Your Name]”

Step 6: Meeting Scheduling Integration

When the AI detects a meeting request, it can check your Google Calendar or Outlook Calendar for availability and suggest times. Even better, it can include a direct booking link (Calendly, Cal.com, or TidyCal) so the other person can self-schedule without the back-and-forth.

Step 7: Sentiment Analysis and Priority Scoring

The AI assigns each email a sentiment score. Negative-sentiment emails from clients get flagged immediately because they often indicate issues that need fast resolution. This connects well with AI agent capabilities where the system can take contextual actions based on sentiment.

Advanced Features Worth Adding

Once your basic assistant is running, consider these enhancements:

• Email thread awareness: Instead of processing each email in isolation, maintain context across an entire email thread so the AI understands the full conversation history.
• Attachment processing: Use AI to read PDF attachments (quotes, proposals, invoices) and extract key data automatically.
• Multi-language support: If you receive emails in languages other than English, the AI can translate and categorise them automatically.
• Out-of-hours auto-responses: Outside business hours, the assistant sends an intelligent auto-response that acknowledges the email and sets expectations for when they’ll hear back.
• Weekly email analytics: Generate a weekly summary of your email patterns: volume by category, average response time, and follow-ups outstanding.

For Australian small businesses looking at the broader picture of AI automation, our guide on AI automation for small business covers how email automation fits into a larger strategy.

Privacy and Security Considerations

When building an AI email assistant, keep these privacy considerations in mind:

• Data processing: Emails are sent to OpenAI’s API for processing. Review OpenAI’s data usage policies and ensure you’re comfortable with how data is handled.
• Sensitive information: Consider excluding emails from certain senders (legal, HR, medical) from AI processing. Use Make.com filters to route these directly to you.
• Australian Privacy Act: If you’re handling personal information, ensure your AI email processing complies with the Australian Privacy Principles (APPs).
• Client consent: Be transparent with clients if AI is involved in reading and responding to their emails. A simple note in your email signature is sufficient.

Frequently Asked Questions

Will the AI send emails without my approval?

Not unless you configure it to. The recommended approach is to save AI-drafted responses as drafts in your email client. You review and send with one click. For low-risk automated responses (appointment confirmations, out-of-office replies), you can enable auto-send.

How much does an AI email assistant cost to run?

Make.com costs from $9/month. ChatGPT API costs depend on volume but typically run $10–30/month for processing 50–100 emails per day. Total: roughly $20–50/month for most small businesses. That’s a bargain compared to the 1–2 hours per day it saves.

Does this work with Google Workspace and Microsoft 365?

Yes, both are fully supported. Gmail (Google Workspace) has a native Make.com module. Microsoft 365 Outlook connects via the Microsoft Graph API module in Make.com.

Can the AI handle emails in Australian English correctly?

Yes. Specify “Australian English” in your system prompt and the AI will use correct spelling (organisation, not organization) and appropriate tone. It handles Aussie business conventions well, including sign-offs like “Cheers” and “Kind regards.”

What if the AI misinterprets an email?

This is why draft mode is recommended for responses. You always review before sending. For categorisation, the AI is typically 90%+ accurate, and any miscategorised emails can be manually re-sorted. The system improves as you refine your prompts over time.

How long does it take to set up?

A basic AI email assistant (categorisation + draft responses) can be built in 3–4 hours. A full-featured system with follow-ups, meeting scheduling, and sentiment analysis takes 8–12 hours. Once running, maintenance is minimal—mostly prompt refinement based on edge cases you encounter.

To automate Google review requests with AI, connect your job management or CRM system to an automation platform like Make.com, then create triggered sequences that send personalised review requests via SMS and email at optimal times after service completion. This approach typically increases review volume by 3–5x compared to manual asking, without adding any admin work to your team’s day.

Why Google Reviews Matter More Than Ever for Australian Businesses

If you run a local business in Australia, Google reviews are one of the most powerful levers you have for attracting new customers. Google’s local search algorithm weighs three factors heavily: relevance, distance, and prominence. Reviews directly influence prominence, and they also affect click-through rates from search results. A business with 50 genuine reviews and a 4.7 rating will almost always outperform a competitor with 8 reviews and a 4.9 rating.

