VIVIMETRIX PLATFORM
CTO & Chief Architect
CTO & Chief Architect
Real-time fintech trading platform with a self-improving AI pattern recognition system.
THE CHALLENGE :
Build a complete real-time trading platform and engineering organization from the ground up in a competitive fintech environment. The system needed to process high-velocity market data, accurately detect complex chart patterns, deliver extremely low-latency alerts, and maintain high reliability, all while the product and team scaled.
APPROACH & KEY DECISIONS :
I treated the core problem as one of perception and decision-making under noise rather than simple data processing. The algorithm had to “see” patterns in pure time-series data the way an experienced trader would on a chart, while filtering noise and continuously improving.
Key decisions included:
Designing the pattern recognition engine from first principles instead of relying on existing libraries
Using geometric analysis (triangles and slope relationships) to evaluate noise
Making the algorithm self-tuning by evaluating every pattern’s outcome and adjusting its parameters daily
Prioritizing extreme performance through careful in-memory design and parallel processing
Building the engineering team, processes, and culture in parallel with the product rather than as an afterthought
SOLUTION & IMPLEMENTATION :
I architected and led the development of the full platform while simultaneously building the engineering organization. This included:
A high-performance pattern recognition engine capable of processing 10,000 charts in under 10 seconds
Real-time data pipelines and alerting infrastructure
Cloud-native architecture on AWS with strong observability
PCI-compliant transaction handling
Engineering team structure, hiring, sprint processes, CI/CD, and code quality practices
I worked closely with early engineers, established clear ownership, and created the operating rhythm that allowed the team to move quickly while maintaining quality. The algorithm itself improved from multi-second analysis per chart to under 1ms through iterative performance work and smarter caching.
RESULTS & IMPACT :
Achieved 99.99% platform uptime
Delivered pattern detection and alerts in well under 5ms (later improved to <1ms)
Grew the platform to thousands of users
Scaled the engineering team while improving delivery velocity
Created a patented, self-improving AI system that became a core product differentiator
Patent
Method, System and Device for Predicting Stock Performance and Building an Alert Model for Such Estimation
US 2022/0084119 A1 · Filed November 17, 2021
A machine learning system for detecting financial market patterns in real time and generating actionable trading alerts. The invention analyzes continuous time-series market data to identify and validate “W” (bullish reversal) and “M” (bearish reversal) patterns using precise geometric and temporal criteria. It applies multi-stage anomaly detection to filter noise, then forecasts key market characteristics including reversal zones, entry targets, and exit targets. Validated patterns feed adaptive models that update in near real time and trigger customized alerts.
WHAT I LEARNED :
Building the product and the organization at the same time reinforced that technical excellence and team health are inseparable. Introducing AI-assisted development also surfaced real concerns among engineers about being replaced. I learned that successful adoption depends less on the tools themselves and more on creating clarity, strong ownership, and a culture where AI is positioned as leverage rather than a threat. The highest leverage came from pairing new capabilities with clear expectations and human judgment.
TECHNOLOGIES :
C# · .NET · SQL Server · In-memory caching · Parallel processing · AWS · Real-time systems · AI/ML
VIVIMETRIX PLATFORM
CTO & Chief Architect
CTO & Chief Architect
Real-time fintech trading platform with a self-improving AI pattern recognition system.
THE CHALLENGE :
Build a complete real-time trading platform and engineering organization from the ground up in a competitive fintech environment. The system needed to process high-velocity market data, accurately detect complex chart patterns, deliver extremely low-latency alerts, and maintain high reliability, all while the product and team scaled.
APPROACH & KEY DECISIONS :
I treated the core problem as one of perception and decision-making under noise rather than simple data processing. The algorithm had to “see” patterns in pure time-series data the way an experienced trader would on a chart, while filtering noise and continuously improving.
Key decisions included:
Designing the pattern recognition engine from first principles instead of relying on existing libraries
Using geometric analysis (triangles and slope relationships) to evaluate noise
Making the algorithm self-tuning by evaluating every pattern’s outcome and adjusting its parameters daily
Prioritizing extreme performance through careful in-memory design and parallel processing
Building the engineering team, processes, and culture in parallel with the product rather than as an afterthought
SOLUTION & IMPLEMENTATION :
I architected and led the development of the full platform while simultaneously building the engineering organization. This included:
A high-performance pattern recognition engine capable of processing 10,000 charts in under 10 seconds
Real-time data pipelines and alerting infrastructure
Cloud-native architecture on AWS with strong observability
PCI-compliant transaction handling
Engineering team structure, hiring, sprint processes, CI/CD, and code quality practices
I worked closely with early engineers, established clear ownership, and created the operating rhythm that allowed the team to move quickly while maintaining quality. The algorithm itself improved from multi-second analysis per chart to under 1ms through iterative performance work and smarter caching.
