Startup Stories - Mixergy

Episode #2292: AI Automation That Makes Cold Calls
Host: Andrew Warner
Guests: Yevgeny Matce & Aiden Richards, Co-Founders of Resora
Date: January 8, 2026

Episode Overview

This episode tells the story of how Yevgeny Matce, a former real estate broker, developed AI automation to handle the grueling task of cold calling for real estate leads. Frustrated by the inefficiency and emotional drain of manual cold calling, he leveraged his computer science background to create a calling AI—initially for his own use—then rapidly realized its appeal for other brokers. Alongside co-founder Aiden Richards, Matce scaled the solution into Resora, a growing SaaS business that automates outbound calls for brokers. Their experience offers insights applicable far beyond real estate, signaling opportunities for automated outreach in many other industries.

Key Discussion Points and Insights

The Cold Call Problem in Real Estate

• Grueling Work: Yevgeny describes a typical day filled with hundreds of cold calls to expired real estate listings—homeowners who struggled to sell with previous agents ([01:32]).
  • “I would cold call those people because they have a higher chance of relisting.” — Yevgeny [02:05]
• Low Conversion Rates: Despite the targeted approach, actual success rates were dishearteningly low.
  • “If I were to estimate, 1 to 2%. 1%.” — Yevgeny [03:10]
• Inefficiency: Often required hundreds of dials to get through to a single relevant prospect.
  • “You can call 200 people before you actually get connected to someone that is the correct homeowner.” — Yevgeny [03:26]

From Broker to Builder: Birth of the AI Agent

• Technical Pivot: Yevgeny, with a degree in computer science, decided to automate his least favorite task by experimenting with early AI voice agents ([03:56]).
  • “After two years of cold calling, I got very fed up with it… when AI voice agents were becoming a thing. So I’m like, oh, wait, I can implement this.” — Yevgeny [03:56]
• Initial Results: Within a day of deploying his prototype AI agent, he booked his first listing appointment ([05:10]).

Rapid Growth and Early Sales

• Explosive Demand: After launching a video ad showing the AI at work, interest surged instantly—overwhelming what was initially a manual, custom setup ([08:54], [09:07]).
  • “As soon as we launched the Facebook ad campaign, it took us less than four days to get our first customer.” — Yevgeny [07:51]
• Monetization Model: Early pricing included a $1K–$2.5K setup fee, $500/month fee, plus $0.20 per AI conversational minute ([00:48]).
• First Revenue Milestone: "$40,000 in 40 days." ([00:44])
• Manual Onboarding: Each customer workflow was tailored, requiring weeks of manual effort and testing per client.
  • “We had above 80% conversion rate...but it was me actually doing a Google Meet with them, walking them through this, and closing the sale myself.” — Yevgeny [10:45]

Insights on Sales, Scaling, and Agency vs. SaaS

• Bottlenecks: Customizing and tweaking each agent for multiple clients quickly became unmanageable ([24:14], [24:47]):
  • “The worst thing you can possibly do, in my opinion, is give them an okay version and not a perfect version.” — Yevgeny [11:40]
  • Even minor tweaks required direct meetings and manual intervention, making scale impossible for a solo founder.
• Transitioning to SaaS: The team recognized the need for a self-serve platform with customizable, yet robust, AI agents—facilitating true scalability ([19:12], [27:45]).
  • “There’s no solution on the market right now where it allows a non-technical user to launch their AI agent and get it going in less than 10 minutes.” — Yevgeny [19:12]
• Agency Model vs. SaaS: Agencies can offer more bespoke services, but require constant manual work and are vulnerable to SaaS competitors ([26:25], [27:23]).

The Technology: Building AI for Voice Sales

• Early-Stage Workflow: Built with off-the-shelf tools like Vapi (voice AI), Zapier for automation, and CRM integrations ([13:23]).
  • “You’re going to be pretty much set to go with Vapi, Zapier, and maybe a CRM connection.” — Yevgeny [13:23]
• Custom Fine-Tuning: Transitioned from generic LLMs (like GPT) to building proprietary models specifically fine-tuned for phone sales to increase realism and conversion ([15:36], [16:07]).
  • “We take real conversations, we turn that into fine-tuning data...and we run supervised fine-tuning on a large language model… so it has more salesmanship in its responses.” — Yevgeny [16:07]
• Open Opportunity: The process could be replicated for other industries (plumbers, landscapers, etc.), using similar off-the-shelf components or as an agency ([14:25], [14:30]).

