LangChain Experts:
From Prototype to Production AI
LangChain is the framework most teams reach for when building LLM-powered applications, and it's the one we use most too. We take projects past the notebook stage into systems that actually run: RAG pipelines grounded in your data, multi-step chains for workflows with real branching logic, and agents with memory that keep working once real users show up.
What LangChain Enables in Production
LangChain turns raw LLM API calls into applications you can actually maintain and test. Prompt templates, output parsers, retrieval chains, memory modules, and tool-calling agents are all composable pieces you assemble rather than code you write from scratch each time.
Skip that layer and LLM applications tend to turn into a mess: prompt strings scattered across files, retry logic bolted on wherever it's needed, output handling that's slightly different in every function. LangChain gives the project a shape before that happens.
Systems We Architect
RAG Pipelines
Retrieval-Augmented Generation systems that ground LLM responses in your proprietary documents, databases, or APIs, so the model answers from what you actually have on file instead of guessing.
Multi-Step Evaluation Chains
Sequential LLM chains that break complex tasks into structured steps: extract, classify, validate, summarize, score. Each step has typed inputs and outputs you can check independently.
Memory-Enabled Agents
Conversational agents with persistent short and long-term memory. Users can reference earlier interactions, and the system builds up context about each one over time.
Tool-Calling Orchestration
Agents that call your existing APIs, query databases, run calculations, and combine the results into one answer, instead of you writing that glue code by hand for every workflow.
LangChain Components We Specialize In
Document Loaders & Splitters
Ingest PDFs, Word files, web pages, databases, and APIs into document chunks sized for retrieval. We pick splitting strategies that keep a paragraph or table intact instead of cutting it in half.
Vector Store Integration
Connect to Pinecone, Chroma, Weaviate, pgvector, and BigQuery Vector Search. We design the embedding strategy, indexing pipeline, and retrieval logic for your document corpus.
Prompt Templates & Chains
Version-controlled, parameterized prompt templates that keep business logic out of the LLM calls themselves, so swapping models or tweaking a prompt doesn't mean touching application code.
Output Parsers
Structured extraction using Pydantic schemas and JSON output parsers. LLM responses come back as typed Python objects with validation, not raw strings you have to regex apart.
Memory Modules
Conversation buffer, summary, entity, and vector store memory. We match the memory type to how the use case actually needs to recall things.
LangSmith Observability
Trace every LLM call, chain execution, and agent step in LangSmith. Debug production issues, evaluate prompt changes, and monitor accuracy metrics over time.