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

What It Covers

Melisa Tokmak, founder of Netic, explains how her company deploys AI agents to run autonomous enterprise operations for billion-dollar home services, HVAC, pet care, and wellness companies — handling all customer interactions, scheduling, and labor dispatch so businesses can focus entirely on service delivery.

Key Questions Answered

• •AI-First Adoption Rate: Over 70% of Netic's enterprise customers have gone fully "Netic-first," meaning every initial customer interaction is handled by AI agents rather than humans. This shift from overflow support to primary interface represents a fundamental operational restructuring, not incremental automation, and has generated over $600 million in revenue for customers through AI-handled interactions.
• •Essential Services Complexity: Deploying AI in HVAC or plumbing requires resolving multiple variables simultaneously — unit type, customer lifetime value, technician specialization, and scheduling urgency — before dispatching labor. Businesses targeting this space must build orchestration layers, domain-specific harnesses, and vertical software on top of foundation models; raw model capability alone cannot address this operational depth.
• •Rollup vs. Platform Trade-off: Founders choosing between AI-enabled rollups and vertical software platforms should assess whether their core skill is M&A or product building. Rollup products serve only acquired companies and cannot compound across industries, while a platform approach allows the same intelligence layer to serve thousands of businesses, creating scalable network effects and compounding data advantages.
• •Screening for Agency in Hiring: Rather than asking about recent work achievements, probe a candidate's entire life history for sustained, self-initiated effort. Ask what the hardest thing they have ever done is, then dig into the why. The signal is not one dramatic example but a consistent pattern across years of starting things independently and following through without external pressure.
• •Private Equity AI Misconception: PE firms default to framing AI as a cost-cutting tool because they rarely encounter platforms that generate net-new revenue. When selling AI into PE-owned portfolios, lead with live deployment data showing incremental revenue generated — not demos — and explicitly reframe the conversation from margin compression to new customer acquisition and cross-portfolio service bundling opportunities.