No PriorsBuilding an Autonomous Enterprise for Real-World Services with Netic Founder Melisa Tokmak
CHAPTERS
- 0:05 – 0:45
Netic’s mission: AI that runs essential-service enterprises
Elad introduces Melisa Tokmak and frames Netic’s focus: applying AI to the “real world” by operating core customer-facing workflows for essential-service businesses. Melisa positions Netic as infrastructure that sits between large service providers and their customers.
- •Melisa’s background (Scale AI, Meta) and why she’s focused on operational AI
- •Netic’s thesis: AI to run millions of real-world businesses
- •Starting point: large enterprises in essential services
- •Netic’s role as the connective layer between company and customer
- 0:45 – 2:02
What counts as “essential services” and where Netic fits in the stack
Melisa defines essential services broadly—home services, wellness, hospitality, automotive, and pet services—and emphasizes these are often very large, sophisticated businesses. Netic intermediates customer intent and business operations to match needs with service delivery.
- •Examples: HVAC, plumbing, electrical, consumer wellness memberships, hospitality, auto, pet services
- •These businesses can be billion-dollar, serving millions of consumers/businesses
- •Netic sits between the enterprise and end customer
- •Goal: understand customer needs and map them to operational rules and capacity
- 2:02 – 4:02
HVAC call walkthrough: voice agents + operational reasoning for dispatch
A concrete scenario (a heating failure in extreme cold) illustrates how Netic handles inbound across channels and conducts intake via AI agents. The system then reasons through operational constraints—urgency, equipment type, customer value, technician specialization—to schedule and dispatch optimally.
- •Multi-channel entry points: phone, text, website scheduling
- •Netic provides the voice/agent interface and dialogue
- •Complex triage: unit type, urgency, serviceability, timing
- •Optimization: right technician allocation, timing, and customer experience for revenue
- 4:02 – 6:04
Why these enterprises need automation: labor volatility, seasonality, and missed demand
Melisa explains how these companies traditionally scale by hiring, but that strategy caps growth and hurts margins—especially for EBITDA-focused, often private-equity-owned operators. Unpredictable staffing and seasonal spikes create missed calls and lost revenue, making always-on AI agents attractive.
- •Incumbent solution is people-heavy customer support teams
- •Margins matter: EBITDA businesses prioritize investing in field labor
- •Operational chaos: early hours, no-shows, churn, demand pileups
- •Seasonality/cycles (weather, holidays) amplify the need for reliable capture of demand
- 6:04 – 6:26
From “overflow” to “Netic-first”: AI becomes the front door
Elad notes that AI agents can function as overflow during spikes, but Melisa highlights a stronger pattern: most customers are adopting Netic as the primary first-touch interface. Netic becomes the default entry point for customer interactions rather than a backup.
- •Initial wedge: handling overflow and peak call volume
- •Shift to AI-first deployments
- •“Netic-first / N-one” pattern across customers
- •Majority of first interactions are handled by Netic agents
- 6:26 – 10:38
Service platform vs. AI roll-up: Melisa’s rationale and trade-offs
Elad contrasts Netic with AI roll-ups that buy and optimize service businesses. Melisa argues Netic’s path wins on personal fit (builder vs. M&A), product compounding, and broader applicability—building a reusable platform rather than point solutions for acquired assets.
- •Three reasons: what she wants to build, skill set, and scale
- •Scale AI experience shaped her interest in real-world impact and mission-critical workflows
- •Roll-ups emphasize M&A competence; Netic emphasizes product and engineering
- •Platform approach compounds across many enterprises, not just owned assets
- 10:38 – 12:56
AI in the real world vs. robotics: timelines and the human element
The conversation distinguishes software autonomy from robotics autonomy. Melisa believes robotics is a later “chapter” due to dexterity limits, environmental variability (non-standard buildings), and the importance of human interaction on high-stress customer moments.
