Success Stories: Proven Results Across Industries
Real results through strategic technical leadership. Discover how Anton Golosnichenko helped startups in AdTech, TravelTech, and EdTech scale their technology, teams, and business.
Over 8+ years in software engineering management, I've helped companies at various stages—from early-stage startups to fast-growing Series A+ companies—solve critical technical challenges and scale platforms.
While these stories reflect my experience managing development and technology at Artix, Tripfusion, and EasyCoders, they demonstrate exactly the value I bring as a Fractional CTO: strategic vision, hands-on technical expertise, and proven results.
Scaling a MarTech Platform
When I joined Artix, one of Russia's leading digital marketing agencies, the technical department consisted of just 2 engineers supporting basic tasks—building BI reports and web analytics. The company had ambitious growth plans but lacked the technical infrastructure and team to meet the demands of major corporate clients.
Initial Situation:
- Small engineering team (2 developers) struggling with growing client needs
- Manual processes limiting campaign scale and efficiency
- Lack of automation and advanced analytics
- Limited personalization and audience segmentation capabilities
- Corporate clients demanding more sophisticated solutions
Approach
- Conducted comprehensive technical audit of existing processes
- Identified critical tasks where optimization would deliver quick wins
- Defined key bottlenecks in campaign management and analytics
- Established development best practices and code standards
- Developed strategy for building the technology department
- Built structured hiring process and technical assessment system
- Built technology department from scratch: hired developers, BI developers, data scientists, ML engineers, and web analysts
- Strategically grew team from 2 to 8 specialists
- Designed scalable microservices architecture
- Implemented AI algorithms for A/B testing optimization and traffic acquisition (multi-armed bandit algorithms)
- Implemented CI/CD pipeline and automated testing
- Implemented AI/ML tools for deep traffic analysis and optimization for conversion audience segments
- Built trigger communication system from scratch for multi-channel touchpoint sequences with different audience segments
- Developed custom HRM system tailored to agency needs, simplifying cross-department communication
- Created product for automatic data feed updates for price platforms (e-commerce and real estate)
- Expanded team to 12 specialists with clear role specialization
- Grew team leads and senior specialists within the team
Results
- Team growth 6x: from 2 to 12 specialists (developers, BI, DS, ML, web analysts) with structured engineering culture
- AI/ML tools: significant reduction in conversion costs and discovery of new ready-to-buy audience segments
- Automation: trigger communication and auto-updating feeds reduced manual tasks by 70%
- Scalability: platform handles 10x campaign volume without infrastructure issues
- Optimization: A/B testing based on multi-armed bandit algorithms
- Revenue acceleration: technology became key competitive advantage
- Corporate client acquisition: won and retained major clients thanks to advanced capabilities
- Team retention: built strong engineering culture with <10% annual turnover, grew team leads and senior specialists
- Operational efficiency: internal HRM system increased employee loyalty
- Market position: Artix became technology leader in Russian digital marketing
Key Takeaway:
"Strategic technical leadership isn't just about writing code—it's about building the right team, architecture, and AI/ML products that become your competitive advantage. The technical department transformed from a cost center into a revenue generator and key market differentiator."
Optimizing Complex Travel Platform Performance
As Head of Engineering at Tripfusion, a growing travel-tech platform, I faced the classic TravelTech problem: ensuring fast, reliable search results while integrating with dozens of supplier APIs, managing real-time inventory, and handling complex pricing logic—all at scale.
