LangChain Development. LangChain apps built for production. With evals. Not just Jupyter notebooks.
LangChain development for production LLM applications. RAG pipelines with hybrid retrieval, LangGraph agent workflows with state management, LangSmith observability, vector store integration, and proper eval harnesses. Built on LangChain Python or LangChain JS depending on your stack. Shipped in 4 to 14 weeks. USD pricing.
Tell us your AI use case, the LLM provider you prefer (OpenAI, Anthropic, Azure OpenAI, open source), and where your data lives. Scoped plan plus quote within 3 business days.
Get started in 60 seconds
Who we've built for.
How we work
- Overview
- Three phases from Jupyter prototype to production LangChain app. The biggest gap between LangChain demo and LangChain production is the eval harness, the observability, and the prompt versioning. We start with those, not after.
- Step 1 — Scope and architecture
- Two weeks scoping. AI use case definition. Eval set of 50 to 200 representative inputs. LLM provider selection. Vector store decision (pgvector, Pinecone, Weaviate, Qdrant, Azure AI Search). LangSmith vs custom observability decision. Prompt versioning plan.
- Step 2 — Build in sprints
- 2 to 10 weeks build. Two-week sprints. RAG pipeline with hybrid retrieval and re-ranking. LangGraph for multi-step agents with proper state management. LangSmith traces on every PR. Prompt regression tests on every commit. Cost tracking per request.
- Step 3 — Harden and launch
- 1 to 2 weeks hardening plus deploy. Red-team for prompt injection. PII redaction validated. Production deploy with rate limiting and cost guardrails. LangSmith dashboards for production observability. Two weeks of hypercare with prompt iteration based on real traffic.
Recent cloud and AI infrastructure builds
Recent LangChain and LLM application builds.
LangChain RAG with hybrid retrieval over compliance documents, LangGraph agents for multi-step case analysis, and LangSmith observability for audit-grade evaluation.
Read case study →
LangChain on Azure OpenAI for clinical decision support with grounded RAG over clinical guidelines, citation enforcement, and clinical-safety review built into the eval harness.
Read case study →
LangChain agents for legal contract analysis with custom tools for case-law lookup, citation extraction, and human-in-loop review workflows.
Read case study →What we deliver. LangChain
RAG pipelines with LangChain
Production RAG with hybrid retrieval (vector plus BM25). Re-ranking via Cohere Rerank or cross-encoder for top-k quality. Chunking strategies tuned to your content (semantic chunking, hierarchical chunking, late chunking). Citation extraction so the user sees the source.
LangGraph agent workflows
LangGraph for multi-step agents with explicit state management. State persisted between turns for proper conversation memory. Conditional edges for routing based on tool output. Human-in-loop nodes for steps that need approval. Replay for debugging production agents.
Tool-using agents
Function calling with custom tools (database queries, API calls, file operations, code execution via sandbox). Tool definitions with proper schemas and descriptions. Confirmation flow on side-effectful tools. Audit log of every tool call for compliance.
LangSmith observability
LangSmith traces on every LLM call, retrieval, and tool use. Production datasets sampled to eval datasets for regression testing. Dashboards for cost, latency, and quality. Annotation queues for human review of low-confidence outputs.
Vector store integration
pgvector for Postgres-resident workloads. Pinecone for managed scale. Weaviate or Qdrant for self-hosted. Azure AI Search for Azure-resident. Choice based on existing infrastructure, scale, and operational capability rather than vendor preference.
LangChain to production deployment
Production deploy via LangServe, FastAPI, or custom Node.js / Python service. Streaming responses via Server-Sent Events. Rate limiting per user. Cost guardrails per workspace. Caching for repeated queries. Background queues for long-running agent workflows.
Related capabilities: Cloud DevOps, Azure AI cloud, n8n automation, Workato integration, AI & machine learning, AI chatbot development, Generative AI, NLP development, AI-powered software.
Typical engagement ranges
Cloud setup and CI/CD
From $6,000
- Single LangChain workflow in production with RAG pipeline, eval harness, and LangSmith observability.
- Best for first AI feature going from demo to production.
- 4 to 6 weeks.
Platform engineering
From $9,500
- Multi-workflow LangChain platform with shared retrieval infrastructure, LangGraph agents, prompt versioning, and dataset management.
- Common for SaaS products with multiple AI features.
- 6 to 10 weeks.
Enterprise cloud program
From $21,000
- Enterprise LangChain platform with multi-tenant agent isolation, governance dashboards, advanced eval pipelines, custom tool libraries, and integration with your data warehouse.
- 10 to 18 weeks.
DevOps retainer
From $4,500 / mo
- Model monitoring, prompt updates, eval harness maintenance, cost guardrails, and on-call cover.
FAQ
LangChain wins for breadth of integrations and agent workflows via LangGraph. LlamaIndex wins for retrieval-heavy use cases with sophisticated indexing. Custom wins when you need full control of the stack at scale and the LangChain abstractions are slowing you down. Most production builds in 2026 use LangChain because the ecosystem velocity is highest. We pick per use case.