Beyond rankings, reviews build trust. Research from BrightLocal shows that 87% of consumers read online reviews for local businesses, and 79% trust them as much as personal recommendations. For tradies, healthcare providers, professional services firms, and retail businesses across Australia, a consistent flow of fresh reviews is the difference between a steady pipeline and a quiet phone.

The problem is that most businesses rely on memory and goodwill to collect reviews. A technician finishes a job, means to ask for a review, gets busy, and it never happens. Even when someone does ask, only a fraction of customers follow through. That’s where workflow automation transforms the game entirely.

How Automated Review Request Sequences Work

An automated review request sequence is a series of messages triggered by a specific event—usually the completion of a job or service. Instead of relying on staff to remember, the system handles everything automatically. Here’s what a typical sequence looks like:

Step 1: Trigger Event

When a job is marked as complete in your job management system (ServiceM8, Tradify, Jobber, Cliniko, or your CRM), this fires a webhook to your automation platform. If you’re using Make.com, this webhook starts the scenario instantly.

Step 2: Initial SMS (2 Hours After Completion)

The first message goes out via SMS, because text messages have a 98% open rate compared to email’s 20%. The message is short, personalised, and includes a direct link to your Google review page. For example:

“Hi [First Name], thanks for choosing [Business Name] today! We’d love your feedback—it takes 30 seconds. [Direct Google Review Link] — Cheers, [Tech Name]”

Timing matters enormously. Sending within 2 hours of service completion catches customers while the experience is fresh and they’re feeling positive about the work done.

Step 3: Follow-Up Email (24 Hours Later)

If the customer hasn’t left a review within 24 hours, a follow-up email goes out. This email can include more detail—perhaps a brief summary of the work completed, a thank you, and another direct link. The email should look professional but personal, not like a mass mailout.

Step 4: Final Gentle Nudge (5 Days Later)

A last SMS or email goes out at the five-day mark. After this, the sequence stops. You never want to harass customers—three touchpoints is the sweet spot between persistence and respect.

Choosing the Right Channels: SMS vs Email vs Both

For Australian businesses, SMS is the strongest channel for review requests. Australians check their phones constantly, and a well-timed text feels personal rather than spammy. Email works well as a follow-up because it allows more space for context and branding. The best approach is a multi-channel sequence: SMS first, email second, final SMS third.

Some industries benefit from WhatsApp Business messages too, particularly if you serve demographics that prefer messaging apps. The beauty of automation is you can test different channel combinations and measure which gets the best response rate for your specific customer base.

Templates That Actually Get Reviews

The key to high-converting review request messages is brevity, personalisation, and a direct link. Here are templates that work well for Australian tradies and service businesses:

SMS Template (Post-Service)

“Hey [Name], [Tech] here from [Business]. Hope you’re happy with the [service type] today! Would mean a lot if you could leave us a quick Google review: [link]. Thanks legend!”

Email Template (Follow-Up)

Subject: How did we go, [Name]?

“Hi [Name], just checking in after your [service type] on [date]. We hope everything’s working perfectly. If you have 30 seconds, we’d really appreciate a Google review—it helps other [city] locals find us. [Button: Leave a Review]. Thanks for supporting a local Aussie business!”

Final Nudge Template

“Hi [Name], last message from us! If you had a good experience with [Business], a quick Google review would mean the world: [link]. No worries if not—we appreciate your business either way. Cheers!”

Handling Negative Reviews With AI

One concern businesses have about automating review requests is the fear of negative reviews. Here’s the thing: negative reviews will come regardless. What matters is how you handle them. AI can help in several ways:

Sentiment pre-screening: Before sending a review request, you can include a quick satisfaction check. If the customer indicates they’re unhappy, the automation routes them to a private feedback form instead of Google. This lets you address issues before they become public reviews.

AI-drafted responses: When negative reviews do appear, AI tools like ChatGPT can draft thoughtful, professional responses in seconds. The key is to acknowledge the concern, apologise where appropriate, and offer to resolve the issue offline. A well-handled negative review can actually build more trust than five generic positive ones.

Alert notifications: Set up automated alerts so you’re notified instantly when a new review appears, particularly anything under 4 stars. Speed of response matters—responding within a few hours shows you care.

For a deeper dive into AI tools for trade businesses, check out our complete AI for tradies guide.

