Netic positions itself between large essential-service enterprises and their customers, autonomously managing the entire workflow from understanding customer intent to matching operational constraints and deploying the right worker.
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Melisa Tokmak: 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.
Using an HVAC emergency as a concrete example, Melisa walks through how Netic agents do far more than answer phones: they diagnose the problem, assess the customer's value, and decide which technician to deploy and when.
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Melisa Tokmak: It's actually a lot of the businesses we work with, this is why we started with essential services. The operational needs are very complex. It's not as simple as oh Eli's heat broke and now Melissa goes. It's 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? So should we be deploying the best person who can only work on let's say boilers or new age systems today or later? So it's actually quite complex to first of understand what is the need from the customer can this company service it if so who when and to ensure that it is all optimized to in create customer delight to generate more revenue for the company right.
Melisa explains why she chose to build Netic as a platform rather than pursue the AI-rollup model: rollup products are inherently limited to the acquired companies, whereas Netic aims to be usable by every real-world business, letting them focus on their labor and differentiation while AI handles the rest.
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Melisa Tokmak: What I have seen there's a lot of successful roll-ups and tech enabled roll-ups, but at the end all the products you're building are for the company you just bought. It can't really be actually applied to any other company. So you are committing to buying these a few companies in whatever industries that you are interested in and serving those with your products versus what I'm interested in is how every real world business can run on netic right if we didn't have to limit them if they could focus on what they are good at in that business which is the labor which is the differentiation quality of the service how could netic run the rest.
Melisa argues that labs like OpenAI, Anthropic, and Meta are partners rather than competitors because solving real-world problems requires focus on specific industries, deep operational harnesses and orchestration software, and a product layer — none of which labs prioritize given their generalizable-research orientation.
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Melisa Tokmak: I really respect that. But in terms of looking at what we provide to these industries and companies, I actually think two things are very important. One in focus in what you're building and I will say you know it would be a funny question to these enterprises right open AI builds amazing products really fast but also it kills them really fast so I don't think enterprises or at least enterprises in these industries looking for that really fast. Or in anthropics case you know Silicon Valley converged in the idea that you know they pulled ahead in coding agents because they had focus but you see exactly the opposite in the enterprise case there's about like 20 products like what is really happening. Um and I don't really see that. And a meta focused question maybe about the labs and specifically researchers they really care about solving the most generalizable way of the problem right so in this case maybe looking at the problem we're solving the answer would be well when we get the AGI we'll ask how to solve it for essential services and I think that is both operationally and intellectually a bit lazy thinking. And the finally is for to solve these type of extremely difficult problems with millions in the country that have completely different worries, different accents. How do they want to engage different contexts and also even engage them again to make them multi-time customers. There's quite a bit of lost mile that you really have to do that doesn't only come from models that has to come from your harnesses and orchestration, the software and the product that you have to build on top.
Pushing back on the idea that home services and similar industries are technologically backward, Melisa says some of the most tech-forward business owners she's met are in these sectors. They adopt quickly when value is demonstrated — citing a half-million-dollar contract closed in 14 days — and Netic now uses satellite data and AI context to transform how these companies prospect and serve customers.
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Melisa Tokmak: I think it's a big misconception to think about these industries as old school. Actually some of the most techforward business focused people owners founders I have met have been in these industries. So first of all we work with large enterprises so they're extremely value focused and they have to be tech forward right for example to give you a sense you know like just one of the businesses we closed it can be like a half a million contract and it took from end to end 14 days. And it's not because you know there is you know they I'm not a magic potion and the company is not or just AI is not they're very thoughtful about what they have to do and checking if the value is there right even in their buying behavior.
Melisa observes that PE firms still default to cost-cutting conversations because they haven't seen platforms like Netic that generate net-new revenue. She argues the path forward is showing live deployments with real customer data — including over $600 million generated for Netic's customers — rather than running demos.
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Melisa Tokmak: I will say though it's still they are always the first conversations very focused on cost cutting because I think they don't see a lot of products or platforms like ours. I'm not really there to cut your costs. Sure that is happening in this way but I'm really interested in this is how you're going to make net new revenue. So that is new. You have to actually start that conversation. You have to show them intangible examples because otherwise it still focuses on how do we get to the bottom line and cut some costs, right? But they have to almost expand their horizon on thinking about what else is possible with AI, right? It would be pretty sad if we used AI only for cost cutting.