You will get Production-ready RAG system architecture for enterprise knowledge retrieval
Rising Talent
Project details
You will get a production-grade AI system design tailored to your business use case, focused on Retrieval-Augmented Generation (RAG), multi-agent workflows, and scalable AI automation. The goal is not a prototype, but a structured architecture that can operate reliably in real production environments with real data, constraints, and user traffic.
With a background in building enterprise AI systems, I specialize in translating complex business requirements into clear, scalable system architectures. This includes defining data flows, model selection, retrieval strategies, orchestration layers, and evaluation frameworks to ensure reliability, cost efficiency, and maintainability.
Each solution is designed with production constraints in mind, including latency, failure handling, and integration into existing systems such as APIs, databases, and cloud infrastructure. The deliverable is a complete architecture package that can be directly used for implementation by engineering teams.
With a background in building enterprise AI systems, I specialize in translating complex business requirements into clear, scalable system architectures. This includes defining data flows, model selection, retrieval strategies, orchestration layers, and evaluation frameworks to ensure reliability, cost efficiency, and maintainability.
Each solution is designed with production constraints in mind, including latency, failure handling, and integration into existing systems such as APIs, databases, and cloud infrastructure. The deliverable is a complete architecture package that can be directly used for implementation by engineering teams.
AI Algorithms
Feedforward Neural Network, Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorchAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 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
About Javad
LLM Systems Architect | RAG, AI Agents & Enterprise Automation
Hamburg, Germany - 8:17 am local time
My work is centered around building reliable AI systems that operate in real-world environments where accuracy, scalability, and system stability matter more than prototypes or demos.
I design and build production-grade LLM systems focused on Retrieval-Augmented Generation (RAG), multi-agent workflows, and enterprise AI automation.
Core Areas of Work
AI oriented RevOps. Guaranteeing a unified marketing-sales and operation agentic processes
Multi-agent AI systems for workflow automation and decision support
AI orchestration layers for integrating and managing multiple models
Production RAG systems for enterprise knowledge and data retrieval
LLM-based document intelligence and structured data extraction systems
Evaluation and reliability frameworks for LLM output consistency
Engineering Approach
I design systems from architecture first, focusing on:
Identical business mapping
data flow design before implementation
cost and latency constraints
long-term maintainability of AI systems
My focus is not on building isolated AI features, but on delivering complete, production-ready orchestrations that integrate into real operational environments.
Steps for completing your project
After purchasing the project, send requirements so Javad can start the project.
Delivery time starts when Javad receives requirements from you.
Javad works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & System Analysis
Understand business use case, data sources, constraints, and success criteria. Define system scope and integration requirements.
Architecture Design
Design end-to-end system architecture including data flow, model selection, retrieval strategy, and orchestration layer.