The AI Opportunity
Most businesses know AI can help but struggle to move from concept to implementation. Generic chatbots disappoint. Pilot projects stall. The gap between what AI can do and what your systems actually do remains wide.
I bridge that gap. With deep experience in both web development and AI systems, I build integrations that work with your existing tools, respect your data policies, and actually ship to production.
What I Build
- LLM Integration: Connect OpenAI, Anthropic, or other LLM APIs to your existing systems. Proper error handling, rate limiting, and cost management built in.
- Agentic Workflows: AI that takes action. Multi-step processes where models reason, use tools, and complete complex tasks autonomously.
- Document Processing: Extract structured data from PDFs, contracts, emails. Build knowledge bases from unstructured content.
- Custom AI Tools: Internal tools that leverage AI for your specific workflows. Code review assistants, research tools, content pipelines.
Why Work With Me
AI expertise combined with production engineering experience
Beyond Chatbots
AI can do more than answer questions. Automate document processing, generate reports, analyze data, and augment your team capabilities in ways that actually move your business forward.
Production-Ready Integration
Proof of concepts are easy. Production systems that handle edge cases, fail gracefully, and scale with your business require engineering expertise. That is what I deliver.
Security and Privacy First
Your data stays your data. I implement AI solutions with proper access controls, data handling policies, and audit trails. Enterprise-grade security is not optional.
Measurable ROI
Every AI project should have clear success metrics. Hours saved, accuracy improved, costs reduced. If we cannot measure the impact, we should not build it.
Right-Sized Solutions
Not every problem needs GPT-4. Sometimes a simple classifier or rule-based system is the right answer. I recommend the approach that actually fits your needs and budget.
Agentic Architecture
Modern AI systems are more than single prompts. I build multi-step workflows where AI agents plan, execute, and iterate. True automation, not just text generation.
Common Use Cases
Real applications that deliver business value
Customer Support Automation
Triage tickets, draft responses, escalate complex issues. Reduce response time while maintaining quality.
Document Intelligence
Parse contracts, extract key terms, flag anomalies. Turn document review from hours into minutes.
Internal Knowledge Base
Ask questions in natural language, get answers from your documentation, wikis, and historical data.
Content Generation Pipeline
Draft marketing copy, product descriptions, or technical documentation with human review workflows.
My Approach
Discovery First
Before writing code, I understand your workflows. Where are the bottlenecks? What decisions require human judgment? What data do you have? This shapes what we build and how we measure success.
AI projects fail when they solve the wrong problem. Discovery prevents that.
Iterative Delivery
We start small and prove value. A focused pilot with clear metrics. Once you see results, we expand. This approach reduces risk and ensures you only invest in what actually works for your business.
No six-month projects with uncertain outcomes. Working software, frequent delivery.
Frequently Asked Questions
Is AI integration right for my business?
If you have repetitive tasks that require judgment but follow patterns, AI can likely help. Document processing, customer communication, data analysis, and content creation are common starting points. I can help assess your specific situation and identify high-impact opportunities.
What about data privacy and security?
This is critical and often overlooked. I implement solutions with proper data handling: what goes to external APIs, what stays local, how data is logged and retained. For sensitive industries, I can architect solutions using local models or private cloud deployments.
How much does AI integration cost?
Projects range from simple API integrations (a few thousand dollars) to full agentic systems (tens of thousands). I scope projects carefully and recommend starting small. Prove value with a focused pilot before scaling up.
What is an agentic workflow?
Traditional AI integration is: send prompt, receive response. Agentic workflows are: give AI a goal, let it plan steps, use tools, evaluate results, and iterate. Think of it as the difference between asking a question and delegating a task.
Which AI models do you work with?
OpenAI (GPT-4, GPT-4 Turbo), Anthropic (Claude), open source models (Llama, Mistral), and specialized models for specific tasks. I recommend the model that fits your requirements, not the most hyped option.
What if AI makes mistakes?
It will. Good AI systems are designed with this in mind. Human review for critical decisions, confidence thresholds, graceful fallbacks, and clear audit trails. The goal is augmenting human work, not replacing human judgment where it matters.