Integrate Claude, OpenAI, Gemini, and open-source LLMs into your product the right way. Production-grade, cost-optimized, with the evaluation and monitoring that separates professional work from weekend hacks.
Every major LLM provider, plus the orchestration and data layers that make them actually useful in your product.
Claude, OpenAI (GPT-5, o-series), Google Gemini, Mistral, open-source models via Hugging Face, Groq, Together. The right model for each task.
Retrieval-Augmented Generation with vector databases (Pinecone, Weaviate, Qdrant, Chroma). Your LLM actually knows your data.
Systematic prompt development with evaluation. Not vibes-based — measured, iterated, optimized for your specific use case.
LLM calls are expensive at scale. Model routing, caching, prompt compression, batch processing — cut costs by 70-80% without sacrificing quality.
LLM evals, prompt versioning, response monitoring. Know if your AI is getting better or worse over time, not just vibes.
When prompting isn't enough — fine-tuning, embedding training, custom model adaptation. For specialized domains.
Real products with real LLM integrations serving real users every day.
Full LLM pipeline: image analysis → food recognition → nutritional reasoning → personalized advice. Claude + OpenAI + custom prompts.
See it live →Turkish-language LLM integration with vector database of 1,400+ foods. Multi-turn conversation, context management, production at scale.
See it live →I don't have allegiance to any single LLM provider. I recommend what works best for your use case and budget — Claude, OpenAI, Gemini, open-source.
Not a demo builder. I architect for production: reliability, cost, monitoring, graceful degradation. Your AI features work at scale.
I measure prompt quality, not guess. Systematic eval frameworks, A/B testing, regression catches. Real engineering, not "wow, cool response!"
I've reduced LLM costs by 70%+ on previous projects through smart caching, model routing, and prompt optimization. Token waste is my enemy.
Depends on your use case. Claude excels at reasoning, long context, and writing. GPT-5/o-series are strong at complex problem-solving. Gemini is often best at multimodal. Open-source (Llama, Qwen) wins on cost for simple tasks. Free consultation — I'll help you pick.
Retrieval-Augmented Generation. You need it if your LLM needs to know specific facts about your business, documents, or data that weren't in its training. Customer support, internal knowledge bots, document Q&A — all need RAG.
Simple single-LLM integration: $1,500-5,000. Full RAG system with evaluation: $10,000-30,000. Enterprise multi-model setups with fine-tuning: $30,000-100,000+. Ongoing token costs are separate (I'll help estimate).
I'm obsessive about this. Claude and OpenAI offer zero-data-retention options. I can design architectures where sensitive data never touches external APIs — on-premise LLMs, hybrid approaches. Tell me your constraints, I'll design around them.
Yes. I've deployed Llama, Qwen, Mistral models via vLLM, Ollama, Together AI, Groq. Open-source makes sense when you need privacy, cost control, or specialized fine-tuning. I'll recommend if it fits your case.
Free consultation. Tell me about your product, I'll design an LLM integration strategy that's production-ready, cost-effective, and actually useful.
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