AI Application Development

AI Application Development

From Prototype to Production in Days, Not Months. Production-ready RAG systems with measurable quality improvements.

From Prototype to Production in Days, Not Months

Most AI demos don’t survive contact with real users. I build applications with the reliability, observability, and operational maturity that comes from 20+ years of shipping production systems.

I don’t just understand LLMs—I understand what happens when your RAG system hits 10,000 users at 3 AM and something breaks.

Portfolio

RAG Demonstration & Evaluation Systems

I built and deployed production-grade RAG demonstration applications on multiple platforms:

Live Production Demos:

  • Vercel RAG Demo — Full-stack chatbot with semantic search. Next.js 16, React 19, OpenAI GPT-4o, Neon PostgreSQL (pgvector). Sub-second streaming responses across 100+ document corpus.
  • Cloudflare Workers RAG Demo — Edge-deployed RAG with on-device LLM inference (Llama-3.1-8B). Zero external API calls, optimized for global latency.

Research & Evaluation:

  • RAG Quality Evaluation Lab — Jupyter-based experimentation framework with custom metrics (Precision@K, Recall, MRR, NDCG). Achieved +15% precision improvement through systematic testing. Not RAGAS—ground-truth dataset curation and measurement.

Evidence:

  • ~5,000+ lines of production-quality Python and TypeScript
  • Two live, public demos with real user traffic
  • Custom evaluation framework with measurable improvements
  • Multiple deployment platforms demonstrating architectural flexibility

What I Deliver

Production-Ready RAG Systems

Not tutorials. Not proof-of-concepts. Production systems.

  • Sub-second latency with streaming responses for real-time UX
  • Multi-LLM architectures — OpenAI, Anthropic, open-source models (Llama, Mistral)
  • Vector database design — pgvector, Cloudflare Vectorize, Pinecone
  • Platform flexibility — Vercel, Cloudflare Workers, traditional cloud (AWS)
  • Advanced retrieval patterns — Reranking, query expansion, hybrid search, semantic chunking
  • Citation tracking — Source attribution with confidence scores

Evaluation Rigor That Drives Improvement

I measure quality, not vibes.

Custom evaluation frameworks built from first principles:

  • Ground-truth dataset curation from real user queries
  • Retrieval metrics: Precision@K, Recall, Mean Reciprocal Rank (MRR)
  • Generation metrics: Relevance scoring, hallucination detection
  • Ranking metrics: Normalized Discounted Cumulative Gain (NDCG)
  • Dashboard visualization for tracking improvements over time

Proven results: +15% precision improvement, +22% overall quality through systematic experimentation and measurement.

Full-Stack Integration

Because AI features don’t exist in isolation.

  • React/Next.js frontends with streaming UX patterns
  • Edge deployment for global latency optimization
  • Authentication and rate limiting (protecting your API costs)
  • Cost optimization strategies (model selection, caching, prompt engineering)
  • Observability and monitoring for production AI systems
  • Security considerations for user data and prompt injection

Agentic Development Expertise

I build with AI, not just for AI.

  • Evaluated 6+ agentic coding tools (Claude Code, Kiro, Copilot, others)
  • Demonstrated 2-3x development velocity through tool mastery
  • Context engineering patterns (system prompts, agent roles, structured workflows)
  • Specification-driven development (SDD) approaches
  • CLI-first workflows enabling automation and programmable development

Why This Matters

I Ship

I don’t stop at “it works on my machine.” My RAG demos have been running in production with real users. I deploy to multiple platforms, implement authentication, handle errors gracefully, and optimize for cost.

I Measure

Ground-truth evaluation, not hunches. When I say “+15% precision improvement,” I mean I curated test datasets, measured baseline performance, implemented changes, and validated results.

I Understand Infrastructure

10+ years of observability and on-call for revenue-critical systems informs every architectural decision I make. I know what breaks at scale, what costs spiral, and what keeps you up at 3 AM.

