You will get Custom Enterprise AI RAG Knowledge Assistant for Your Data

Tanvirul I.Status: Offline
Tanvirul I.
5.0

Let a pro handle the details

Buy Other AI & Machine Learning services from Tanvirul, priced and ready to go.
Tanvirul I.Status: Offline
Tanvirul I.
5.0

Let a pro handle the details

Buy Other AI & Machine Learning services from Tanvirul, priced and ready to go.

Project details

Unlock the true potential of your corporate data with a production-grade, highly secure Retrieval-Augmented Generation (RAG) system. I build enterprise AI assistants that don't just chat, but synthesize accurate, context-aware answers grounded directly in your private databases, PDFs, and internal handbooks—with exact source citations to prevent hallucinations.

What sets this service apart:
 • Hybrid Search: Combining keyword (BM25) and dense vector embeddings for maximum precision.
 • Advanced Architecture: Implementation of semantic chunking, metadata filtering, and reranking pipelines.
 • GraphRAG Ready: Integrating Neo4j/knowledge graphs to capture complex entity relations (Advanced Tier).
 • Complete Handoff: Clean, fully commented Python code (LangChain/LlamaIndex) and detailed system documentation.

With extensive experience developing production-grade AI applications, I deliver scalable, secure, and evaluation-tested solutions tailored to your unique workflows. Let's transform your unstructured documents into an intelligent corporate oracle.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software Maintenance
AI Tools
Amazon SageMaker, MLflow, OpenCV, PyTorch, TensorFlow
AI Development Language
Python
What's included
Service Tiers Starter
$700
Standard
$2,500
Advanced
$5,000
Delivery Time 5 days 14 days 25 days
Number of Revisions
233
AI Model Integration
Detailed Code Comments
Knowledge Graph
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Model Documentation
-
Ontology
-
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Source Code
Taxonomy
-

Frequently asked questions

5.0
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MR

MD Mahabur R.
5.00
Jul 18, 2026
Software Engineer | AI Agent Development Excellent AI Software Engineer! Delivered a high-quality AI agent solution with great professionalism and clear communication. Everything worked as expected, and I'll definitely hire again for future AI projects.
Tanvirul I.Status: Offline

About Tanvirul

Tanvirul I.Status: Offline
Senior Software Engineer with specializing in AI Agent Development
5.0  (1 review)
Dhaka, Bangladesh - 6:10 am local time
Software Engineer and AI/ML practitioner with experience designing and delivering scalable web applications, robust backend systems, and intelligent AI-powered products—spanning Retrieval-Augmented Generation (RAG) systems, LangGraph-based agent orchestration, Model Context Protocol (MCP) integrations, and autonomous AI agents. Deep expertise across Java, Python, TypeScript, and the Grails, FastAPI, Django, and Flask frameworks, with a strong track record of architecting maintainable systems and optimizing complex, high-traffic applications. Experienced in designing and implementing RAG pipelines using vector databases, embedding models, semantic search, document ingestion, and retrieval optimization to build knowledge-aware AI applications. Hands-on experience building stateful, multi-agent AI workflows with LangGraph, enabling tool calling, workflow orchestration, memory management, and complex decision-making across LLM-powered systems. Specializes in applied machine learning, deep learning, and NLP, with practical experience across the full LLM lifecycle—including instruction tuning, reward modelling, RLHF (PPO), and DPO—as well as designing agentic systems, MCP integrations, and AI workflows that connect LLMs to enterprise data, external tools, and real-world business processes. Passionate about translating cutting-edge AI research into dependable, high-performance solutions that deliver measurable business impact.

Steps for completing your project

After purchasing the project, send requirements so Tanvirul can start the project.

Delivery time starts when Tanvirul receives requirements from you.

Tanvirul works on your project following the steps below.

Revisions may occur after the delivery date.

Data Ingestion, Chunking & Embedding Setup

I will analyze your sample documents, design an optimal parsing and chunking strategy (semantic or hierarchical), generate vector embeddings, and store them securely in the vector database.

Retrieval Pipeline & LLM Orchestration

I will test retrieval accuracy using Ragas metrics, wrap the engine in a clean interface (Streamlit, Chainlit, or a custom API), and hand over the fully commented source code and documentation.

Review the work, release payment, and leave feedback to Tanvirul.