Connecting to Job Management Tools

The automation only works if it’s connected to your existing systems. Here are the most common integrations for Australian businesses:

• ServiceM8: Webhooks fire when jobs are marked complete. Make.com catches these and triggers the review sequence.
• Tradify: Use Tradify’s API to detect completed jobs and pass customer details to your automation.
• Jobber: Native integrations with Zapier and Make.com allow seamless triggers on job completion.
• Cliniko: For healthcare practices, trigger review requests after appointments using Cliniko’s webhooks.
• HubSpot/Zoho CRM: For professional services, trigger sequences based on deal stage changes or invoice payments.

The setup typically takes 2–4 hours and runs indefinitely once configured. No ongoing maintenance required unless you change job management systems.

Measuring the Impact of Automated Review Requests

Once your automation is running, track these metrics monthly:

• Review volume: How many new reviews per month compared to before automation?
• Average rating: Has your overall rating improved? (It usually does because the sentiment filter catches unhappy customers first.)
• Response rate: What percentage of customers who receive a request actually leave a review? Aim for 15–25%.
• Local search rankings: Monitor your Google Business Profile rankings for key service terms.
• Lead volume: Track whether increased reviews correlate with more enquiries and calls.

Most businesses see a 200–400% increase in monthly review volume within the first 60 days of implementing automated sequences. The compound effect over 6–12 months is transformative for local SEO.

Getting Started With Review Automation

If you’re ready to put your review collection on autopilot, start by identifying your trigger point (job completion, appointment end, invoice payment) and your primary communication channel (SMS is usually best). Then connect the pieces using Make.com or a similar automation platform.

Need help setting it up? Our workflow automation service includes done-for-you review automation as part of our standard implementation for Australian businesses.

Frequently Asked Questions

Is it legal to automate Google review requests in Australia?

Yes, as long as you’re asking real customers for genuine reviews. You cannot offer incentives for reviews (that violates Google’s policies), and you must comply with the Spam Act 2003 for electronic messages. Always include an unsubscribe option in emails and only message customers who have a legitimate business relationship with you.

How many reviews should I aim for per month?

Consistency matters more than volume. For most local businesses, 5–15 new reviews per month is excellent. Google values a steady flow of recent reviews over a large total that stopped growing months ago.

What if a customer leaves a negative review through the automated sequence?

With sentiment pre-screening built into your automation, most unhappy customers are redirected to private feedback. For the occasional negative review that does come through, respond professionally within 24 hours. AI can help draft appropriate responses quickly.

Which SMS platform works best in Australia?

MessageMedia, Burst SMS, and Twilio all work well for Australian businesses. Make.com integrates with all three. Choose based on pricing (Burst SMS is often cheapest for low volumes) and whether you need a dedicated sender ID.

Can I automate review responses as well as requests?

Yes. AI can draft review responses that you approve before posting. Many businesses set up a workflow where new reviews trigger an AI-drafted response sent to the business owner for quick approval, then posted automatically once approved.

How long does it take to set up automated review requests?

A basic sequence (trigger + SMS + follow-up email) can be set up in 2–3 hours. A more sophisticated system with sentiment screening, multi-channel sequences, and reporting takes 4–6 hours. Once running, it requires minimal maintenance.

Short Answer: How Do I Set Up a Make.com Automation?

To set up a Make.com automation, create a free account, build a new “scenario” (Make’s term for a workflow), add a trigger module to start the workflow, connect action modules that do something with the data, test with real data, and turn on scheduling. The whole process takes 15-30 minutes for a basic automation.

What Is Make.com?

Make.com (formerly Integromat) is a visual automation platform that connects your apps and services to create automated workflows. Think of it as a digital assistant that watches for events in one app and automatically performs actions in other apps — without you lifting a finger.

Unlike Zapier’s linear step-by-step approach, Make.com uses a visual canvas where you can see your entire workflow laid out as a flowchart. This makes it easier to build complex automations with branching logic, parallel paths, and error handling. And importantly for Australian small businesses, it’s significantly cheaper than Zapier at any meaningful scale.

This guide walks you through everything from creating your account to building your first real-world automation. By the end, you’ll have a working workflow that captures form submissions, sends notification emails, and adds contacts to a CRM — all automatically.