RESULTS & IMPACT :
Achieved 99.99% platform uptime
Delivered pattern detection and alerts in well under 5ms (later improved to <1ms)
Grew the platform to thousands of users
Scaled the engineering team while improving delivery velocity
Created a patented, self-improving AI system that became a core product differentiator
Patent
Method, System and Device for Predicting Stock Performance and Building an Alert Model for Such Estimation
US 2022/0084119 A1 · Filed November 17, 2021
A machine learning system for detecting financial market patterns in real time and generating actionable trading alerts. The invention analyzes continuous time-series market data to identify and validate “W” (bullish reversal) and “M” (bearish reversal) patterns using precise geometric and temporal criteria. It applies multi-stage anomaly detection to filter noise, then forecasts key market characteristics including reversal zones, entry targets, and exit targets. Validated patterns feed adaptive models that update in near real time and trigger customized alerts.
WHAT I LEARNED :
Building the product and the organization at the same time reinforced that technical excellence and team health are inseparable. Introducing AI-assisted development also surfaced real concerns among engineers about being replaced. I learned that successful adoption depends less on the tools themselves and more on creating clarity, strong ownership, and a culture where AI is positioned as leverage rather than a threat. The highest leverage came from pairing new capabilities with clear expectations and human judgment.
TECHNOLOGIES :
C# · .NET · SQL Server · In-memory caching · Parallel processing · AWS · Real-time systems · AI/ML
VIVIMETRIX PLATFORM
CTO & Chief Architect
CTO & Chief Architect
Real-time fintech trading platform with a self-improving AI pattern recognition system.
THE CHALLENGE :
Build a complete real-time trading platform and engineering organization from the ground up in a competitive fintech environment. The system needed to process high-velocity market data, accurately detect complex chart patterns, deliver extremely low-latency alerts, and maintain high reliability, all while the product and team scaled.
APPROACH & KEY DECISIONS :
I treated the core problem as one of perception and decision-making under noise rather than simple data processing. The algorithm had to “see” patterns in pure time-series data the way an experienced trader would on a chart, while filtering noise and continuously improving.
Key decisions included:
Designing the pattern recognition engine from first principles instead of relying on existing libraries
Using geometric analysis (triangles and slope relationships) to evaluate noise
Making the algorithm self-tuning by evaluating every pattern’s outcome and adjusting its parameters daily
Prioritizing extreme performance through careful in-memory design and parallel processing
Building the engineering team, processes, and culture in parallel with the product rather than as an afterthought
SOLUTION & IMPLEMENTATION :
I architected and led the development of the full platform while simultaneously building the engineering organization. This included:
A high-performance pattern recognition engine capable of processing 10,000 charts in under 10 seconds
Real-time data pipelines and alerting infrastructure
Cloud-native architecture on AWS with strong observability
PCI-compliant transaction handling
Engineering team structure, hiring, sprint processes, CI/CD, and code quality practices
I worked closely with early engineers, established clear ownership, and created the operating rhythm that allowed the team to move quickly while maintaining quality. The algorithm itself improved from multi-second analysis per chart to under 1ms through iterative performance work and smarter caching.
RESULTS & IMPACT :
Achieved 99.99% platform uptime
Delivered pattern detection and alerts in well under 5ms (later improved to <1ms)
Grew the platform to thousands of users
Scaled the engineering team while improving delivery velocity
Created a patented, self-improving AI system that became a core product differentiator
Patent
Method, System and Device for Predicting Stock Performance and Building an Alert Model for Such Estimation
US 2022/0084119 A1 · Filed November 17, 2021
A machine learning system for detecting financial market patterns in real time and generating actionable trading alerts. The invention analyzes continuous time-series market data to identify and validate “W” (bullish reversal) and “M” (bearish reversal) patterns using precise geometric and temporal criteria. It applies multi-stage anomaly detection to filter noise, then forecasts key market characteristics including reversal zones, entry targets, and exit targets. Validated patterns feed adaptive models that update in near real time and trigger customized alerts.
WHAT I LEARNED :
Building the product and the organization at the same time reinforced that technical excellence and team health are inseparable. Introducing AI-assisted development also surfaced real concerns among engineers about being replaced. I learned that successful adoption depends less on the tools themselves and more on creating clarity, strong ownership, and a culture where AI is positioned as leverage rather than a threat. The highest leverage came from pairing new capabilities with clear expectations and human judgment.
TECHNOLOGIES :
C# · .NET · SQL Server · In-memory caching · Parallel processing · AWS · Real-time systems · AI/ML