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AI Event Reminder Agent
To improve event attendance rates, I built an automated ai voice agent using vapi ai agent and make automation. This system monitors a calendar and triggers a personalized, human-like call to participants before the event starts. Built on a sophisticated make com scenario, the automation handles time-zone logic and rescheduling requests. This project highlights my expertise in make.com and n8n, delivering a high-ROI workflow automation that functions as a proactive ai receptionist for busy professionals.
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Connect with clients, get discovered, and run your business 100% commission-free
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Most AI agents are unnecessarily expensive and the problem is usually architecture, not the model.
One of the biggest pain points when building AI agents is LLM cost. Many teams use high-end models like Claude Sonnet or GPT-5 for everything, even tasks that don’t need that level of reasoning. That quickly becomes unsustainable at scale.
The reality is: not every task needs a premium model. Models like Qwen 3.x can cost up to 10x less and still handle tasks like intent classification, basic responses, structured extraction, and routing decisions with similar efficiency.
A well-designed AI agent isn’t just an LLM wrapper it’s a layered system. Routing decides which model to use, context (RAG/memory) reduces tokens, tools handle logic, and execution orchestrates everything. The LLM is just one component.
The winning strategy is simple: use the right model for the right job.
If your AI agent is expensive, it’s probably an architecture problem.
How different are AI node-based tools from each other?
ElevenLabs just launched Flows, a node-based canvas, so I tested it by creating a 30s product ad with narration.
I’ve always liked working with node-based tools, so it’s nice to have another option in the mix.
So far, it felt pretty straightforward, especially if you’re already familiar with this kind of workflow. Getting started was easy, and having voice, sfx, and music powered by ElevenLabs is a big plus.
I also like that the text-to-speech feature has a large voice library to choose from, and that being able to add audio tags helps improve the accuracy of the narration’s tone.
Overall, I think Flows is useful for projects that need strong audio integration, worth trying if that’s your use case.
Watch my process details in the comments 👇
Turn sound on 🔉
It’s a common misconception that automation is a "set it and forget it" kind of thing. Over the past few days, I’ve been deep in the setup for a large-scale online event where the data flow between Typeform, OneDrive (Excel), and Slack felt more like an obstacle course at first.
What I often see in these projects is a massive manual workload happening behind the scenes: attendee data being exported, analyzed, and shoved into spreadsheets by hand, while the team on Slack doesn't have the info they need when they need it.
This doesn't just eat up time; it drains the mental capacity needed for MMAs (Money Making Activities). You really just want a system that handles the logic while you focus on managing the exceptions.
My solution: The data flows automatically now, but I’ve integrated an AI step that categorizes responses before they even hit the Slack Canvas.
Building this took several hours and a few feedback loops with Grok and ChatGPT, but the result is that manual operations were cut down to just a few minutes, replaced by occasional maintenance.
Of course, it’s not "set and forget." If the form changes or a new event comes up, I need to update the Zaps and maybe tweak the AI’s categorization. But that’s qualified maintenance instead of repetitive copy-pasting.
One side effect that clients value most: This shifts the process from constant human dependency to sporadic system maintenance. The workflow no longer stalls if someone is away—the system just keeps running.
Watching the logic chain verify and file real data correctly makes you realize just how much brain capacity was tied up in the old process.
If you’re building an event setup right now and feel like your tools are clashing instead of flowing: Let me know in the comments what your biggest workflow time-sink is.
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