LangChain Development & Consulting
LangChain and LangGraph engineers who turn prototypes into production AI agents, RAG pipelines and tool-calling workflows, with evaluation, monitoring and cost control.
LangChain consulting for production AI
LangChain makes it quick to build an AI prototype; making it reliable for real users is harder. Netcloud Consulting offers LangChain consulting and development for teams that need agents and retrieval systems that work every day, not just in a demo.
We use LangChain, LangGraph and LangSmith alongside n8n, vector databases and the model that fits each job, from OpenAI and Anthropic Claude to open-source Llama, Mistral and Qwen running on your own infrastructure.
What we build with LangChain
- AI agents that call your APIs, databases and SaaS tools to complete tasks.
- Stateful multi-step workflows with LangGraph, including human approval and retries.
- RAG pipelines over documents, product catalogs, tickets and knowledge bases.
- Chatbots and copilots for customer support, sales and internal teams.
- Structured extraction from emails, PDFs, invoices and quotes.
Why teams hire Netcloud Consulting for LangChain
- Production focus: evaluation sets, tracing and monitoring from day one.
- Model-agnostic: we benchmark models on your data before choosing one.
- Full-stack delivery: APIs, front ends, integrations and deployment, not just notebooks.
- Commerce and enterprise experience: see our agentic AI development and enterprise RAG work.
LangChain development and consulting services
- Architecture review: assess an existing LangChain or LangGraph app for reliability, latency, cost and security.
- Prototype to production: harden a proof of concept with tests, guardrails, caching and observability.
- Migration and upgrades: move legacy chains to current LangChain versions or to LangGraph agents.
- RAG optimisation: chunking, embeddings, hybrid search and re-ranking to improve answer quality.
- Team enablement: code reviews, patterns and training for your developers.
LangChain, LangGraph or n8n?
LangChain gives fine-grained control in code; LangGraph adds explicit state, branching and human-in-the-loop for complex agents; n8n is faster for integration-heavy workflows that business teams need to see and change. Many of our systems combine them: LangGraph agents exposed as tools inside n8n agentic workflows, or n8n triggering LangChain services. We recommend the mix after reviewing your use case.
For background, read our article on engineering reliable agents with LangChain.
How an engagement works
- Discovery call to understand the goal, data and constraints.
- Technical assessment or prototype within a fixed scope.
- Build and evaluate against agreed test cases and quality targets.
- Deploy and monitor in your cloud or ours, with optional managed AI agent support.
Frequently Asked Questions
Answers to curated most asked questions
A LangChain consultant designs, reviews and builds applications using LangChain and LangGraph, such as AI agents, RAG systems and chatbots, and helps take them from prototype to reliable production.
Use LangChain for straightforward chains and retrieval; use LangGraph when you need stateful, multi-step agents with branching, retries or human approval. We often use both.
Yes. We review the code, add evaluation and tracing, find the causes of wrong answers, slowness or high cost, and fix them.
OpenAI, Anthropic Claude, Google Gemini and open-source models, with vector stores such as pgvector, Pinecone, Weaviate, Qdrant and Elasticsearch, chosen to fit your stack.
Yes. We can deploy open-source models and the full LangChain stack in your private cloud or on-premises for data privacy.
Yes. We are based in Kolkata with offices in Mumbai and Fairfield, California, and work with clients worldwide.