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You will get Custom RAG System with GPT-4o for Private AI Search & Knowledge Retrieval
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Project details
Unlock the power of your internal knowledge with a fully customized RAG (Retrieval-Augmented Generation) system built on GPT-4o, designed to deliver accurate, private, and context-aware answers from your documents, databases, and internal resources.
Unlike generic chatbots, this solution is engineered for zero-hallucination performance, advanced semantic search, and robust data privacy, tailored specifically for your organization’s workflows. I design everything end-to-end, including data preprocessing, chunking strategy, vector database setup, embedding optimization, pipeline engineering, and GPT-4o integration.
Whether you need a private company knowledge assistant, an AI customer support engine, a research search tool, or an automated Q&A system, your RAG pipeline will be scalable, secure, and deployment-ready. You’ll receive full documentation and support for seamless adoption.
Unlike generic chatbots, this solution is engineered for zero-hallucination performance, advanced semantic search, and robust data privacy, tailored specifically for your organization’s workflows. I design everything end-to-end, including data preprocessing, chunking strategy, vector database setup, embedding optimization, pipeline engineering, and GPT-4o integration.
Whether you need a private company knowledge assistant, an AI customer support engine, a research search tool, or an automated Q&A system, your RAG pipeline will be scalable, secure, and deployment-ready. You’ll receive full documentation and support for seamless adoption.
AI Algorithms
Autoencoder, Deep Belief Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, AI Content Creation, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Sentiment Analysis, Sequence Modeling, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, BLOOM, ChatGPT, GPT-3, GPT-4, GPT-J, GPT-Neo, Jurassic-2, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$300
|
Standard
$750
|
Advanced
$1,400
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code |
Frequently asked questions
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FE
Farzad E.
Apr 3, 2026
Algorithm Development for Biosignal Related Data
Very conscientous and easy to communicate with.
FP
Farrukh P.
Sep 18, 2025
Build an AI Agent to Automate Business Report Creation
Highly recommend him for any AI or full-stack project that requires both technical depth and business understanding.
About Zaman
AI Engineer | AI Agents & Automation | RAG, n8n, GenAI | US-Based
100%
Job Success
Princeton, United States - 8:37 pm local time
Most AI developers hand you a ChatGPT wrapper and call it a product. I build the system underneath the retrieval layer, the agent logic, and the eval pipeline. So, your AI actually works on your data, at scale, in production.
I'm an AI Engineer focused on RAG systems, LLM agents, AI agents, AI automations and AI-powered SaaS. I work with founders and teams who need their AI to do real work.
𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱:
✅𝗥𝗔𝗚 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Custom retrieval pipelines that ground LLMs in your private data, with proper chunking, hybrid search, and eval loops. Stack: LangChain, LlamaIndex, Pinecone, Weaviate, pgvector.
✅𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Multi-step agents that replace manual workflows, from research and outreach to internal ops. Stack: OpenAI, Claude, n8n, Make, Zapier, custom Python orchestration.
Stack: Python, FastAPI, NodeJS, NextJS, React, Postgres.
✅𝗔𝗜 𝗦𝗮𝗮𝗦 𝗠𝗩𝗣𝘀: Python, FastAPI, SaaS Development, AI App Development
✅𝗟𝗟𝗠 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Large Language Model, GPT-4, Generative AI, Prompt Engineering
𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸:
I scope before I code. You get a clear architecture doc, weekly demos, and clean modular code your next engineer can pick up without a tour. Same-day response on weekdays, full overlap with US Eastern hours.
If you have a half-formed AI idea, a stalled MVP, or a workflow you want autonomous, send me your scope and I'll tell you honestly whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Zaman can start the project.
Delivery time starts when Zaman receives requirements from you.
Zaman works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Data Audit Description
I will review your documents, content sources, use cases, and integration needs. This ensures the foundation of your RAG system is tailored to your workflow.
Data Cleaning & Chunking Pipeline Setup
I will clean, preprocess, and chunk your documents using optimized strategies for accuracy, context retention, and retrieval performance.