I build production AI systems and web platforms — end-to-end, from architecture to deployment. Based in Pickering, Ontario, serving clients across the GTA and remotely worldwide.
End-to-end AI engineering from prompt architecture to production deployment. I build systems using OpenAI, Azure OpenAI, Anthropic Claude, and ElevenLabs that handle real traffic and real costs.
OpenAI & Azure OpenAI
GPT-4, GPT-4o, and Azure model deployment with latency optimization
Anthropic Claude
Claude 3.5/4 integration for reasoning-heavy workflows
Prompt Engineering
Structured prompt architecture, testing, and versioning
AI SaaS Architecture
Billing, usage tracking, and defensively correct cost controls
Architecting multi-agent pipelines that coordinate LLMs, tools, and data sources to automate complex business processes end-to-end. From state management to tool-use patterns.
Agent Orchestration
Designing agent hierarchies and handoff protocols
Tool-Use Patterns
Function calling, API integration, and external tool coordination
State Management
Context windows, memory, and conversation persistence
Workflow Automation
End-to-end pipelines from input to validated output
High-performance websites and web applications built with Next.js, React, and TypeScript. I integrate AI capabilities into modern web stacks with performance and accessibility built in.
Next.js & React
App Router, Server Components, and modern React patterns
TypeScript
Type-safe code with robust architecture
Performance Optimization
Core Web Vitals, lazy loading, and edge caching
AI-Enhanced UX
Embedding LLM-powered features into web products
Strategic SEO, GEO (Generative Engine Optimization), and AEO (Answer Engine Optimization) that drives visibility in both traditional search and AI-powered search engines.
SEO & GEO Strategy
Technical SEO optimized for AI crawlers and citations
Google Analytics (GA4)
Data analysis, reporting, and actionable insights
Google Ads Management
Campaign management for budgets up to $10k/month
AEO Optimization
Structured content for AI Overviews and answer engines
How much does LLM integration cost for a typical project?
Most production LLM integrations start at $8,000–$15,000 for a scoped MVP. Costs depend on model choice, expected traffic volume, and whether you need custom prompt engineering, billing infrastructure, or multi-agent orchestration. I provide transparent estimates with per-token cost projections before we start.
What is the difference between prompt engineering and fine-tuning?
Prompt engineering optimizes how you instruct a pre-trained model — faster to implement, lower cost, and sufficient for 80% of use cases. Fine-tuning retrains the model on your proprietary data — necessary when you need domain-specific behavior, brand voice consistency, or compliance with internal terminology.
Can you integrate AI into an existing Next.js or React app?
Yes. I specialize in embedding LLM capabilities into existing web applications without full rewrites. Common patterns include AI-powered search with RAG, document summarization widgets, conversational assistants, and automated content generation.
What is a multi-agent workflow, and when do I need one?
A multi-agent workflow coordinates multiple AI systems (or 'agents') that each handle a specific task — research, writing, fact-checking, formatting — and hand off work between them. You need one when a single LLM prompt cannot reliably solve your problem end-to-end.
Do you work with Toronto / GTA clients in person?
Yes. I'm based in Pickering, Ontario, and regularly meet clients across Toronto, Mississauga, Markham, and the broader GTA for workshops, architecture reviews, and sprint planning.
How do you prevent AI hallucination in production systems?
I use a defense-in-depth approach: constrained prompt templates, retrieval-augmented generation (RAG), post-generation validation layers, and human-in-the-loop workflows for high-stakes outputs.