LLM OpenAI / LLM APIs

LLMs in Production Applications

Large Language Models have moved from research curiosity to production infrastructure in 18 months. We build LLM-powered features that are reliable, cost-controlled, and observable — not demos that break at scale or leak data.

Our LLM Engineering Approach

• LangChain and LlamaIndex for RAG (Retrieval-Augmented Generation) pipelines
• Vector databases (Pinecone, Weaviate, pgvector) for semantic search
• Prompt engineering with version control and A/B testing
• Structured output parsing with Instructor / Pydantic
• LLM observability with LangSmith or Helicone
• Cost monitoring and model routing for optimal price/performance