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6d •
Welcome! Introduce yourself + share what you're building 👋
Welcome to the AI Automation Agency Hub. You're in the right place. This community is run by the team at Morningside. We build AI systems for real companies every day, and everything in here comes from that work. Beginners have gone from "I know nothing about AI" to their first paying client in this group, and the whole point of this place is that you're next. Two things to do right now 👇 Introduce yourself below. Drop a comment with: • Where in the world you're from • What you're building toward (first client? career change? scaling an agency?) • One thing you're stuck on right now Then head to the Classroom → Start Here. It takes 10 minutes, and you'll know exactly what to do first. While you're in the comments, read a few intros. If someone's story sounds like yours, tell them. Some of the best client work in this community started with two members saying hi. See you inside, The Morningside team.
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9d •
One Question. What would you ask?
Hey, quick one. What's the one thing you'd ask if you had an experienced AI consultant for 30 minutes? Pricing your first offer? Which niche? What to say when they go quiet? Whether your build is even worth charging for? Whatever it is, it's probably been sitting there a while. Now that question has an answer, but not a generic one. The right answer depends on what you're selling, who you're selling it to, and where you actually are right now. So here's how your call will go. We look at the exact situation you're in, work out what's actually standing between you and your next client, and map out the steps to get there. You leave with a clear roadmap. Then it's just a question of how fast you want to walk it, and whether you want to do that on your own or with people who've walked it a few hundred times. [BOOK MY CONSULTING CALL!] fyi - this is what we have done over the last few years for our members. - $18M+ in client generated revenue. - $27k+ average revenue per client. - 67 days average to first client. Jamie.
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15d •
the complete AI workshop deck (60+ slides), yours to keep
Over the past two years we've sold tens of thousands of dollars' worth of AI workshops at Morningside. They're the easiest way we've found to get in the door with a larger company. You're not pitching. You're in the room, teaching their exec team, building trust before anyone talks about a project. 75%+ of the organisations we run these with end up bringing us in on AI adoption work afterwards. Last Friday I ran a session inside the Accelerator breaking the whole thing down: why companies buy workshops, how we deliver them, and what's on every one of our 60+ slides. The agencies in there loved it, so I filmed a version for you too. I walk through the deck slide by slide. At the end you can grab the full Executive Workshop template, yours to keep. Same deck we use to run workshops companies pay thousands for. If you're working with larger organisations, or you want to see how we get executive teams to actually move on AI, this one's for you. → Watch the video and get the template Talk soon. Josh.
2h •
When Should You Use an LLM in an Automation Workflow—and When Should You Avoid It?
I've been thinking about something while learning AI automation: do we actually need an LLM for every step of a workflow, or are we sometimes making things more complicated than they need to be? For example, in a lead qualification workflow, you could use AI to analyze incoming messages, but do you really need an LLM to score a lead based on a few predefined rules? A few things I'm curious about: - When would you use an LLM instead of traditional conditional logic? - Would you use an LLM to classify incoming messages and then handle the rest with deterministic logic? - How do you decide whether a task needs a more capable model or whether a smaller, cheaper model is enough? - How do you balance API costs, response time, and output accuracy? - At what point do validation, retries, and fallback logic become necessary? My thinking is that a good automation isn't necessarily the one with the most AI in it, but the one that uses AI where it actually adds value. How do you approach this when building production workflows in n8n or similar platforms?
11h •
3 calls in 90 days to 20 qualified calls in 30 days. What we changed for a lending CEO.
John runs a funding company. Before we started, he had booked 3 appointments in the previous 90 days, all from referrals. His offer was fine. His calendar was the problem. Week 1 was setup. Week 2 the first qualified replies came in. Week 3 the first booking landed. What we changed: 1) One platform, one message. LinkedIn only, one opener, and we did not rewrite it for 30 days. Every rewrite resets the data. 2) A fixed follow-up ladder. Day 1, 2, 3, 4, then 6, 8, 10, 12. Most of his bookings landed after follow-up 5, not on the first message. 3) No calendar link in the first message. Once someone showed interest we offered two specific times in their timezone. The link went to people who had already said yes. Result: 20 qualified calls in 30 days, from a list he already had access to. One client, so I am not claiming this holds everywhere. The pattern I keep seeing is that the leak is in the follow-up count, not the offer. How many touches does your sequence send before you drop someone?
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