You will get Production-Ready RAG & LLM System for Your Business
Project details
I will design and develop a production-ready Retrieval-Augmented Generation (RAG) system tailored to your business needs. This solution transforms your documents, databases, or knowledge sources into an intelligent AI assistant using advanced LLMs and vector databases.
What sets me apart is my focus on real-world deployment, scalability, and observability — not just prototype demos. I integrate semantic search, optimized retrieval pipelines, API architecture, and monitoring (Prometheus/Grafana) to ensure your system is reliable and enterprise-ready.
With hands-on experience building RAG systems for legal document review, product recommendation engines, and backend AI integrations, I deliver clean, modular, and deployment-ready solutions.
You receive secure architecture, production-grade code, and a scalable AI system designed for long-term use.
What sets me apart is my focus on real-world deployment, scalability, and observability — not just prototype demos. I integrate semantic search, optimized retrieval pipelines, API architecture, and monitoring (Prometheus/Grafana) to ensure your system is reliable and enterprise-ready.
With hands-on experience building RAG systems for legal document review, product recommendation engines, and backend AI integrations, I deliver clean, modular, and deployment-ready solutions.
You receive secure architecture, production-grade code, and a scalable AI system designed for long-term use.
AI Algorithms
AdaBoost, Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Gated Recurrent Unit, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Image Analysis, Image Recognition, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, Microsoft 365 Copilot, Replit, Streamlit, TensorFlow, Word2vecAI Models
ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$300
|
Standard
$500
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 14 days |
Number of Revisions | 2 | 3 | 5 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300 - $700Frequently asked questions
About Nit
Reliability-First LLM Engineer | Production RAG, Validation Layers
Surat, India - 2:42 am local time
My focus is not just building LLM features — it is engineering AI backbones that are:
• Grounded (RAG with structured asset isolation)
• Resistant to hallucination and cross-contamination
• Equipped with validation and confidence scoring layers
• Modular and extensible for long-term scale
In 2026, I proposed a 4-layer AI router architecture for a maintenance diagnostics platform (asset isolation, hybrid RAG grounding, dual-pass validation, and confidence scoring). The client described the proposal as “production-ready and future-proof” and noted that it significantly refined their MVP direction.
Core Capabilities
• Advanced RAG pipelines (hybrid PDF + curated knowledge libraries)
• Multi-asset isolation routing
• Dual-pass LLM validation & scoring
• Abstain/retry logic & safety fallback systems
• FastAPI / API-first AI integration
• Structured JSON outputs for downstream systems
• Agentic diagnostic workflows
I prioritize:
Reliability over novelty.
Clarity over overengineering.
Production-readiness over demos.
If you’re building an AI system where factual grounding, modularity, and failure control matter — I can help design and implement the backbone correctly from day one.
“Highly recommend for any AI reliability or agentic system project — 5 stars.” (Client feedback, 2026)
Steps for completing your project
After purchasing the project, send requirements so Nit can start the project.
Delivery time starts when Nit receives requirements from you.
Nit works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Planning
Client shares use case, data type, deployment preference, and technical constraints. I define architecture and confirm scope.
System Architecture & Setup
Design RAG pipeline, embedding strategy, vector database configuration, and LLM integration plan.