S11 E20: Alex Galkin, Competera AI
🎯 Summary
Podcast Episode Summary: S11 E20: Alex Galkin, Competera AI
This episode features Alex Galkin, CEO and founder of Competera AI, discussing the journey of building his company focused on leveraging contextual AI for dynamic price optimization, contrasting early skepticism with current market necessity.
1. Focus Area
The primary focus is on AI-driven Price Optimization within retail and service industries (like cruise lines). The discussion heavily covers the challenges of building a data-intensive AI startup, product roadmap strategy, bootstrapping, securing initial customers, and the critical importance of the founding team and company culture.
2. Key Technical Insights
- Contextual Machine Learning for Pricing: Competera AI utilizes a deep machine learning algorithm they term “contextual machine learning.” This system incorporates significant context about how customers interact with products in specific store locations and aligns recommendations with stated business goals, moving beyond simple historical price elasticity tracking.
- Legacy System Inefficiency: The traditional method of price optimization involved large teams (e.g., 50-60 people) spending days or weeks running complex Excel models to track price elasticity. The AI solution reduces this analysis time to mere minutes.
- Initial MVP as a Spreadsheet: The very first iteration of Competera AI was essentially a Google Spreadsheet where optimization advice was delivered directly to the client, demonstrating an early focus on delivering immediate value even before building a polished software interface.
3. Business/Investment Angle
- Value Proposition: The AI offers significant ROI, positioning itself as a low-cost solution (e.g., $100/month) that can generate substantial returns (e.g., $10,000 back) by optimizing pricing on a location-by-location basis.
- Bootstrapping and Funding Realities: Galkin details a difficult period of bootstrapping for years, highlighting that while the technology was sound (even winning awards in 2018), market adoption for AI pricing was slow until recently. He emphasizes that marketplace success often requires intensive funding, contrasting with his initial bootstrap approach.
- Customer Prioritization Philosophy: Galkin advocates for a “healthy backlog” prioritizing: 1) Delighting existing paying customers, 2) Addressing top requests from future prospects, and 3) Paying down technical debt. He stresses that feedback from paying customers is the most serious and actionable.
4. Notable Companies/People
- Alex Galkin (CEO, Competera AI): The visionary who pitched AI pricing internally as early as 2014 (the same year OpenAI was founded) but was initially rejected by his former employer due to the subscription model conflict with their hourly billing structure.
- Cruise Liners: Mentioned as a key customer segment where the AI optimizes pricing based on the ship’s destination location.
- Steve Blank: Galkin mentions meeting him and strongly recommends reading his foundational works on customer development, despite having initially built his first version without following those principles.
- Basecamp Founders: Galkin expresses admiration for their bootstrapping philosophy and notes that Competera uses elements inspired by their “Shape Up” methodology.
5. Future Implications
The conversation suggests a strong industry shift toward AI taking over complex, data-intensive analytical roles that were previously the domain of highly paid consultants or large analyst teams. Galkin’s experience shows that while the technology might be ahead of its time initially, the current environment is ripe for AI tools that provide clear, quantifiable ROI in core business functions like pricing.
6. Target Audience
This episode is highly valuable for SaaS Founders, CTOs, Product Managers, and AI/ML Practitioners interested in the practical, non-linear journey of bringing deep-tech solutions to market, particularly those focused on B2B enterprise software and pricing strategy.
Comprehensive Summary
The podcast episode chronicles the entrepreneurial journey of Alex Galkin, founder of Competera AI, focusing on the development and market penetration of AI-driven price optimization software. Galkin recounts his early career, including dropping out of university to build a local ISP in Ukraine, before transitioning into consulting and subsequently championing the idea of subscription-based AI pricing in 2014—an idea that was initially dismissed by his employer who preferred hourly consulting models.
The core of Competera AI lies in its contextual machine learning approach, which analyzes numerous store-specific factors to recommend optimal pricing daily or weekly, replacing inefficient, manual processes involving large teams and complex Excel sheets. Galkin highlights the dramatic efficiency gains, citing the reduction of analysis time from days to minutes for clients like cruise liners.
Galkin candidly discusses the arduous path to product-market fit. He spent a year and a half building a large-scale software solution based on an outsourcing model, funding it himself, only to realize the critical importance of market traction and funding over pure technological complexity. This led to a painful pivot where he convinced his former Big Four employer to adopt his AI pricing solution. He details the high-stakes initial deployment: promising to optimize 30,000 products in 90 days but only having the infrastructure ready for 300. He successfully navigated this near-disaster through quick thinking and leveraging the client’s commitment to the concept rather than the initial scale.
Regarding product development and team building, Galkin stresses the difficulty of managing a roadmap without established roles, advocating for a customer-centric backlog that prioritizes paying users. He also touches upon the necessity of addressing technical debt proactively. On team building, he emphasizes that founding members must be driven by the
🏢 Companies Mentioned
đź’¬ Key Insights
"Now you see the AI adoption is everywhere. We are finally getting very cool questions on our piece. This is the procurement procedure when they buy software. Before, like I felt like we were sending flying cars where people were looking for horses."
"We even now feel this is now the time because of ChatGPT, because of all this adoption. But can you imagine me spending like millions of dollars raising—I think that three or four people in different countries—I never done it before. And because investors like get the money and not spend it, the same issue that actually take and spend it wrong. They force you to do probably my biggest mistake."
"number one is actually the second reason why startups fail is actually premature growth."
"I'm still a big fan of if you are at the stage like zero to one million, this monolith is absolutely cool stuff to have. But you need to detect at a certain point to start building this microservices that help you to scale."
"At a certain point, when you feel that your audience is reaching eight people sticking with your product, you find this use case, and people continue returning to your platform, this is exactly the time that you need to think to build in parallel your own software but in a more scalable way."
"I was really hands-on. And if I love it by the end of the day, still got feel because you can't build what people ask because otherwise, they don't know how to ask, especially if you build some new AI tool that they can't imagine, guys."