Key Takeaways

  • Melisa Tokmak's company, Netic, doesn't just sell AI tools to businesses; it builds AI that runs core, revenue-generating operations for large, essential service businesses like HVAC and plumbing.
  • Netic's AI acts as the primary interface between the service company and its customers, handling everything from initial customer need assessment to matching it with complex operational rules and deploying labor.
  • This isn't a simple chatbot; Netic's AI navigates complex factors like customer unit types, urgency, and even customer lifetime value to make decisions, a process that previously required “hundreds and hundreds of people.”
  • Over 70% of customers interacting with these service businesses now have their first interaction with Netic's AI agents, showcasing rapid and deep AI adoption in industries often considered low-tech.

The Invisible AI That Keeps the World Running

Forget the hype cycles and vague promises of “AI transformation.” Melisa Tokmak, founder and CEO of Netic, is doing something far more concrete: her company is building the invisible AI agents that run the backend of essential service businesses. We're talking HVAC, plumbing, even pet care and consumer wellness—the industries that literally keep our homes and lives functioning. Tokmak cuts right to it: “Netic builds AI to run millions of real world businesses that keep the world running. That means basically we work with large enterprises in essential services.”

These aren't sexy tech startups; they're the bedrock of the economy, and until now, their customer interactions were a bottleneck. Imagine a burst pipe at 2 AM or a furnace dying in winter. The immediate need for support is immense, but the operational complexity to resolve it is often handled by hundreds of human agents. Tokmak's insight was to realize that AI could shoulder this burden, not just as a support tool, but as the primary operational engine.

Beyond Chatbots: The AI Agent as Your Front Office

Netic's approach isn't about layering a generic AI chatbot onto an existing call center. It’s a complete re-architecture of the customer-facing side of these businesses. Tokmak explains, “So netic exists between the company and its customers. So every single thing to understand the customer need or want and match that with how can we even help that customer with the operational rules of the business and even deploy the services or the labor all happens on netic.” This means the AI doesn't just answer questions; it understands the customer's problem, knows the company's service offerings and internal rules, and then dispatches the right human or initiates the correct workflow. It's a full-stack automated front office.

The critical distinction here is the depth of decision-making. As Tokmak notes, “The operational needs are very complex. It's not as simple as oh Eli's heat broke and now Melissa goes is it that actually what kind of even units do you have? What kind of needs do you have? Can we come to you? Is it something that we need to come to you today or tomorrow? What is your lifetime value as a customer?” This is where Netic differentiates itself: the AI is trained on the nuanced, specific operational rules of each business, allowing it to act with the judgment of an experienced human agent. The proof is in the numbers: “Over 70% of our customers are AI first. We call it netic first. They go N1. Uh and over 70% actually all of their customers first interaction with the company is with NetIC agents.” That's not a marginal improvement; that's a wholesale shift in how these companies operate, freeing up human staff to tackle the truly exceptional cases.

What to Do With This

Stop thinking about AI as a bolt-on feature. Instead, pick one core, high-volume, repetitive operational loop in your business that currently requires human judgment to connect customer need to service delivery. This isn't your internal IT help desk; it's a process directly linked to revenue or churn. Document every single decision point in that loop, including the complex variables like customer history or urgency that Melisa Tokmak mentioned. Then, ask yourself: Can I build or train an AI agent to handle 80% of these scenarios autonomously, making the actual dispatch or resolution decisions, not just providing information? Don't automate a fragment; aim to automate the entire, complex interaction from first touch to action taken. This week, pick that one process and map out the data and decision trees an AI would need.