Building an Autonomous Enterprise for Real-World Services with Netic Founder Melisa Tokmak

Summary

Melisa Tokmak describes Netic as an AI platform for real-world service enterprises—HVAC, plumbing, hospitality, automotive, pet care, and wellness—that sits between companies and customers to handle intake, qualification, scheduling, and dispatch. She emphasizes that the hard problem is not just conversation, but determining whether a business can service a request, who should do it, when it should happen, and how to optimize for both customer delight and revenue. Tokmak says over 70% of Netic customers are now AI-first and over 70% of end-customer first interactions happen with Netic agents, and she claims the company has generated more than $600 million for customers from AI-handled interactions. She also makes the case that robotics is still far off for these industries, that big labs are not the primary competitive threat, and that private equity is increasingly evaluating AI through measured ROI rather than generic cost-cutting narratives.

Notes

• Netic is positioned as AI infrastructure for real-world service businesses, not just a chatbot layer.
• Initial focus is essential services where urgency and variability are high.
• Target verticals include HVAC, plumbing, electric, roofing, wellness, hospitality, automotive, and pet services.
• Netic acts as the intermediary between company and customer.
• The platform handles matching customer needs to operational constraints and labor deployment.
• Netic agents can handle inbound via phone, text, and website scheduling flows.
• Intake is contextual, not generic, and can start with property or account details.
• The core technical problem is decisioning, not just dialogue.
• The orchestration layer decides who should go, when, and under what service constraints.
• Revenue optimization and customer delight are both explicit objectives in the workflow.
• Legacy operations are still staffed by hundreds of people.
• The old model is brittle because support staffing is unreliable and demand spikes are predictable but hard to absorb.
• Heat waves and seasonal cycles create operational surges that break legacy support teams.
• Over 70% of Netic customers are AI-first, or 'Netic first' (N1).
• More than 70% of end-customer first interactions now happen with Netic agents.
• Tokmak's background includes Scale AI business units across government, enterprise, logistics, manufacturing, financial services, and healthcare.
• Her prior experience is framed as exposure to many operationally complex industries.
• The product has to work across multiple layers, not just the model.
• Netic needs to combine model access with orchestration, software, and last-mile productization.
• A key strategic point is that big labs are not the full answer for enterprise deployment.
• Netic's end-state vision is an autonomous enterprise.
• The only non-automated piece in that vision is the actual service labor.
• A representative enterprise contract can be about $500,000 and close in 14 days.
• Enterprise sales are framed as value-validation rather than hype-driven buying.
• Netic's product spans inbound, outbound, analytics, and labor delivery.
• Netic ingests satellite data to reason about neighborhood-level roof risk.
• The satellite context is used to improve both customer conversations and targeting.
• Tokmak claims Netic has generated more than $600 million for customers through AI-handled interactions.
• The company uses real-world customer interaction data as proof of product-market fit.
• Robotics is viewed as a future chapter, not the immediate solution for these industries.
• Variation, dexterity, and human-emotional components keep robotics adoption distant.
• Tokmak highlights agency as a hiring criterion and values long-term company building.
• The company emphasizes craftsmanship and patience over quick exits.
• Tokmak says she prefers founders with ambition, focus, and agency.
• The sales motion in slow industries still requires measurable proof of value.
• Private equity conversations often still start with cost reduction, but the playbook is shifting.
• The new AI-era diligence question is whether the system creates enough value.
• Tokmak believes the AI opportunity in education is especially large and positive.
• AI could personalize support for every student's learning journey.
• The education thesis is framed as one-to-one tutoring at scale.