Build production LLM applications with LangChain. We develop RAG pipelines, AI agents, conversational systems, and enterprise AI products, with LangChain orchestration, LangGraph workflows, and LangSmith observability. From prototype to production-grade deployment.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Production RAG systems with LangChain — document loading, chunking strategies, embedding generation, vector store integration (Pinecone, Weaviate, Chroma, pgvector), retrieval optimization, and re-ranking for accurate, grounded AI responses.
AI agents that use tools, query databases, call APIs, and make decisions. Built with LangChain agent framework, custom tool definitions, structured output parsing, and error handling for reliable autonomous operation.
Stateful, graph-based agent workflows with LangGraph. Multi-step processes with branching logic, human-in-the-loop nodes, state persistence, and cycle support for complex business automation.
Production monitoring with LangSmith — trace every LLM call, evaluate output quality, track costs, debug chains, and run automated evaluation pipelines. Full visibility into your LLM application behavior.
Chatbots and assistants with persistent memory — conversation history, user preferences, and session context. Built with LangChain memory modules and external stores for scalable, context-aware conversations.
Take your LangChain prototype to production. Performance optimization, streaming responses, caching, rate limiting, error recovery, model fallbacks, and deployment on AWS/GCP/Azure with auto-scaling.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Production RAG systems with LangChain — document loading, chunking strategies, embedding generation, vector store integration (Pinecone, Weaviate, Chroma, pgvector), retrieval optimization, and re-ranking for accurate, grounded AI responses.
AI agents that use tools, query databases, call APIs, and make decisions. Built with LangChain agent framework, custom tool definitions, structured output parsing, and error handling for reliable autonomous operation.
Stateful, graph-based agent workflows with LangGraph. Multi-step processes with branching logic, human-in-the-loop nodes, state persistence, and cycle support for complex business automation.
Production monitoring with LangSmith — trace every LLM call, evaluate output quality, track costs, debug chains, and run automated evaluation pipelines. Full visibility into your LLM application behavior.
FAQ
Tell us about your LLM use case — we'll design a LangChain architecture with the right chains, agents, and retrieval strategy for production deployment.