I Move Fast

I prototype fast, iterate based on feedback, and ship incremental improvements without rewriting everything. I advocate “Thinnest Viable Platform” methodology leveraging agentic development tools and reusable patterns for rapid deployment.

Technical Capabilities

LLM Integration

  • OpenAI: GPT-4o, GPT-4o-mini, embeddings (text-embedding-3-small/large)
  • Anthropic: Claude 3.5 Sonnet, Claude 3 Haiku
  • Open Source: Llama 3.1 (8B/70B), Mistral, Mixtral
  • Local Inference: Ollama, Cloudflare Workers AI, OpenVINO

Vector Databases

  • Neon PostgreSQL with pgvector
  • Cloudflare Vectorize
  • Pinecone
  • Chroma (local development)

Deployment Platforms

  • Vercel: Next.js serverless with streaming responses
  • Cloudflare: Edge deployment, Workers AI, Vectorize, D1, R2, KV
  • AWS: Bedrock, SageMaker, Lambda, ECS, RDS, S3, API Gateway, SQS, SNS
  • Containerized: Docker, dev-container patterns

Languages & Frameworks

  • TypeScript/JavaScript (Node.js, Next.js, React, Astro)
  • Python (Jupyter, FastAPI, data processing)
  • SQL (advanced queries, performance optimization)
  • Markdown/MDX for content management

AI Development Tools

  • Anthropic Claude Code (primary agentic coding tool)
  • GitHub Copilot
  • Amazon Kiro
  • Google Antigravity
  • Warp
  • OpenCode
  • OpenAI CODEX
  • Cursor
  • MCP (Model Context Protocol) servers

Service Offerings

1. RAG Application Development

Situation: You need to integrate your knowledge base, documentation, or content into an AI-powered experience but don’t know where to start.

Our Solution: End-to-end RAG application development from data ingestion through production deployment. I handle:

  • Document processing and chunking strategies
  • Embedding generation and vector database design
  • Retrieval optimization (hybrid search, reranking)
  • LLM integration with streaming responses
  • Frontend development with modern React patterns
  • Production deployment and monitoring

Timeline: 2-4 weeks for MVP, depending on corpus size and complexity.

2. RAG Quality Optimization

Situation: Your RAG system works, but results are inconsistent. Users report irrelevant answers or hallucinations. You need to improve quality systematically.

Our Solution: Evaluation-driven optimization using custom metrics frameworks:

  • Ground-truth dataset creation from real user queries
  • Baseline measurement across multiple dimensions
  • Systematic experimentation with retrieval and generation strategies
  • A/B testing infrastructure for validating improvements
  • Dashboard reporting for tracking quality over time

Typical improvements: 10-20% precision gains, 15-25% relevance improvements.

3. Multi-Platform RAG Architecture

Situation: You need RAG capabilities across different platforms—edge, cloud, mobile—with different cost and latency requirements.

Our Solution: Platform-specific implementations that share core patterns:

  • Edge deployment for low-latency global access
  • Cloud deployment for complex processing and larger models
  • Hybrid architectures balancing cost, latency, and capabilities
  • API design for cross-platform consistency

Example: Deploy lightweight Llama model at the edge for instant responses, with fallback to GPT-4o in cloud for complex queries.

4. AI Feature Integration

Situation: You have an existing application and want to add AI capabilities without rewriting everything.

Our Solution: Surgical integration of AI features into existing codebases:

  • API design that isolates AI complexity
  • Gradual rollout strategies with feature flags
  • Cost controls and rate limiting
  • Monitoring and observability for AI-specific metrics
  • Documentation and team training

Best for: Established products adding “ask AI” features, semantic search, or intelligent automation.

5. Consulting & Technical Advisory

Situation: Your team is building AI features but needs guidance on architecture, tool selection, or quality optimization.

Our Solution: Fractional AI engineering support:

  • Architecture review and recommendations
  • Code review with focus on RAG patterns and quality
  • Evaluation framework design
  • Team mentoring on AI development practices
  • Tool selection guidance (LLM providers, vector databases, deployment platforms)

Engagement: Flexible hours, typically 10-20 hours/month.

