LangChain is a framework, not a finished product — it still takes engineering judgment to turn chains and agents into something reliable under real traffic. We staff developers who've shipped LangChain and LlamaIndex projects past the prototype stage, with the orchestration, error handling, and cost discipline that production requires.
We use LangChain where it earns its complexity and drop down to direct API calls where a chain would just add overhead.
Tool calls fail, APIs time out, models hallucinate arguments — we build retry logic and fallbacks so one bad step doesn't break the run.
Chunking, embeddings, and retrieval strategy are chosen for your documents, not copied from a generic tutorial.
For document-heavy retrieval use cases, we'll recommend LlamaIndex over LangChain when it's the simpler, more maintainable choice.
You get a scoped deliverable — a working agent or pipeline — with a fixed estimate, not an open-ended hourly clock.
Multi-step agents that call tools and APIs reliably, with structured error recovery.
Document ingestion, chunking, embeddings, vector store, and retrieval logic end to end.
Testing outputs against real queries, not just a handful of demo prompts.
Conversation history and session state handled correctly across multi-turn interactions.
Taking over an existing LangChain codebase and keeping it stable through framework version changes.
Most commonly: AI agents that use tools and APIs to complete multi-step tasks, RAG systems that answer questions from your own documents, and chatbots with memory that hold a coherent conversation across turns.
Yes. We design the chunking strategy, embedding model, and vector store around your actual documents and query patterns, rather than defaulting to a generic template.
LangChain adds structure for chaining prompts, managing memory, connecting tools, and swapping models — useful once your logic gets complex. For a single prompt-response call, a direct API call is often simpler and we'll say so.
Yes. We regularly take over LangChain codebases — auditing the current chains and agents, fixing fragile error handling, and keeping the project stable through framework updates.
Tell us what your agent or RAG pipeline needs to do — we'll scope the engineering work and a fixed estimate.