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Creatives on Contra have earned over $150M and we are just getting started
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AI-Powered Client Enquiry Automation
Project Overview:
I built an AI-powered client enquiry automation workflow using Zapier, Google Forms, Google Sheets, AI by Zapier, Gmail, and Google Calendar.
The goal was to automate the initial handling of client enquiries from receiving the enquiry to understanding the client's needs, sending an acknowledgement, and creating a follow-up task.
The Problem:
Managing client enquiries manually can be repetitive and time-consuming. Each new enquiry needs to be reviewed, categorized, summarized, responded to, and followed up.
Without a structured process, it's easy for enquiries to be delayed or follow-ups to be missed.
The Solution:
I designed a workflow that automates these repetitive administrative tasks while using AI to analyze the information provided by the client.
The automation follows this process:
Client submits enquiry
↓
Google Sheets captures the enquiry
↓
AI analyzes the enquiry
↓
Gmail sends an acknowledgement
↓
Google Calendar creates a follow-up
How It Works:
1. Enquiry Collection.
The client submits their details and enquiry through a Google Form. The information is automatically recorded in Google Sheets.
2. AI Analysis.
AI by Zapier reviews the enquiry and extracts key information such as the service category, priority, concise summary of the client's needs, and recommended next action.
This eliminates the need to manually review every enquiry just to determine what it is about and how it should be handled.
3. Automated Client Response.
After the enquiry is processed, Gmail automatically sends the client an acknowledgement, ensuring they receive a timely response.
4. Follow-Up Management.
The workflow creates a Google Calendar follow-up, helping ensure that the enquiry doesn't get lost after the initial response.
Result:
The completed workflow turns a manual enquiry process into a structured automated system:
Capture → Analyze → Respond → Follow Up
The key improvement is the use of AI as a decision-making layer, rather than simply moving information from one application to another.
This demonstrates how AI automation can reduce repetitive administrative work, improve response consistency, and help businesses stay organized with client enquiries.
Tools Used:
Zapier | AI by Zapier | Google Forms | Google Sheets | Gmail | Google Calendar
Skills Demonstrated:
AI Automation • Workflow Design • Administrative Automation • AI Prompting • Client Enquiry Management • Email Automation • Calendar Automation • Process Optimization
Really clean workflow, Ann. The Capture → Analyze → Respond → Follow Up structure makes the business value immediately clear. One useful next layer could be an exception route for urgent or low-confidence enquiries, so AI handles the routine cases while anything uncertain...
New client site live 🥳
Normally I'm knee-deep in funnels, opt-ins, sales pages, the whole direct-response world.
Good Life Therapy let me bring that same instinct somewhere different.
I revamped the brand, wrote the copy, and built the practitioner profile system from scratch, structured so every counsellor and therapist has a consistent profile that still sounds completely like them... And made the journey of someone looking for a therapist a piece of cake too.
Same conversion instincts I use on a funnel, just aimed at something quieter: making someone feel safe enough to reach out for the first time. Full brand, full copy, full build, and a client who's genuinely proud of what she got.
A workflow can finish successfully and still be wrong.
That is the reliability problem with production AI agents. A green execution only tells you the workflow ran; it does not tell you whether the model had the right context, chose the right tool, or returned a useful answer.
A practical n8n reliability loop:
• constrain inputs, tools, schemas and routing
• tag executions and inspect full agent logs
• run evals whenever prompts, tools or models change
• add real production failures to the test set
• monitor execution, quality, efficiency and safety separately
The best metric is not the one you can collect. It is the one that changes a decision.
Which failure would your current monitoring miss?
