You will get Enterprise RAG System with LLM Integration
Project details
I design and deploy production-ready Retrieval-Augmented Generation (RAG) systems that transform your documents and internal data into intelligent, secure, LLM-powered applications.
This is not a demo chatbot. It is a structured, scalable architecture built for real business use.
The system includes:
• Document ingestion and semantic chunking
• Embedding generation and vector database setup
• Hybrid retrieval (vector + structured data)
• ACL-aware filtering (users access only permitted data)
• Guardrails (PII redaction, restricted topics, grounded responses)
• LLM integration with optimized prompt engineering
• REST API deployment (FastAPI)
• Logging and traceability
Ideal for:
• Internal knowledge assistants
• Secure enterprise search
• Customer support automation
• Executive reporting tools
• Domain-specific AI systems
The architecture is modular, scalable, and designed for production environments with security and performance in mind.
If you need a reliable AI system built correctly from ingestion to deployment, this project delivers a complete solution.
This is not a demo chatbot. It is a structured, scalable architecture built for real business use.
The system includes:
• Document ingestion and semantic chunking
• Embedding generation and vector database setup
• Hybrid retrieval (vector + structured data)
• ACL-aware filtering (users access only permitted data)
• Guardrails (PII redaction, restricted topics, grounded responses)
• LLM integration with optimized prompt engineering
• REST API deployment (FastAPI)
• Logging and traceability
Ideal for:
• Internal knowledge assistants
• Secure enterprise search
• Customer support automation
• Executive reporting tools
• Domain-specific AI systems
The architecture is modular, scalable, and designed for production environments with security and performance in mind.
If you need a reliable AI system built correctly from ingestion to deployment, this project delivers a complete solution.
AI Development Type
Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Keras, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$297
|
Standard
$797
|
Advanced
$1,797
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
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.
Fast Delivery
+$150 - $750
Additional Revision
+$100Frequently asked questions
About Jailson
AI Systems Architect | Machine Learning | RAG & LLM Integration | DS
Fortaleza, Brazil - 10:14 pm local time
I specialize in building scalable AI solutions that integrate directly into business operations, not just models in notebooks, but end-to-end systems.
What I build:
Customer satisfaction modeling systems
Predictive analytics and forecasting pipelines
RAG (Retrieval-Augmented Generation) architectures
LLM-powered reporting and interpretability layers
Hybrid retrieval systems (vector + structured data)
Production-ready ML APIs
I have led enterprise AI initiatives involving:
Large-scale structured and unstructured data processing
Feature importance modeling and explainability
Automated executive reporting using LLMs
Multi-LLM evaluation pipelines
End-to-end data ingestion and orchestration
Technical Stack
Python | SQL | Airflow | Jenkins
FastAPI | Flask | Streamlit | Shiny
Vector Databases | RAG | LLM Integration
Statistical Modeling | Machine Learning | NLP
I work across the full lifecycle:
Data ingestion → modeling → deployment → monitoring.
If you need a reliable AI engineer who can design systems that are scalable, interpretable, and production-ready, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Jailson can start the project.
Delivery time starts when Jailson receives requirements from you.
Jailson works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements + security scope
Define use case, data sensitivity, ACL rules, auth method, and guardrail policies.
Data ingestion + permissions mapping
Ingest documents, apply metadata, and map document/user permissions for retrieval.