RAG system on OpenAI and Upstash Vector with 25+ language support.
AI Integration
Add AI features to your existing product. Whether it's a chatbot, document processing, or smart recommendations — I'll plug it in cleanly.
There's a lot of AI hype and a lot of AI waste. I focus on AI that does something genuinely useful: answering customer questions accurately, processing documents automatically, extracting structured data from unstructured input.
I've built RAG systems for multilingual e-commerce (30+ languages), document analysis tools for fintech, and AI chatbots that actually stay on-topic. I know what works in production and what looks good in demos but falls apart with real data.
I work with OpenAI and Anthropic's Claude. I'll recommend the right model for your use case — not the most expensive one.
What's included
- OpenAI (GPT-4o) and Claude integration
- RAG (retrieval-augmented generation) with vector search
- AI chatbots with conversation memory and context
- Document analysis, extraction, and classification
- Embedding pipelines and semantic search
- Prompt engineering and output validation
How I approach this
Define what 'good' looks like
What should the AI do? What's an acceptable error rate? What are the failure modes you can't tolerate?
Choose the right approach
RAG, fine-tuning, or pure prompting? The right choice depends on your data, latency requirements, and budget.
Build and evaluate
Build the pipeline, then evaluate it against real examples. Iteration is built into the process.
Deploy with guardrails
Output validation, rate limiting, cost monitoring. AI in production needs more guardrails than most integrations.
Relevant projects
International e-commerce platform with 30 locales, product configurators, AI chatbot, and fully automated order flow: Stripe → Zoho CRM → Airtable → Mailgun →
AvailableReady to discuss your ai integration project?
Tell me what you're building. I'll get back to you within 24 hours.