Step 1: Create Your Make.com Account

Head to make.com and sign up for a free account. The free tier gives you:

• 1,000 operations per month
• 2 active scenarios
• Minimum interval of 15 minutes between runs
• 100 MB of data transfer

That’s enough to build and test several automations before deciding whether to upgrade. No credit card required.

Once you’ve verified your email and logged in, you’ll land on the Dashboard. This shows your active scenarios, operation usage, and recent activity. Take a moment to look around, then click “Create a new scenario” to get started.

Step 2: Understand the Canvas

Make.com’s canvas is where the magic happens. Here’s the terminology you need to know:

• Scenario: A complete workflow (equivalent to a Zapier “zap”)
• Module: A single step in your workflow — either a trigger, action, search, or transformer
• Trigger: The first module in a scenario — it watches for an event (new form submission, new email, new row in a spreadsheet)
• Action: A module that does something — sends an email, creates a record, updates a spreadsheet
• Connection: Your authentication credentials for each app (saved securely and reusable across scenarios)
• Bundle: A package of data flowing through your scenario (like a single form submission with all its fields)

When you open a new scenario, you’ll see an empty canvas with a single “+” button in the centre. That’s where you’ll add your first module.

Step 3: Add a Trigger Module

Every scenario starts with a trigger — the event that kicks off the automation. Click the “+” button and search for the app you want to use as your trigger.

For this tutorial, let’s use Google Forms as our trigger. Here’s the process:

1. Click the “+” button on the canvas
2. Search for “Google Forms” in the app list
3. Select “Watch Responses” as the trigger type
4. Click “Create a connection” to authenticate with your Google account
5. Select the specific form you want to monitor
6. Click “OK” to save the module configuration

Make.com will now watch for new submissions on your selected Google Form. Every time someone submits the form, it triggers the scenario and passes the form data to the next module.

Pro tip: After setting up the trigger, click “Run once” in the bottom toolbar. Make will pull in a recent form submission as sample data, which you’ll need for configuring subsequent modules.

Step 4: Add Action Modules

Now let’s add what happens after the form is submitted. Hover over the trigger module and click the small “+” that appears on its right side to add the next module.

Action 1: Send a Notification Email

1. Search for “Email” and select the built-in email module (or Gmail if you prefer)
2. Select “Send an email”
3. Configure the fields:
   • To: Your notification email address
   • Subject: “New form submission from [map the name field from the trigger]”
   • Content: Map the relevant form fields into a formatted email body
4. Click “OK” to save

Notice how you can “map” data from the trigger into your email fields. When you click into the Subject or Content field, you’ll see a panel showing all available data from previous modules. Click a field to insert it — Make handles the dynamic data substitution automatically.

Action 2: Add Contact to CRM

1. Click the “+” after the email module
2. Search for your CRM (HubSpot, Pipedrive, or whatever you use)
3. Select “Create a Contact” or “Create/Update a Contact”
4. Map form fields to CRM fields (name, email, phone, etc.)
5. Click “OK”

Your scenario now has three modules: Google Forms trigger, email notification, and CRM contact creation. The canvas shows them connected in a line, with data flowing from left to right.

Step 5: Filters — Adding Conditional Logic

Filters let you control which data passes through your scenario. They sit between modules and act as gates — data that meets the filter condition passes through; data that doesn’t is stopped.

To add a filter:

1. Click on the line connecting two modules (the dotted connection line)
2. A filter panel appears where you can set conditions
3. Set your condition — for example, “Email contains @gmail.com” or “Budget is greater than 5000”
4. Click “OK”

Filters are invaluable for preventing junk data from cluttering your CRM or triggering unnecessary emails. Common filter uses include filtering out test submissions, routing based on inquiry type, or only processing submissions from specific regions.

Step 6: Routers — Branching Your Workflow

Routers are one of Make.com’s killer features — they let you split your workflow into multiple paths that run in parallel. This is something Zapier charges premium prices for (Paths) and limits heavily.

To add a router:

1. Right-click on the canvas and select “Add a router”, or add it as a module
2. The router creates multiple branches — each branch can have its own filter and action modules
3. Add filters to each branch to control which data goes where

For example, you might route form submissions based on inquiry type:

• Branch 1: If inquiry type = “Sales” → send to sales team email and create opportunity in CRM
• Branch 2: If inquiry type = “Support” → create ticket in support system and send auto-reply
• Branch 3: If inquiry type = “Partnership” → send to partnerships email and add to partner pipeline

All branches run in parallel, so the scenario processes quickly regardless of how many paths you have. Learn more about advanced Make.com features in our Make.com vs Zapier vs n8n comparison.