Productization and Market Fit

• Customization: The early product provided only basic dashboards for clients; lack of user-level customization demanded a more robust platform ([24:47]).
  • “Our front dashboard—the only thing you could see was audio recordings… you couldn’t tweak it whatsoever.” — Yevgeny [24:47]
• Self-Serve SaaS: The new Resora platform exposes agent parameters (voice, style, prompts, etc.), allowing users to launch agents and campaigns quickly ([27:45]).
• Profitability: Of the first $40K, $34K–$35K was profit, thanks to low running costs apart from Facebook ads ([32:49]).

The Founders' Dynamic

• Meeting Co-Founders: Yevgeny recruited Aiden via Y Combinator’s co-founder matching service. They “clicked” due to shared ambition and complementary skills ([20:27]).
  • “He’s very motivated, very ambitious and has that drive… without that, you’re not going to get anywhere.” — Yevgeny [21:35]
• Role Breakdown: Yevgeny leads everything product and technical; Aiden handles sales, outreach, marketing, and ops ([22:13]).
  • “My responsibilities really lie on the sales and marketing side...outreach and kind of brand creation, brand promotion, and handling everything on the operations side.” — Aiden [22:13]

Reflections on Technology and the Future

• AI Coding Tools’ Limits: Despite tools like Claude and Cursor making technical work faster, deep understanding of systems is still required ([30:19]).
  • “If you don’t know how to swing a hammer, the nails could go in bent… if you don’t know the framework, you wouldn’t even know what to look for.” — Yevgeny [30:24]
• Empowering Small Teams: Modern AI tools allow solo founders and small teams to achieve what used to require entire engineering departments ([31:49]).

Real Estate as an Early Adopter Market

• Willingness to Try New Tech: Some agents were excited to experiment, while others were deeply resistant—creating a mixed landscape ([34:13]).
• Brokerage-Led Training: Brokerages now offer AI classes, pushing adoption among agents ([35:10]).

Notable Quotes & Memorable Moments

• “$40,000 in 40 days.” — Aiden ([00:44])
• “I just got fed up with it. There was… no room for, ‘let’s try this new system…’ All the processes are the exact same thing as they were 30 years ago.” — Yevgeny ([07:00])
• “There’s no solution on the market right now where it allows a non-technical user to launch their AI agent and get it going in less than 10 minutes. There’s absolutely nothing.” — Yevgeny ([19:12])
• “If you don’t know how to swing a hammer, the nails could go in bent...” — Yevgeny ([30:24])
• “He’s very motivated, very ambitious and has that drive… without that, you’re not going to get anywhere.” — Yevgeny, on meeting Aiden ([21:35])
• “The worst thing you can possibly do, in my opinion, is give them an okay version and not a perfect version.” — Yevgeny ([11:40])
• “There’s literally no research out there on this topic. Zero.” — Yevgeny, on fine-tuning LLMs for sales ([18:38])

Important Segment Timestamps

• [00:44] – First 40 days revenue and business model
• [01:32] – Real estate cold calling realities
• [03:56] – Move from real estate to AI automation
• [05:10] – First success: AI sets listing appointment
• [07:51] – Launching customer acquisition via Facebook ads
• [10:45] – Manual sales closing and onboarding
• [13:23] – Off-the-shelf tools to build the initial workflow
• [15:36] – Evolution from agency to SaaS and need for custom LLMs
• [16:07] – Details on fine-tuning the AI sales model
• [19:12] – Decision to build scalable SaaS for non-technical users
• [20:27] – Co-founder search and meeting
• [22:13] – Division of responsibilities between Yevgeny and Aiden
• [24:47] – Early product’s lack of customization and customer demands
• [27:45] – Turning agency workflow into a flexible SaaS platform
• [30:19] – Reflecting on the role of AI coding tools and current technical challenges
• [32:49] – Startup profitability and ad spending
• [34:13] – Real estate agents’ openness (or resistance) to new technology
• [35:26] – Origin of the name “Resora”

Takeaways for Listeners

• AI-driven automation can unlock massive efficiency gains in any field reliant on high-volume outreach—if the tech feels ‘human’ enough.
• Agency models can be lucrative, but are difficult to scale without productization and self-serve customization.
• Technical founders must still possess deep expertise; coding AI tools aren’t “magic” in themselves.
• First-mover advantage is real, but so is the threat posed by the inevitable SaaS-ification of any successful manual process.
• The founding story of Resora demonstrates the power of combining domain frustration, technical skills, and a scrappy approach to validating (and then rapidly scaling) a new business solution.

Speaker Key:

• A: Andrew Warner (Host)
• B: Aiden Richards (Co-Founder, Resora)
• C: Yevgeny Matce (Co-Founder, Resora)

For founders, marketers, or technologists, this episode is a playbook for identifying automatable industry pain points, hacking together a first version, rapidly selling to eager early users, and then racing to evolve into scalable SaaS before the agency workload crushes the team. The lessons—and opportunities—are endlessly reusable.