When Nate Herk first started posting his AI agent builds on YouTube, he wasn’t trying to build a business or learn how to sell AI agents. He was experimenting, documenting small projects, and seeing what kind of automations could actually make people’s lives easier. But one viewer emailed him asking for a custom version of what he’d just built. That single message marked the beginning of a new company and a very real revenue stream.
Over a few months, Nate and his partners Milan and Tyler turned those casual builds into paid products under the name True Horizon AI. They weren’t chasing big corporate contracts or over-engineering systems that required huge cloud infrastructure. Instead, they focused on one simple question: what small, time-consuming tasks can AI take off someone’s plate right now?
From Demo Videos to Paying Clients
The first project Nate sold was a personalised outreach writer. The idea was almost too simple: feed it a list of contacts, let the agent research each person, and then generate customised messages for each one. It didn’t even send the emails, it just created them. But for one small business owner spending a few hours every week writing cold outreach, that was enough. Nate charged $1,650 for the build.
The client could see exactly how much time they’d get back each month and at roughly $50 an hour, saving a few hours each week easily justified the cost. That kind of immediate return is what makes small AI builds compelling for businesses who aren’t ready to dive into complex enterprise software.
The project also showed Nate the power of sharing work publicly, he hadn’t advertised any services, he just added an email to his YouTube description.
Turning Repetition Into Opportunity
The next few projects followed a similar pattern, a sales quoting agent that handled customer enquiries and logged information into a CRM system sold for around $4,000. It saved hours of admin work, reduced human error, and sped up the entire response process.
Then came an internal Slack assistant for a small team. It lived inside the company’s Slack workspace and acted as a quick-access helper for common questions and internal data. It was simple, friendly, and saved the team from constantly switching tabs or digging through files, that build went for around $6,000.
Finally, there was the most ambitious one yet: an AI concierge. It helped onboard new members, manage guest passes, and even handled event information for a membership-based business. It remembered previous conversations, worked across multiple users, and essentially functioned as a digital front desk. That single project brought in about $12,000.
By the time these four builds were complete, True Horizon AI had made roughly $23,000 in total but the real success wasn’t the number, it was the process that emerged. They had stumbled upon a repeatable way to turn simple automations into tangible business tools.
Building What People Understand
Many people assume that selling AI products means building complex systems with deep learning models and research-grade infrastructure. Nate’s approach was the opposite. Each project relied on straightforward components: a workflow builder to orchestrate steps, an API connection to a language model, and a vector store to hold useful information.
For anyone unfamiliar, a vector store is like a smart database that helps the AI remember context. Instead of just searching for keywords, it stores pieces of information in numerical form so the system can find related ideas based on meaning, not just matching words. It’s what allows an assistant to recall company policies or summarise conversations intelligently.
By using simple, accessible tools, Nate kept development quick and margins high. Clients didn’t care what was running underneath; they just cared that it worked and saved them time.
Pricing Around Value, Not Effort
In the early days, pricing was guesswork. But over time, the team learned to price builds based on the value they created, not the hours it took to make them. If a client was saving hundreds or even thousands of dollars a month, it didn’t make sense to charge a few hundred for the setup.
They started using a basic formula: estimate the hours saved per week, multiply by the client’s hourly rate, then show what that looked like over a year. Once the client could see the annual savings, the price of the build felt small in comparison.
This approach turned True Horizon AI from a “developer for hire” setup into a consultancy that sold clear outcomes. They weren’t pitching bots, they were selling efficiency, scale, and freedom from repetitive work.
Learning to Scale Without Breaking
As more clients came in, Nate and his partners realised that success brought its own challenges. The CTO found himself too deep in day-to-day builds instead of managing architecture and team structure, they started introducing standard operating procedures, quality assurance stages, and account management roles to create a more professional experience for clients.
Every project included a week of internal testing followed by a week of client testing before launch. They began capturing baseline data to show the impact of each agent, creating simple case studies to share with future prospects, those details made it easier to close new deals and to raise prices without pushback.
What This Means for AI Builders
Nate’s story shows how accessible this kind of work has become. You don’t need to be a machine learning researcher or a Silicon Valley startup to make real money from AI tools, you just need to understand a problem deeply and build something that solves it cleanly.
The best opportunities aren’t always in the glamorous areas like autonomous research or multimodal generation. They’re in the quiet, boring corners of business where someone is losing hours each week to admin, customer support, or repetitive messaging. Those problems are everywhere, and small, well-scoped agents can make an immediate difference.
For founders, freelancers, and small studios exploring AI, the takeaway is simple, start small, charge for value, and package your work like a product. You don’t need a hundred clients, four solid ones, like Nate’s, can prove the model, fund the next build, and give you enough credibility to grow from there. We have seen lot of great stories of the potential business uses for AI such as Deepak Singla, as well as the importance of AI in SEO, showing that there is no better time than now to start a business in the field, with the likes of n8n making it easier than ever.
