AI & LLM Integration
I add LLM-powered features to your product: RAG, smart assistants, automated content and agent workflows. From prototype to production quality.
Adding an LLM is not just calling an API. The real challenge starts in production: cost control, latency, hallucination, prompt stability, data privacy and what your product does when the model answers wrong. Most teams ship a demo fast; turning it into a reliable, measurable and cost-sane production feature is a separate engineering effort.
I treat the LLM like the rest of the product: an engineering problem. The right model/cost balance, grounding on your own data with RAG when needed, keeping output structured and verifiable, designing error and timeout paths, and token/cost observability. I run these flows in production in my own apps (Motorii, CVCrafter, Yaris Analiz).
- LLM API integration (OpenAI, Anthropic Claude, Google Gemini)
- RAG: grounded, source-citing answers over your data (embeddings + vector search)
- Smart assistant / chat UI and agent / tool-calling workflows
- Structured output (JSON / schema) with a validation layer
- Cost and latency optimization, caching, model routing
- Privacy and security: data scoping, PII handling
- Grounded, source-citing answers with RAG
- Agent and tool-calling workflows
- Cost, latency and token observability
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