Initial Situation:
- Search performance degraded as user base grew (12-15 second response times)
- Monolithic architecture with implicit domain boundaries creating technical debt
- Unreliable supplier API integrations causing booking failures
- Complex inventory management from multiple suppliers
- Mobile experience significantly slower than desktop
- Codebase made it difficult to scale team and add new features
Approach
- Conducted deep analysis of performance bottlenecks and architectural issues
- Identified implicit domain boundaries and dependencies in monolith
- Started migration to modular monolith with clear bounded contexts (DDD approach)
- Defined key domains: Search, Booking, Inventory, Payments, Users
- Implemented explicit contracts between modules and clean architecture within them
- Redesigned aggregation layer for parallel supplier queries
- Built adaptive timeout handling for different API response patterns
- Optimized database queries and indexing strategies
- Implemented progressive loading for mobile experience
- Developed anti-corruption layer to isolate external integrations
- Developed dynamic recommendation system based on AI/ML algorithms
- Improved real-time inventory management system
- Built comprehensive monitoring and alerting infrastructure
- Created integration testing framework for supplier APIs
- Prepared architecture for future microservices migration
Results
- Modular monolith: clear bounded contexts with explicit contracts between domains
- Development velocity: time to add new features reduced by 40%
- Code quality: test coverage increased from 45% to 85%
- Team scalability: ability for multiple teams to work in parallel on different domains
- Microservices readiness: architecture allows gradual migration of critical modules
- Search speed: average response time from 15s to 3s (5x faster)
- Mobile optimization: from 30s to 5s on 4G connections (6x improvement)
- System reliability: search function uptime from 94% to 99.5%
- Conversion: 35% improvement thanks to faster search and personalized recommendations
- Booking success rate: from 87% to 96% (reduced failures due to improved integration handling)
- Tech debt: bug fixing time reduced by 50% thanks to clean architecture
Key Takeaway:
"Strategic architecture refactoring isn't just about improving code—it's an investment in the product's future. Moving to a modular monolith with DDD patterns not only solved current performance issues but also created a foundation for scaling both technology and team."
Building EdTech MVP from Concept to Product-Market Fit
As founder and CTO of an EdTech startup, I experienced firsthand the challenges of building a product from scratch—making critical architectural decisions, managing limited resources, and iterating toward product-market fit while controlling technical debt. The product concept: teaching programming through hands-on practice building clones of successful startups (Airbnb, Uber, Instagram).
Initial Situation:
- Unique concept: project-based learning on real startup cases
- Limited funding requiring smart technology choices
- Need to quickly prove concept to attract additional investment
- Balancing feature development with code quality/scalability
- Creating quality educational content in parallel with platform development
Approach
- Chose optimal tech stack: Next.js + Hasura + PostgreSQL + Yandex Cloud for rapid development
- Created core LMS features (courses, modules, user progress tracking)
- Developed video content delivery system with adaptive quality
- Created teacher dashboards for monitoring student progress
- Created first course: Airbnb clone with step-by-step guide
- Launched beta version with 200 students for initial feedback
- Added new courses: Uber, Instagram, Twitter clones
- Developed supplementary materials system (source code, cheat sheets, references)
- Developed engaging gamification elements (achievements, progress bars, challenges)
- Created personal student dashboards with progress visualization
- Added community features: forum, Q&A sections
- Optimized video content delivery for various internet speeds
- Implemented advanced analytics: watch time, completion rate, dropout points
- Added bookmarking and note-taking features for lessons
- Created GitHub repository system for each course with starter templates
- Scaled infrastructure on Yandex Cloud to support 5,000+ concurrent users
Results
- Course completion: 42% (2+ times higher than industry average for self-paced learning)
- Student engagement: average 45 min/day on platform
- Practical skills: 78% of students successfully created at least one complete clone
- Retention: 65% of users returned at least 3 times per week
- NPS: 58 among active users
- Organic growth: 40% of users came through word-of-mouth
- Revenue: reached $150K ARR within 12 months of launch
- Technical debt: maintained at manageable level despite rapid iterations
- Scalability: Hasura + Next.js architecture allowed easy addition of new courses
Key Takeaway:
"As founder-CTO, I learned that initial technology decisions define everything that follows. Choosing the right stack (Next.js + Hasura + PostgreSQL) and focusing on unique value proposition—learning through building real products—allowed us to quickly achieve PMF and create a solid foundation for scaling."
What These Stories Have in Common
Across different industries and company stages, strategic technical leadership delivers consistent results:
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