You will get a production-ready AI RAG search system
Project details
Tired of keyword search that misses the point? I build production-ready RAG (Retrieval-Augmented Generation) systems that let your users ask real questions and get accurate, context-aware answers — pulled directly from your own data.
I specialize in end-to-end RAG pipelines: document ingestion, embedding generation, vector storage, and LLM-powered response — all wired together into a clean, deployable system. Whether you need an internal knowledge base search, a customer-facing chatbot, or a document Q&A tool, I deliver working code, not prototypes.
What sets this apart: I focus on retrieval quality — proper chunking strategies, metadata filtering, and reranking — so your search actually returns relevant results, not just semantically close noise.
Built with LangChain, LlamaIndex, OpenAI/Anthropic/Open source models, Qdrant — or your preferred stack.
I specialize in end-to-end RAG pipelines: document ingestion, embedding generation, vector storage, and LLM-powered response — all wired together into a clean, deployable system. Whether you need an internal knowledge base search, a customer-facing chatbot, or a document Q&A tool, I deliver working code, not prototypes.
What sets this apart: I focus on retrieval quality — proper chunking strategies, metadata filtering, and reranking — so your search actually returns relevant results, not just semantically close noise.
Built with LangChain, LlamaIndex, OpenAI/Anthropic/Open source models, Qdrant — or your preferred stack.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Deeplearning4j, Google AutoML, Keras, MLflow, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, PyTorch, TensorFlow, TheanoAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$150
|
Standard
$400
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Video walkthrough
(+ 5 Days)
+$100
Existing System Integration
(+ 3 Days)
+$250
Custom Web Interface
(+ 4 Days)
+$350Frequently asked questions
About MD Shahriar
AI Engineer | RAG & LLM Systems | Full-Stack & API Developer
Dhaka, Bangladesh - 12:53 pm local time
I combine strong engineering fundamentals with practical AI expertise to deliver solutions that actually scale. Whether it's architecting intelligent search systems, integrating LLMs into production, or building full-stack applications, I focus on results that matter to clients.
Steps for completing your project
After purchasing the project, send requirements so MD Shahriar can start the project.
Delivery time starts when MD Shahriar receives requirements from you.
MD Shahriar works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Planning
Review your data sources, use case, and stack. Confirm scope and deliver a technical plan.
Data Ingestion & Chunking
Process and chunk your documents, generate embeddings, and load into vector database.