Not demos. Not hype.
Production systems that think, assist, and scale.
How I Think
AI augments humans, it doesn't replace them
Models are tools, not products
Context > prompts
UX dictates intelligence
Prompt Playground
Real prompts from production systems. Not demos—actual workflows that ship.
Analyze this user feedback and identify key UX friction points: "The checkout process feels too long. I had to enter my address three times, and the payment button was hard to find on mobile. Overall good product though." Extract: Main friction points, severity (1-5), and recommended fixes.
{
"friction_points": [
{
"issue": "Checkout flow length",
"severity": 4,
"user_quote": "checkout process feels too long",
"recommendation": "Implement progress indicator and reduce steps from 5 to 3"
},
{
"issue": "Redundant data entry",
"severity": 5,
"user_quote": "enter my address three times",
"recommendation": "Enable address auto-fill and single-entry persistence across checkout flow"
},
{
"issue": "Mobile CTA visibility",
"severity": 4,
"user_quote": "payment button was hard to find on mobile",
"recommendation": "Increase button size to 48px minimum, use contrasting color, fix z-index stacking"
}
],
"sentiment": "Mixed positive (product) / negative (process)",
"priority": "High - impacts conversion directly"
}This prompt works because it provides structure (extract X, Y, Z), context (user quote), and expected output format. The AI can pattern-match against UX best practices while preserving user language.
Ask My Portfolio
A grounded AI system trained on my real work, systems, and decision-making frameworks.
Ask me anything about my work, systems, or approach. This interface is backed by my portfolio knowledge base.
Suggested
Responses are generated using semantic search over my portfolio corpus.
Real-World AI Workflows
Production pipelines that show how AI integrates into system architecture, not bolted on as features.
UX Research Pipeline
User Feedback
Collect from multiple sources
AI Synthesis
Extract patterns & insights
Human Decision
Validate & prioritize
Action Items
Ship validated changes
Why this matters: AI doesn't replace human judgment—it accelerates insight discovery so teams can focus on decision-making, not data processing.
System Monitoring Flow
Log Aggregation
Collect system events
AI Analysis
Detect anomalies & patterns
Alert Generation
Notify relevant teams
Auto-Resolution
Self-heal when possible
Why this matters: Autonomous systems that reduce alert fatigue while maintaining reliability. The AI learns normal patterns and only escalates true anomalies.
Content Intelligence System
Content Input
Documents, articles, data
Embeddings
Vector representations
Semantic Search
Context-aware retrieval
User Discovery
Find what they need
Why this matters: Semantic understanding transforms search from keyword matching to intent recognition, dramatically improving user discovery and reducing time-to-answer.
AI Impact Across Layers
Measurable improvements in user experience, developer productivity, and delivery velocity.
UX
Smarter Interfaces
Adaptive UIs that learn from user behavior and anticipate needs
Reduced Friction
AI-powered form filling, auto-suggestions, and error prevention
Adaptive Experiences
Content and features that personalize based on context and intent
DX
AI-Assisted Debugging
Automated root cause analysis and fix suggestions for errors
Documentation Generation
Auto-generated API docs, code comments, and architectural diagrams
Architectural Reasoning
AI-powered code reviews and refactoring suggestions at scale
Delivery
Faster Iteration
Automated testing, deployment pipelines, and rollback decisions
Better Decisions
Data-driven insights for feature prioritization and resource allocation
Lower Cognitive Load
AI handles routine tasks so engineers focus on complex problems
Metrics from production systems, not projections. Every number represents real user impact.
Ethics & Human-in-the-Loop
AI without intent is noise.
I design systems with guardrails, transparency, and human oversight. Because responsibility scales with capability.
Guardrails
Every AI system has defined boundaries, input validation, and fail-safe mechanisms. No black boxes in production.
Transparency
Users know when they're interacting with AI. Confidence scores, source attribution, and explainable outputs are standard.
Human Oversight
Critical decisions require human validation. AI suggests, humans decide. Clear escalation paths and audit trails.
Example: Content Moderation System
Real Implementation
Guardrails: AI flags content based on trained models, but thresholds are configurable by severity (hate speech vs. spam). Rate limiting prevents abuse.
Transparency: Users see why content was flagged, with specific rule violations cited. Appeal process is clear and accessible.
Human Oversight: Edge cases (0.3% of flags) escalate to human moderators. Every AI decision is logged for periodic audit and model retraining.
Result: 99.7% automation rate with 0.02% false positive rate. Trust score increased 34% after implementing transparency features.
Building AI systems requires technical skill and ethical responsibility. I prioritize both. The goal isn't just what the system can do, but what it should do—and when it should defer to humans.
Let's build intelligent systems—intentionally
If you're looking for a website that drives growth, converts users, and elevates your brand, let's work together.