Your system is slow and your team can't figure out why. I can. Usually within the first 48 hours.
Performance optimization for B2B SaaS and AI platforms where latency, throughput, or infrastructure costs have become a business problem. Not a theoretical review. I find the bottleneck, prove it with data, fix it, and measure the result.
Most performance problems aren't where your team thinks they are. A slow API endpoint isn't always a database problem. A scaling wall isn't always an infrastructure problem. The diagnosis matters more than the fix, and misdiagnosis is expensive.
I've optimized systems handling millions of requests, reduced infrastructure costs by 40-60%, and turned 8-second page loads into sub-second responses. The patterns are consistent. The wins are measurable. Every optimization ships with before/after benchmarks so you can see exactly what changed.
11+ years across SaaS platforms, AI systems, real-time applications, and high-throughput infrastructure. 80+ clients. The performance problems I solve today are the ones I caused and fixed 10 years ago.
What Gets Optimized:
→ Application Performance
• API response times, rendering pipeline, and data fetching
• Backend processing pipelines and async workflow efficiency
• Memory management, connection pooling, and resource utilization
→ Database Optimization
• Query performance, indexing strategy, and execution plans
• Connection pooling, read replicas, and schema optimization
• Data archival strategy and table partitioning
→ Infrastructure & Cost Efficiency
• Compute right-sizing and auto-scaling configuration
• Infrastructure spend mapped to actual utilization
• Environment optimization and resource consolidation
→ Caching & Load Distribution
• Redis architecture, CDN strategy, and cache invalidation patterns
• Load balancing, rate limiting, and queue architecture
• Failover design and traffic spike handling
→ AI Workflow Performance
• Model serving latency and inference optimization
• Orchestration efficiency and batch processing
• Vector database tuning and embedding pipeline speed
→ Monitoring & Observability
• APM setup, custom dashboards, and performance baselines
• Alerting thresholds and regression detection
• Frontend performance: bundle size, Core Web Vitals
Who Needs This:
→ SaaS platforms where page load times are killing conversion
→ AI products where inference latency makes the UX unusable
→ Companies whose AWS bill doubled but traffic didn't
→ Teams hitting scaling walls they can't diagnose internally
→ Products preparing for a traffic spike, launch, or enterprise onboarding
Core Stack:
Performance profiling and optimization across the full application layer.
Your system is slow and your team can't figure out why. I can. Usually within the first 48 hours.
Performance optimization for B2B SaaS and AI platforms where latency, throughput, or infrastructure costs have become a business problem. Not a theoretical review. I find the bottleneck, prove it with data, fix it, and measure the result.
Most performance problems aren't where your team thinks they are. A slow API endpoint isn't always a database problem. A scaling wall isn't always an infrastructure problem. The diagnosis matters more than the fix, and misdiagnosis is expensive.
I've optimized systems handling millions of requests, reduced infrastructure costs by 40-60%, and turned 8-second page loads into sub-second responses. The patterns are consistent. The wins are measurable. Every optimization ships with before/after benchmarks so you can see exactly what changed.
11+ years across SaaS platforms, AI systems, real-time applications, and high-throughput infrastructure. 80+ clients. The performance problems I solve today are the ones I caused and fixed 10 years ago.
What Gets Optimized:
→ Application Performance
• API response times, rendering pipeline, and data fetching
• Backend processing pipelines and async workflow efficiency
• Memory management, connection pooling, and resource utilization
→ Database Optimization
• Query performance, indexing strategy, and execution plans
• Connection pooling, read replicas, and schema optimization
• Data archival strategy and table partitioning
→ Infrastructure & Cost Efficiency
• Compute right-sizing and auto-scaling configuration
• Infrastructure spend mapped to actual utilization
• Environment optimization and resource consolidation
→ Caching & Load Distribution
• Redis architecture, CDN strategy, and cache invalidation patterns
• Load balancing, rate limiting, and queue architecture
• Failover design and traffic spike handling
→ AI Workflow Performance
• Model serving latency and inference optimization
• Orchestration efficiency and batch processing
• Vector database tuning and embedding pipeline speed
→ Monitoring & Observability
• APM setup, custom dashboards, and performance baselines
• Alerting thresholds and regression detection
• Frontend performance: bundle size, Core Web Vitals
Who Needs This:
→ SaaS platforms where page load times are killing conversion
→ AI products where inference latency makes the UX unusable
→ Companies whose AWS bill doubled but traffic didn't
→ Teams hitting scaling walls they can't diagnose internally
→ Products preparing for a traffic spike, launch, or enterprise onboarding
Core Stack:
Performance profiling and optimization across the full application layer.