Step 7: Error Handling

Things go wrong. APIs time out, rate limits get hit, and occasionally an app just has a bad day. Make.com’s error handling ensures your automations are resilient:

Break Directive

Pauses the scenario and saves the unprocessed bundle for later retry. Use this for transient errors (API timeouts, rate limits) where retrying later will likely succeed.

Retry Directive

Automatically retries the failed module a specified number of times with a delay between attempts. Perfect for rate limit errors.

Rollback Directive

Stops the scenario execution and marks it as an error. Use this for data integrity scenarios where partial completion would cause problems.

Ignore Directive

Ignores the error and continues the scenario. Use sparingly — only for non-critical actions where failure is acceptable (like sending a notification that isn’t essential).

To add error handling, right-click on any module and select “Add error handler”. Choose the appropriate directive and configure the behaviour.

Step 8: Scheduling

Once your scenario is tested and working, you need to set the schedule — how often Make.com checks for new trigger events:

• Free plan: Minimum 15-minute intervals
• Paid plans: As frequent as every 1 minute
• Instant triggers: Some apps support webhooks, which trigger the scenario immediately when an event occurs (no polling delay)

To set the schedule, click the clock icon on the trigger module and choose your interval. For most business automations, 15-minute intervals are perfectly adequate — instant triggers are available for time-sensitive workflows.

Toggle the scenario to “ON” using the switch in the bottom toolbar, and your automation is live.

Real Example: Form to Email to CRM

Let’s put it all together with a complete, working example that you can build in under 30 minutes:

The Scenario

When someone submits a contact form on your website, automatically: send yourself a notification email, send the lead an auto-reply, and create a contact in your CRM with all their details.

The Modules

1. Trigger: Google Forms → Watch Responses (or Webhook if your form supports it)
2. Action 1: Email → Send yourself a notification with the submission details
3. Action 2: Email → Send the lead an auto-reply thanking them for their inquiry
4. Action 3: HubSpot/Pipedrive → Create a new contact with mapped fields (name, email, phone, message)

Total Setup Time

About 20-25 minutes including connection setup, field mapping, and testing. Once running, this automation handles your lead capture 24/7 without any manual intervention.

For a deeper comparison of automation platforms, check our tool comparison resource, or explore our workflow automation services if you want hands-on help building more complex scenarios.

Frequently Asked Questions

How many operations does a typical scenario use?

Each module that processes data counts as one operation. A three-module scenario (trigger + two actions) processing one form submission uses three operations. The free plan’s 1,000 operations per month is enough for about 330 three-step automations — plenty for testing and light production use.

What happens if my scenario fails?

Make.com logs all scenario runs with detailed execution history. Failed runs show exactly which module failed and why. You can view the error, fix the issue, and rerun the failed bundle without losing data. Incomplete executions are stored for 15 days on paid plans.

Can I use Make.com with Australian-specific apps like Xero?

Yes. Make.com has a native Xero module with support for invoices, contacts, bank transactions, and more. It also integrates with other Australian-popular apps like MYOB (via API), Deputy, and various Australian payment processors.

What’s the difference between Make.com and Zapier for beginners?

Zapier is slightly easier for absolute beginners because of its linear step-by-step interface. Make.com has a slightly steeper initial learning curve because of the visual canvas, but becomes easier than Zapier once you need conditional logic, branching, or complex data transformations. Most users become comfortable with Make.com within their first or second scenario.

Should I start with the free plan or paid?

Start with free. Build your first two or three scenarios, confirm everything works, and only upgrade when you need more operations, faster scheduling, or more than two active scenarios. Most small businesses eventually land on the Core or Pro plan, which starts at around US$10-19/month.

Next Steps

You now know enough to build your first Make.com automation. Start with a simple three-module scenario, get it working, and then explore routers and error handling as your confidence grows. The Make.com canvas becomes intuitive surprisingly quickly.

If you’d like help building more complex automations or want someone to set up your workflows professionally, explore our Make.com automation services. We help Australian businesses build reliable, scalable automations that run themselves.