LangChain Development Services
We use LangChain and LlamaIndex to build RAG chatbots, AI agents and LLM features that hold up in production.
What we build with LangChain
RAG chatbots
Assistants that answer from your documents and data, with sources users can check.
AI agents
Agents that call your APIs and tools to finish multi-step tasks, within clear limits.
Retrieval pipelines
Chunking, embeddings and search tuned to your content, stored in Pinecone or pgvector.
Document processing
Extraction and summaries for contracts, tickets, reports and forms.
Evaluation
Test sets built from real questions, so every change is measured, not guessed.
Fixes and tuning
Faster, more accurate chains for an app you already run.
How we keep LLM apps reliable
A demo is easy. These are the parts that make an LLM feature safe to ship.
Grounded answers
Answers cite their sources, and the app says when it doesn't know.
Model choice on evidence
We test OpenAI and Claude models on your tasks and pick on results.
Guardrails
Input checks, output validation and limits on what agents may do.
Monitoring
Prompts, latency and failures are logged, so problems surface early.
How a LangChain project runs
Step 1: Use case and test set
We agree the task and collect real questions with known good answers.
Step 2: Prototype
A working chain on a sample of your data, so you judge real answers early.
Step 3: Evaluate and tune
Retrieval, prompts and model choice tuned until the test set passes.
Step 4: Ship and monitor
Release with guardrails, logging and alerts, then improve from real use.
OpenAI
Anthropic Claude
Pinecone
pgvector
Python
Node.js
AWSLangChain development FAQs
Do we need LangChain, or can we call the model API directly?
For a single prompt, call the API directly. LangChain earns its place when you add retrieval, tools, memory or several steps, because it gives those pieces a tested structure and makes models easy to swap.
LangChain or LlamaIndex: which should we use?
LlamaIndex is strongest at indexing and retrieving your data; LangChain is broader, with agents and tool use. Many apps use LlamaIndex for retrieval inside a LangChain workflow. We choose after seeing your data and use case.
Which vector database works best with LangChain?
Pinecone and pgvector both work well. pgvector keeps embeddings in a Postgres database you may already run; Pinecone is a managed service that suits large or fast-growing indexes. Data size and latency decide it.
Can you fix or speed up an existing LangChain app?
Yes. We profile the chain, then fix the usual causes: weak retrieval, oversized prompts, too many model calls and missing caching. An evaluation set shows that each change improves answers, not just speed.
Tell us what you're building.
