← All Services
AI ML

🦜 LangChain

Building production-grade LLM applications that actually work in the real world

3+ Years Experience
15+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua Flowrite Sparrow Intelligence RightHub

$ cat services.json

RAG System Development

Build retrieval-augmented generation systems that provide accurate, contextual answers from your proprietary data.

Deliverables:
  • Custom document ingestion pipelines
  • Vector store integration (PGVector, Pinecone, Chroma)
  • Hybrid search implementation
  • Citation and source tracking
  • Performance optimization

AI Agent Development

Create autonomous AI agents that can reason, plan, and execute multi-step tasks reliably.

Deliverables:
  • Tool and function calling integration
  • Multi-agent orchestration
  • Human-in-the-loop workflows
  • Structured output validation
  • Error handling and fallbacks

LangChain Integration & Migration

Integrate LangChain into existing systems or migrate from custom implementations.

Deliverables:
  • Legacy system integration
  • API wrapper development
  • Performance benchmarking
  • Documentation and training

$ man langchain

My LangChain Architecture Approach

I don’t just use LangChain—I architect systems that are production-ready from day one. This means:

  • Observability First: Integration with LangSmith/Langfuse for debugging and monitoring
  • Cost Control: Intelligent caching, model routing, and token optimization
  • Reliability: Retry logic, fallback chains, and graceful degradation
  • Scalability: Async processing, queue integration, and horizontal scaling

When to Use LangChain (And When Not To)

LangChain is powerful but not always the right choice. I help you decide:

Use LangChain when:

  • Building complex chains with multiple LLM calls
  • Need built-in integrations with 100+ tools
  • Rapid prototyping with production path

Consider alternatives when:

  • Simple single-prompt applications
  • Extreme performance requirements
  • Minimal dependencies preferred

$ cat README.md

Why LangChain Matters for Your Business

LangChain has become the de facto framework for building LLM applications because it solves the hard problems:

  • Composability: Chain together multiple AI operations reliably
  • Integrations: Connect to 100+ data sources, tools, and LLM providers
  • Observability: Debug and monitor complex AI workflows
  • Community: Massive ecosystem of templates, tools, and best practices

But here’s the catch: LangChain is easy to start with and hard to master. Most tutorials show simple demos that break in production. That’s where my expertise comes in.

My LangChain Stack

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

# Production LangChain Architecture
from langchain_core.runnables import RunnableParallel, RunnableLambda
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import PGVector
from langsmith import Client

# Multi-model routing for cost optimization
router = ModelRouter(
    complex_model=ChatOpenAI(model="gpt-4-turbo"),
    simple_model=ChatOpenAI(model="gpt-3.5-turbo"),
    classifier=TaskComplexityClassifier()
)

# RAG with citation tracking
rag_chain = (
    RunnableParallel(
        context=retriever | format_docs_with_citations,
        question=RunnableLambda(lambda x: x["question"])
    )
    | prompt
    | router
    | StructuredOutputParser(pydantic_model=AnswerWithCitations)
)

Technologies I Integrate With LangChain

  • LLM Providers: OpenAI, Anthropic, Google Gemini, Cohere, HuggingFace, local models
  • Vector Stores: PGVector, Pinecone, Chroma, Weaviate, Qdrant
  • Observability: LangSmith, Langfuse, custom dashboards
  • Frameworks: LangGraph for agents, LangServe for deployment
  • Infrastructure: FastAPI, Redis, PostgreSQL, Docker, Kubernetes

Where I’ve Used LangChain:

Case Studies:

Related Technologies: RAG Systems, AI Agents, OpenAI, Anthropic Claude, Vector Databases, Prompt Engineering

$ ls -la projects/

Enterprise IP Search System

@ Anaqua (RightHub)
Challenge:

Legal teams needed to search millions of patent documents with natural language and get accurate, cited answers.

Solution:

Built a LangChain-powered RAG system with custom chunking for legal documents, PGVector for semantic search, and citation-aware retrieval.

Result:

50% faster search, 99.9% uptime, became key factor in company acquisition.

AI Email Writing Assistant

@ Flowrite
Challenge:

Scale from 10K to 100K users while maintaining response quality and controlling LLM costs.

Solution:

Implemented LangChain with intelligent model routing—GPT-4 for complex emails, Cohere for simple responses.

Result:

40-50% cost reduction, 10x user growth, successful acquisition by MailMerge.

Multi-Agent Document Analysis

@ Sparrow Intelligence
Challenge:

Analyze complex legal documents with multiple specialized AI agents working together.

Solution:

LangGraph-based multi-agent system with specialized agents for extraction, classification, and summarization.

Result:

Reduced document analysis time from hours to minutes.

$ diff me competitors/

+ Built LangChain systems that contributed to two successful acquisitions
+ Experience with enterprise-grade requirements (security, compliance, scale)
+ Deep understanding of LLM internals—not just API calls
+ Full-stack capability—can build the entire backend, not just the AI layer
+ Focus on production reliability, not demo quality

Let's Build Your LLM Application

Within 24 hours

📅 Schedule a Call 📧 Send Email