This Week in Startups E2035: AI-Driven Auto Shops with MasterTech.ai and Data Observability with Monte Carlo

Date: October 29, 2024
Host: Jason Calacanis (represented by guest host Alex)
Guests: Linda Gray (CEO, MasterTech.ai), Lior Gauche (CTO & Co-founder, Monte Carlo)

Episode Overview

This episode dives deep into two startups innovating with AI in legacy industries: MasterTech.ai, an AI-powered platform revolutionizing auto repair shops, and Monte Carlo, a leader in the field of data observability for organizations managing complex data systems. Host Alex explores the founding stories, technical challenges, product showcases, customer traction, and future opportunities with both companies’ founders.

Part 1: MasterTech.ai — AI for Mechanics

The Founder's Journey and Startup Motivation [01:42–06:35]

• Linda Gray’s Path: Linda shares her unconventional journey from 15 years at Microsoft and Niantic to founding MasterTech.ai.
  • “After 15 years at Microsoft and 17 years in tech in general, I really just didn’t want to do one thing in my life. ... I wanted to eventually really go do a startup, do a zero to one project.” (Linda Gray, 03:38)
• Why Auto Shops?
  • Linda deliberately chose to leave the tech bubble, seeking to solve real-world, underserved problems.
  • “There was this big rest of the world where there were so many industries in the real world that were underserved and overlooked.” (Linda Gray, 05:17)

Tech in the Auto Shop: Challenges & Opportunities [09:11–11:05]

• Surprising Tech Savviness: Technicians (especially Gen Z) are highly adaptive to tech, though shop owners may resist.
• Co-founder’s Expertise: Partnered with Dave, a 20-year veteran ASE Master Technician, grounding the startup in real-world pain points.

What does MasterTech.ai Do? [11:05–13:48]

• Simplifying Complex Diagnostics: Techs face massive data fragmentation (makers, models, years, specifications).
  • “On average, 25% of their technicians’ time is spent doing computer research.” (Linda Gray, 12:10)
• Healthcare Analogy: Like healthcare AI, MasterTech.ai coalesces data for mechanics but benefits from the existence of rigid vehicle blueprints.

Building the Data Backbone [13:30–17:34]

• Data Trustworthiness: Avoided “baking” data into AI models to prevent probabilistic mistakes; relies on licensed OEM data sources.
• AI’s Role: The AI interprets technician queries and fetches precise data, enhancing ease-of-use beyond clunky legacy tools.
• OEM Partnerships: Major progress except for Honda & Toyota; their absence is mitigated as shops specialize and these brands are “less breakable.”
  • “It’s actually fine… a smaller volume for a lot of our shops. ... [Meanwhile] they specialize in European vehicles.” (Linda Gray, 16:23)
• Community Knowledge: Planning to aggregate expert technician insights, aiming to be “the Stack Overflow for automotive repair.” (19:14)

Platform Demo (Live Tour) [22:20–27:42]

• Flow: Add vehicle (via form or VIN scan) → AI chat interface → Quick action buttons (diagnostics, specs, labor times, etc.).
• Sample Query: Diagnosing a noise in a Mercedes; AI narrows possibilities by asking clarifying follow-ups.
• Quote:
  • “You’re only putting in like ‘noise issue’ and ‘when accelerating’; you don’t have to give it hyper-detailed requests. ... It goes to work for you.” (Alex, 25:13)
• User Experience: Platform quickly surfaces OEM flowcharts and recalls, outpacing traditional data lookup.

Market Traction and Growth [27:42–30:49]

• Early Metrics: Launched May 1; 40 shops on subscription (~$100K ARR).
  • “Even the same set of users are coming back... and engagement is growing week over week.” (Linda Gray, 27:52)
• Product Evolution: Continual addition of deeper data (wiring diagrams, maintenance schedules) and third-party integrations.
• Growth Channels: Word of mouth, social groups, and trade shows.

Addressing EVs & Platform Future [30:49–34:03]

• EV Impact: EVs and hybrids complicate maintenance more than they simplify it; MasterTech.ai aims to bridge this gap for shops.
  • “Hybrids are even more complicated because [techs must handle] both sets of systems... We see a huge potential with our platform.” (Linda Gray, 31:17)
• Future Vision: Expansion beyond auto — to HVAC, machinery, renewable energy.
  • “We really envision a future where you can scan the serial number for your HVAC unit or machinery and get all the service info you need.” (Linda Gray, 32:56)

[Notable Quotes – MasterTech.ai]

• “Our end product is not going to be artificial intelligence. It is going to be sort of helping to culminate human intelligence...” (Linda Gray, 19:38)
• “This is a very hard job. It is a very dangerous job as well. ... There’s so much pressure in production and not enough software help or time to even find all this precautions or information and safety.” (Linda Gray, 19:38)

Part 2: Monte Carlo — Data Observability

Origin Story & Category Creation [36:21–38:36]

• Term Coining: Monte Carlo brought “data observability” from DevOps to data engineering.
  • “We called it data observability and we built the equivalent of a Datadog or a New Relic for people that build data systems.” (Lior Gauche, 37:17)
• Market Expansion: Now recognized industry-wide, even outside tech (pharma, manufacturing, sports, etc.).
• Competition:
  • “Competition does make us better... It signals to customers that the category is important. ... We win most of our big comps.” (Lior Gauche, 38:36)

Industry Adoption & Paradigm Shift [40:33–42:17]

• Data Downtime: Concept of “data downtime” (bad dashboards/data outputs) became widely understood.
• Gartner Recognition: Data observability is now a standard industry expectation, with large enterprises issuing RFPs.

