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