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I will build an ai rag chatbot with llm, langchain, vector database and mlops
Wasi Ahmad
À propos de ce service
I build RAG chatbots and AI systems powered by large language models, backed by real MLOps engineering.
Need an AI chatbot for customer service, an e-commerce chatbot, or an internal chatbot that answers questions from your own documents? I build RAG chatbots that connect large language models to your data using retrieval augmented generation. This means your chatbot gives real, accurate answers instead of guessing.
I set up vector databases like Pinecone and tune search so your RAG chatbot finds the right answer, not just a similar one. I use LangChain and Hugging Face for the AI layer. I also bring real MLOps skills. I build CI/CD pipelines and automatic testing, and deploy using FastAPI, so your chatbot stays reliable after launch, not just during a demo.
What I offer:
RAG chatbot development using LLMs, including OpenAI and open source models
Vector database setup and search tuning, including Pinecone
LangChain and Hugging Face integration
MLOps pipelines with CI/CD and automatic testing
FastAPI deployment
Cleanup and documentation for messy chatbot and ML systems
I currently support live chatbot systems handling real user traffic in production. I also built and published
Découvrez Wasi Ahmad
Wasi Ahmad
Software Engineer: AI, MLOps
- DeBangladesh
- Membre depuisaoût 2026
Langues
Bengali, Anglais
I build AI chatbots and machine learning pipelines that work in the real world. I create RAG chatbots that pull the right answers using smart vector search. I also build CI/CD pipelines so ML projects run smoothly after launch. At Nascenia, I support government chatbots and improved their retrieval speed. I built ccllm, a published Python package for compression and retrieval, with its own API and automatic testing. I have a degree in Electrical and Computer Engineering and published research in AI. If you need a chatbot that actually works, let's talk.
Mon portfolio
FAQ
What is a RAG chatbot?
A RAG chatbot uses retrieval augmented generation to connect a large language model to your own data. Instead of guessing, it looks up real information first, then generates an accurate answer based on what it finds.
Can you build a chatbot that uses my own documents or data?
Yes. I connect the chatbot to your documents, PDFs, or database using a vector database like Pinecone, so it answers questions using your actual content instead of general knowledge.
What AI models and tools do you use?
I work with OpenAI models and open source LLMs, using LangChain and Hugging Face for the AI layer, and Pinecone for vector search and retrieval.
Do you offer MLOps services, not just chatbot development?
Yes. I build CI/CD pipelines, set up automatic testing, and handle deployment with FastAPI, so your AI system stays reliable after launch, not just in testing.
Can this chatbot work for customer service or e-commerce?
Yes. I build RAG chatbots for customer service, e-commerce product questions, and internal knowledge base search, so buyers get accurate answers from your real content.
How is a RAG chatbot different from a regular chatbot?
A regular chatbot often relies only on the AI model's general training and can guess or make things up. A RAG chatbot retrieves real data first, so its answers are grounded in your actual information.
Do you fix or improve an existing chatbot?
Yes. I review existing chatbot and machine learning systems, clean up messy code, and improve accuracy, speed, or reliability, including systems with little or no documentation.
What do you need from me to get started?
I typically need your data source, such as documents, a website, or a database, along with your goal for the chatbot, such as customer support or internal search, to design the right RAG setup.
How long does a RAG chatbot project take?
A basic RAG chatbot setup usually takes a few days, while a full system with vector database tuning, testing, and deployment takes longer depending on your data size and requirements.