Technology: ML & AI at the Core [42:17–44:27]

• AI in Action: ML/AI supports anomaly detection and root cause analysis in vast, complex data systems.
  • “If you’re a data engineer... you can’t be expected to track every single table... so we have to use ML and now AI to help.” (Lior Gauche, 42:49)

Data-Driven AI Boom [44:27–47:03]

• Rise of Generative AI: Monte Carlo benefits from customers’ need to ensure their own data is ready for AI products.
  • “The real differentiator is the data… and that’s where we fit in.” (Lior Gauche, 44:46)
• Accelerated Business: Nearly all customers now ask about AI use cases and data quality for unstructured data.

Business Growth & Operational Discipline [48:07–51:16]

• Unstructured Data Partnerships: Strong relationship with Databricks, no acquisition or exit plans; IPO goal remains.
• Post-Series D Prudence: Despite raising $135M (valued at $1.6B), Monte Carlo maintained conservative spending and focus on long-term sustainability.

Profitability & Team Scaling [51:16–53:21]

• Economies of Scale: Continuous improvements in gross margins; infrastructure optimization is ongoing.
• New Hires: Brought in a CRO for scaling the playbook efficiently across larger GTM teams.

Long-Term Vision & Realism [53:21–55:39]

• Startups Always Risky:
  • “There’s always more chances of failing than succeeding in those kinds of things... We’re shooting for the highest outcomes that we possibly can.” (Lior Gauche, 54:18)
• Picks and Shovels for the Gold Rush: Monte Carlo was “selling shovels before people knew there was gold.”
  • “I think we’re in an AI gold rush. ... In the case of Monte Carlo, you guys actually started your picks and shovels business before people knew there was gold.” (Alex, 55:07)

[Notable Quotes – Monte Carlo]

• “Data downtime is the problem. Data observability is one solution to that problem.” (Lior Gauche, 41:29)
• “The models today… are quite incredible, and they’re also a commodity. ... The real differentiator is the data… and that’s where we fit in.” (Lior Gauche, 44:46)

Key Timestamps

• Linda Gray Career Story & Startup Motivation: [02:32–06:35]
• Why MasterTech for Auto Shops: [05:17–07:07]
• Technician Data Challenges Explained: [11:05–13:48]
• Data Source & Data Integrity in AI: [13:48–17:08]
• Product Demo & Live Query: [22:20–27:42]
• Traction, Market Adoption: [27:42–30:49]
• EV Impact & Multi-industry Vision: [30:49–34:03]
• Monte Carlo: Co-inventing Data Observability: [36:21–38:36]
• Tech Adoption & Gartner Recognition: [40:33–42:17]
• AI & ML in Data Observability: [42:17–44:27]
• Unstructured Data & Partnerships: [48:07–51:16]
• Scaling, Leadership, and IPO Ambition: [52:12–55:39]

Memorable Moments & Quotes

• “I didn’t want to be like the 20th company that just solves another variant of this [tech] problem.” — Linda Gray [05:17]
• “MasterTech is really analogous to healthcare AI, but for cars.” — Linda Gray [12:45]
• “You don’t have to spend 30 years working on one specific vehicle to gain insights that can be shared. ... It’s about helping to culminate human intelligence.” — Linda Gray [19:38]
• “We called it data observability and we built the equivalent of a Datadog or a New Relic for people that build data systems.” — Lior Gauche [37:17]
• “The models today... are a commodity. ... The real differentiator is the data.” — Lior Gauche [44:46]
• “[Monte Carlo] started your picks and shovels business before people knew there was gold.” — Alex (Host) [55:07]
• “We want to build. ... the best company of the decade if we can. That’s really hard.” — Lior Gauche [54:18]

Summary Takeaways

• Linda Gray’s MasterTech.ai is bridging a massive information and efficiency gap in auto shops with validated, context-aware AI and an intent to aggregate human expertise.
• Lior Gauche’s Monte Carlo both coined and now dominates “data observability,” critical as companies pour more and more data into generative AI systems, making data quality paramount.
• Both startups are fundamentally about empowering frontline workers — be they mechanics or data engineers — with intuitive, reliable tech, and both have strong product-led growth with clear ambitions for much broader impact.

Listeners unfamiliar with these companies or the episodes will come away with insight into how and why next-generation AI startups are targeting “old” industries, how data and human expertise remain foundational, and how category leadership is built by both technical and cultural vision.