Knowledge is spread across PDFs, wikis and inboxes
03 / Services / GenAI / RAG
LangChain DevelopmentTurn knowledge into answers.
LangChain-powered AI applications that connect LLMs with APIs, databases, vector stores and business logic to reason, retrieve and automate real workflows.
01 / The problem
What usuallygets in the way.
Chatbot demos hallucinate on real questions
No way to tell whether answers are getting better or worse
02 / What I build
Capabilitiesin this service.
Tool & API Integration
Connect LangChain systems with APIs, CRMs, databases, search tools, and external services for real-world business workflows.
RAG Pipelines
Create retrieval-augmented applications that use documents, vector databases, and retrievers for grounded and context-aware answers.
Memory & Context Management
Enable AI systems to maintain conversation history, task state, and contextual continuity across multi-step interactions.
AI Agents & Workflows
Develop LangChain-powered agents that can reason, choose tools, execute tasks, and automate complex workflows.
Optimization & Observability
Improve reliability with logging, tracing, debugging, evaluation, and workflow optimization for production-ready AI systems.
- Ingestion, chunking and retrieval tuned on your content
- Answers with citations and clear fallbacks
- An evaluation set and quality dashboard
03 / How it works
From documents to cited answers
A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.
Documents
PDFs, pages and tickets, with access rules preserved.
Parsing
Text, tables and structure extracted cleanly.
Chunking
Sections sized for meaning, not a fixed character count.
Embeddings
Vectors that capture what each chunk is about.
Vector database
Fast similarity search with metadata filters.
Retrieval
Hybrid search and re-ranking pick the best context.
LLM
A prompt that answers only from retrieved context.
Answer + citations
Sources shown, so people can verify.
04 / In depth
The details.
Unlocking AI Potential with Expert LangChain Development
As large language models become more powerful, businesses need more than simple chatbots - they need intelligent systems that can reason, retrieve information, use tools, and automate real workflows. I build LangChain-powered AI applications that connect LLMs with APIs, databases, vector stores, and business logic to create scalable, production-ready solutions.
My Expertise in LangChain Development
I specialize in building custom LangChain applications, AI agents, and retrieval workflows for businesses across legal tech, healthcare, e-commerce, SaaS, and other knowledge-driven industries. My work focuses on turning large language models into practical systems that can retrieve information, use tools, maintain context, automate tasks, and support real business operations with reliability and scale.
- 🧠 LLM Applications
- 🔗 Tool & API Integration
- 📚 RAG Pipelines
- 🗂 Memory & Context Handling
- 🤖 AI Agents & Workflows
- ⚙️ Observability & Optimization
LLM Applications
Build intelligent applications powered by LLMs for chat, summarization, extraction, classification, and reasoning-based tasks.
My Expertise in Data & AI Industry
From raw data to AI-powered decisions - I help businesses implement end-to-end machine learning workflows, analytics dashboards, and automation pipelines.
Hire MeRemote Data & AI Solutions
I’m a freelance Data Science and ML Engineer with 7+ years of experience transforming data into actionable insights. I specialize in building intelligent solutions, custom ML models, and scalable pipelines for startups and SMEs worldwide.
- United States (Remote Projects)
- Germany (AI Consultancy)
- EUROPE (Remote Projects)
- Remote - Available Worldwide
You Can find me here
As an independent Data & AI Engineer, I help companies unlock the full potential of their data. From predictive analytics to workflow automation, I offer strategic and technical support that drives real impact.
Book Free Consultation View Portfolio05 / Tech stack
Tools I usefor this work.
- LangChain
- LlamaIndex
- OpenAI
- Claude
- Pinecone
- FAISS
- FastAPI
More in GenAI / RAG
06 / Related work
Proof,not promises.
Medical RAG ChatbotLangChain + Pinecone + Flask
AI-Powered Medical Knowledge Assistant Retrieval-Augmented Generation (RAG) Chatbot
- Python
- LangChain
- Pinecone
- OpenAI
- +2
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Built an end-to-end deep learning MLOps pipeline for bird disease classification using TensorFlow/Keras, PyTorch, OpenCV for image processing, FastAPI for model serving, and Docker for containerization. Implemented automated training pipelines, model versioning, and deployment workflows.
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Developed a complete MLOps pipeline for visa approval prediction using Python, FastAPI, Docker, AWS (EC2, ECR, S3), XGBoost, and CatBoost. Implemented automated model training, versioning, deployment, and monitoring with CI/CD integration for production-grade machine learning.
Uber Data Analytics Pipeline
Built a robust time-series forecasting solution with over 90% accuracy using Python and statistical modeling.It enabled a growing online retailer to better manage inventory, reduce overstock, and forecast seasonal demand shifts.
CareSage - RAG Medical Chatbot
Developed an intelligent medical chatbot using Retrieval-Augmented Generation (RAG) with Flask, LangChain, Pinecone vector database, OpenAI embeddings, and sentence-transformers. The system processes medical PDFs using PyPDF to provide accurate, context-aware responses for healthcare queries with citation support.
n8n Workflow Automation
Built 8 comprehensive automation workflows using n8n platform integrated with Retell AI, GoHighLevel, Twilio, OpenAI GPT-4, and PostgreSQL. Projects included AI Voice Agent for customer service, Multi-Channel Communication Hub, Lead Qualification System, CRM Sync Automation, Content Generation Pipeline, Customer Onboarding Workflow, Voice-Based Appointment Scheduler, and BI Reporting Dashboard.
GenAI / RAG
Need LangChain Development?Let’s scope it together.
Tell me about the goal, the data and the constraints. I’ll reply with questions and a practical first step.