You will get a custom graph-vector rag application using azure/gcp/aws
Project details
Standard RAG systems often fail because they retrieve text based on fuzzy similarity, missing the hard facts and hidden relationships. I build Hybrid Graph-Vector RAG systems that fix this.
By combining the speed of Vector Databases (Qdrant/Pinecone) with the reasoning power of Knowledge Graphs (Neo4j), I create AI agents that know the structure of your data.
By combining the speed of Vector Databases (Qdrant/Pinecone) with the reasoning power of Knowledge Graphs (Neo4j), I create AI agents that know the structure of your data.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AIOps, Conversational AI, Image Analysis, Image Processing, Natural Language Generation, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Bing AI, GitHub Copilot, Hugging Face, Microsoft CNTKAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$80
|
Standard
$320
|
Advanced
$730
|
|---|---|---|---|
| Delivery Time | 2 days | 7 days | 14 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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$40 - $315
Additional Revision
+$40Frequently asked questions
About Ronit
AI Architect | Azure Certified (DP-100,AI-102) | LLMs & RAG Systems
Gurgaon, India - 12:25 am local time
I am a Microsoft Certified Engineer (DP-100,AI-102) who steps in when "trying it out" isn't enough. I take ownership from Raw Data to Deployed API, ensuring your system is robust, compliant, and profitable and secure.
go to my website, ronitsaxena/.in (remove spaces)
Steps for completing your project
After purchasing the project, send requirements so Ronit can start the project.
Delivery time starts when Ronit receives requirements from you.
Ronit works on your project following the steps below.
Revisions may occur after the delivery date.
Data Ingestion & Graph Construction
I will clean your raw data and build the extraction pipeline. I use LLMs to identify entities/relationships and populate the Knowledge Graph (Neo4j) while simultaneously generating vector embeddings for the vector store (Qdrant/Pinecone).
Hybrid RAG Pipeline Development
I will engineer the retrieval logic. This involves setting up a hybrid search that queries both the Vector DB (for semantic similarity) and the Knowledge Graph (for structured relationships) to feed the LLM accurate context.