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28 contributions to AI Automation Agency Hub
The part I built first, and nobody will ever see it
After my first post, your comments surprised me. I expected questions about the AI. Instead, you asked the hard ones: What about patient privacy? What if two people book the same slot? What happens when the bot doesn't understand? Those are exactly the questions I asked myself before writing a single line of chat. That's why the bot was not the first thing I built. For the first weeks, I worked on parts no patient will ever see. Here's why. A clinic front desk runs on trust. A patient forgives a slow reply. They don't forgive arriving at the clinic to find someone else in their slot. And a clinic owner will never forgive their patients' data reaching the wrong eyes. One mistake like that, and even the smartest bot is finished. So before building the chat, I set 4 rules the system can never break: 1️⃣ Walls between clinics. One clinic can never see another clinic's data. This is enforced at the deepest layer of the system, not just by a line of code someone might forget one day. 2️⃣ One slot = one patient. If two people tap the same time at the same second, only one gets it. The other is instantly offered the next free times. No "we'll sort it out at the desk." 3️⃣ Everything leaves a trace. Every booking, change, cancellation and check-in is recorded: what happened, and who did it, and when. When a patient says "I never cancelled," the clinic has a record, not an argument. 4️⃣ The bot is not allowed to guess. Emergencies skip everything and go straight to a human. If it still can't understand after a couple of tries, it hands over to staff. It doesn't improvise. The lesson I keep re-learning: In healthcare, the boring part is the product. The chat is just the door. And a real thank-you 🙏 Your comments on my first post pointed at a few gaps I had missed in how the bot hands conversations over to staff. I fixed them this week, and I'll share exactly what changed in my next post. Now I need your help: If you've built anything where one mistake costs trust (health, finance, legal…), what's the one "invisible" piece you'd build first?
2 likes • 1d
One piece I'd add before the pilot: decide what the system does when a check itself fails. If the slot lock or the privacy check can't run, the default should be to do nothing and hand it to staff, never a best guess. Second, messages that mix two kinds of request. On a client's website assistant we built, one text asked for a simple wording edit and a payment change at once. Instead of blocking the whole message or letting the bot touch billing, we split it. Patients will do the same: a reschedule and a billing question in one text. Then break it on purpose. Kill the database mid-booking and read what the patient gets told.
The legal stuff
I've noticed that in most commuities nobody is talking about the legal stuff. I mean how do you handle contracts? Do you sign contracts with your clients? What about international clients?
2 likes • 2d
Yes, every build I do gets a signed agreement, even small ones. A few clauses mattered more than I expected: Scope as a named list the client freezes by a date, not 'up to three dashboards'. Fights come from items nobody wrote down. A list of costs the client pays. Hosting, database, phone numbers, AI usage: every account in the client's company name, on their card. You pay for your own tools. An ownership split. The client owns what is specific to their product once paid. Reusable pieces you bring stay yours, with a permanent license for them to use. A short free bug-fix window after launch with an end date. After that, support is its own agreement. For international clients, have a lawyer check governing law. That one I can't answer.
It Worked. So Why Did It Fail?
Here's something I've been thinking about lately… An AI workflow runs successfully. No errors. The client gets the output. So why might the automation still be costing them more time than it saves? I came across G2's 2026 AI Agents report, and one finding caught my attention: 45% of surveyed B2B buyers said reviewing and correcting agent work was eating up more than a quarter of their expected productivity gains. That got me thinking about what we actually measure when we build automations. We check whether the workflow ran. But do we check how much of the output needed fixing? How many retries it took? How much human time was still involved? A green execution status doesn't necessarily mean a good result. One metric I'd like to see more builders track is cost per usable result, not just cost per run. When you build automations for clients, do you measure the quality of the final output?
2 likes • 2d
One way to get that number without asking anyone: keep the draft the agent produced, compare it with what actually went out, and mark each one approved as-is, edited, or thrown away. Then split it by kind of task. When I did that on my own reply drafts, routine acknowledgments went out unedited 85% of the time. Anything touching price or scope stayed under half. Same agent. Very different review cost. That split also tells you where to give it more rope. Only the routine bucket earned auto-send. Where do your clients' outputs land, somewhere you could capture the final version?
👋 Need advice on landing my first AI automation client!
Hey everyone! I'm working on building an AI automation service for roofing contractors in the US 🇺🇸 💡 My offer idea: - 📲 Automatically text back missed callers - 🔄 Follow up with potential customers who haven't responded to an estimate - 📅 Help contractors avoid losing potential jobs because of poor follow-up I'm confident in my ability to build the systems, but I'm struggling with one thing: How do I actually start conversations with potential clients? 😅 I'd love to hear from people who've successfully landed clients through cold outreach. A few questions: 🔹 Where do you find prospects? email, LinkedIn, Instagram, Facebook groups, or somewhere else? 🔹 How do you start a conversation without sounding too salesy? 🔹 If you were starting from zero with no case studies or testimonials, what would your first steps be? 🎯 I'm looking for practical advice from people who've actually done it, not just theory. Any advice would be genuinely appreciated. Thank you! 🙌
2 likes • 3d
Start local and in person. Roofers trust someone they can meet, and supply houses, home shows and morning networking groups are full of them. Skip the pitch and ask one question: how many calls went to voicemail last week? Most owners don't know, and that number is your opening. Before you have case studies, run the test yourself: call after hours, fill in their web form and time the reply, and read their Google reviews for "never called back." Show the owner what you found. That's a free audit, not a sales call. I run a small directory of vetted home pros in Greenville, and a slow reply is usually where a homeowner gives up and moves to the next name.
0 likes • 2d
@Izhan Sheikh Yes, that works from anywhere. Call each roofer after hours from a US number and note what happens, then read their Google reviews for 'never called back'. Send the owner only what you found, two or three lines, with no offer attached. The finding starts the conversation.
From Building AI Solutions to Building an AI Business
Hey everyone! 👋 I'm a software developer and Generative AI developer, and lately, I've been thinking a lot about how to turn technical skills into real business opportunities. With AI agents, workflow automation, and custom AI solutions becoming more powerful, I believe there's a huge opportunity to help businesses solve real problems. But here's something I'm curious about: For those of you working in the AI space, what's your biggest focus right now? 🔹 Building AI agents and automation systems? 🔹 Offering AI services to businesses? 🔹 Building your own AI SaaS product? 🔹 Finding clients and growing an agency? 🔹 Looking for partners to collaborate on projects? I'd love to connect with people who are serious about building something meaningful in the AI space. I'm particularly interested in AI agents, business automation, and collaborating on practical projects that create real value for clients. Let's share ideas, experiences, and opportunities. Who knows? We might find some great people to build with! 🚀
0 likes • 3d
Offering AI services to local service businesses, and mostly the unglamorous half: keeping the automations working after launch. Clients rarely remember the build. They remember whether the texts still go out three months later. Coming from software, the habits you already have (logging, alerts, a test before each change) are worth more here than any new tool. Which of the five are you leaning toward?
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David He
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303 points to level up
@david-he-9791
Chief AI Officer at NexAI Advisors & Ex-Capital One Tech Lead. We build production voice agents, lead-response systems & full-stack AI apps for businesses.

Active 23h ago
Joined Mar 5, 2024
Greenville, SC
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