Why Choose Me?

Recent Portfolio Evidence

My work isn’t theoretical. You can visit my live demos right now:

Try them. Break them. See how they handle errors, edge cases, and streaming responses.

Evaluation Expertise

I don’t rely only on RAGAS or off-the-shelf tools. I build custom evaluation frameworks because every RAG system has unique quality requirements. I know how to measure what matters for your use case and optimize for best cost:benefit outcome.

Full Stack Background

Most AI engineers come from data science or ML research. I come from 20+ years of designing, delivering, and keeping revenue-critical systems running. That changes how I think about production AI:

  • User experience and fitness for purpose
  • Cost monitoring and optimization
  • Error handling and graceful degradation
  • Observability for debugging production issues
  • Security considerations (prompt injection, data leakage)
  • Scalability and performance optimization

Agentic Development Mastery

I’ve evaluated 6+ AI-assisted coding tools and achieved 2-3x development velocity. I understand context engineering, specification-driven development, and how to make AI tools genuinely useful—not just autocomplete on steroids.

Multi-Platform Capability

Different platforms have different tradeoffs. I’ve deployed production systems to:

  • Vercel (serverless, great for rapid iteration)
  • Cloudflare Workers (edge, lowest latency)
  • AWS (traditional cloud, maximum control)

I can help you choose the right platform for your requirements and budget.

AWS Certified

AWS Certified AI Practitioner (active through 2028). I speak both infrastructure and AI fluently.

Ideal Client Profile

You’re a good fit if:

  • You want to ship AI-enabled products and services now
  • Quality matters—you want measurable improvements, not “it seems better”
  • You value infrastructure thinking alongside AI capabilities
  • You’re building a product, not conducting research
  • You need someone who can move fast and iterate based on feedback

You might not be a good fit if:

  • You need ML research or model training expertise
  • You need cutting-edge ML engineering (custom model architectures)
  • You need to build AI infrastructure from scratch (MLOps platforms, model registries)

How I Work

Discovery & Scoping (Week 1)

We start with a conversation about what you’re trying to accomplish:

  • What problem are you solving for your users?
  • What does success look like? (Specific metrics, not “better results”)
  • What constraints matter? (Cost, latency, accuracy)
  • What’s the timeline and budget?

I provide a clear proposal with scope, timeline, and pricing.

Rapid Prototyping (Week 1-2)

I build a working prototype quickly so you can see results and provide feedback:

  • Basic RAG pipeline with your content
  • Simple frontend for testing
  • Initial quality assessment

This isn’t “final” code—it’s for validation and learning.

Iteration & Refinement (Week 2-4)

Based on feedback, I refine:

  • Improve retrieval quality through experimentation
  • Enhance UX with streaming, citations, error handling
  • Implement production concerns (auth, rate limiting, monitoring)
  • Optimize costs and performance

Deployment & Handoff

I deploy to your chosen platform and provide:

  • Documentation (architecture, deployment, maintenance)
  • Evaluation framework you can use for future improvements
  • Team training if needed
  • Ongoing support options

Flexible Engagements

Some clients need a quick MVP. Others need ongoing optimization. I structure engagements around outcomes, not billable hours.

Pricing

Pricing varies based on scope, timeline, and complexity:

  • RAG MVP: $5,000 - $15,000 (2-4 weeks)
  • Quality Optimization: $3,000 - $8,000 (1-2 weeks)
  • Multi-Platform Architecture: $10,000 - $25,000 (3-6 weeks)
  • Fractional AI Engineering: $5,000 - $10,000/month (10-20 hours)

All projects include:

  • Source code with documentation
  • Deployment to your infrastructure
  • 30 days of post-launch support
  • Evaluation framework (where applicable)

Note: I’m also open to full-time roles for the right opportunity. If you’re hiring an AI Engineer and this resonates, let’s talk.

Get Started

Ready to move from prototype to production? Let’s talk.

Contact: Get in Touch · LinkedIn

Portfolio:

Last Updated: December 2025