- •Robotics will matter, but much later for these industries
- •Non-standard environments: buildings aren’t standardized for robots
- •Dexterity and diagnostics limitations (e.g., screws, tight spaces, unknown issues)
- •Human element: customers often call on a “worst day,” needing empathy
- 12:56 – 15:34
Can big AI labs compete? Focus, enterprise trust, and last-mile orchestration
Elad asks whether OpenAI/Anthropic/Google/Meta could replicate Netic. Melisa argues the labs’ incentives favor general solutions and rapid product churn, while enterprises need stability, vertical depth, and extensive last-mile orchestration beyond the base model.
- •Historical parallel: “Can Google do this?” → now “Can labs do this?”
- •Enterprises in these sectors want reliability, not fast product churn
- •Labs optimize for generality; Netic optimizes for a specific operational outcome
- •Winning requires orchestration: harnesses, software layers, accents, workflows, retention loops
- 15:34 – 19:09
Modern founder mindset: long-term craft vs. short-term exit anxiety
Elad observes founders avoiding verticals due to fear of labs’ roadmaps. Melisa attributes this to short-term exit thinking and a cultural shift toward urgency without commitment; she argues meaningful products require years of disciplined craftsmanship and focus.
- •Founders may over-index on lab roadmaps and potential competition
- •Critique: building for quick exit vs. decades-long mission
- •“Agency + urgency + rigor + patience” as a founder/operator recipe
- •Craftsmanship ethos: build the best “shoe,” not superficial signals
- 19:09 – 22:25
Hiring for agency: screening for follow-through and lived proof
Melisa explains how Netic evaluates candidates for agency by looking for repeated evidence across life, not a single anecdote. She probes for sustained commitment, resilience through difficulty, and personal ownership—often via the question about the hardest thing they’ve done.
- •Agency shows up repeatedly over time, not once
- •Look for initiation + sustained follow-through when things get hard
- •Core interview probe: “hardest thing you’ve ever done,” then dig into why/how
- •Example of disciplined long-term regimen as a valid agency signal
- 22:25 – 23:53
Five-year north star: building the autonomous enterprise (minus the human service work)
Melisa lays out Netic’s vision: automate everything in these companies except the actual on-site labor, freeing teams to focus on service quality and customer delight. She also emphasizes the parallel challenge of scaling the team while keeping the bar for “remarkable people” high.
- •Vision: autonomous enterprise across mission-critical workflows
- •Netic handles operations; humans focus on delivery/labor and service differentiation
- •Product strategy: interconnected layers that compound toward autonomy
- •Organizational goal: maintain a team of exceptional, craft-oriented people
- 23:53 – 27:25
Selling into “slow” industries: why the tech adoption stereotype is wrong
Elad challenges the assumption that these sectors adopt slowly. Melisa argues the best operators are highly value-driven and can move quickly when ROI is clear; she also describes data-driven tactics (e.g., satellite data for roofing) integrated into Netic to improve targeting and outcomes.
- •Misconception: essential services are “old school” and slow
- •Many are tech-forward and intensely ROI-focused; fast procurement when value is proven
- •Example: rapid enterprise deal cycle and large contract sizes
- •Operational + growth tooling: inbound/outbound, analytics, and external data (satellite/hurricane impact)
- 27:25 – 31:13
Private equity and AI: shifting playbooks from cost cutting to revenue creation
The discussion turns to how PE firms are adapting as “undiscovered gems” become rarer, shifting toward operational value creation. Melisa notes PE still defaults to cost-cutting, so Netic must reframe AI around durable ROI and net-new revenue, demonstrated via live deployments and measured outcomes.
- •PE playbook evolving toward tangible value creation, not just financial engineering
- •Risk: treating AI like quick software trials vs. long-term performance measurement
- •Emergence of AI-focused operating partners and technical staff at PE firms
- •Netic’s approach: prove value with live deployments and documented revenue impact
- 31:13 – 34:29
What excites Melisa about AI: education access and more positive narratives
Melisa highlights education as a transformative application—putting world-class learning and feedback “in your pocket,” expanding agency for people without resources. She also hopes the broader AI conversation shifts from fear to tangible benefits in critical life moments (services, health, help when it matters).
- •Education as a leverage point for social mobility and access
- •AI can expand personal agency by removing resource constraints
- •Caution: making tools easier doesn’t guarantee people choose to act
- •Desire for a more positive public narrative: AI helping in crises, health, and real needs
