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English
$
USD
I will build graphrag, rag and neo4j ai applications
Shaza
About this gig
Need a GraphRAG system that actually works beyond demos?
I build production-ready GraphRAG solutions using Neo4j and modern LLM infrastructure for search, analysis, and intelligent knowledge retrieval.
What you get:
- GraphRAG architecture and implementation
- Neo4j integration and graph modeling
- Data ingestion and preprocessing
- Retrieval and query pipelines
- API integration
- Dockerized deployment
- Documentation and setup guidance
Use cases:
- Internal knowledge assistants
- Threat and incident analysis
- Enterprise search
- Relationship discovery
- AI-powered analytics
Tech stack available:
Neo4j Python Docker Streamlit vLLM RAG pipelines
Before ordering, send your requirements and I'll help define the best architecture for your use case.
Get to know Shaza
Shaza
- FromPakistan
- Member sinceOct 2019
- Last delivery4 years
Languages
English
Hi, I’m Shaza!
I'm an AI Engineer focused on building production-ready AI systems that go beyond prototypes.
I specialize in:
• RAG & GraphRAG systems
• Neo4j and knowledge graphs
• Local LLM & vLLM deployment
• Dockerized AI infrastructure
• Streamlit AI applications
• Performance optimization
I deliver clean architecture, scalable deployments, clear documentation, and reliable execution.
Whether you need an AI assistant, internal search, GraphRAG pipeline, or end-to-end deployment, I build practical AI solutions designed for real-world use.
FAQ
What information do you need before starting?
Please share your use case, data sources, preferred LLM/provider, deployment requirements, and expected outcomes. If unsure, I can help define the architecture.
Do you work with existing datasets and databases?
Yes. I can integrate existing structured and unstructured data into GraphRAG pipelines and connect supported databases.
Which technologies do you use?
Depending on requirements, I work with Neo4j, Python, Docker, Streamlit, vLLM, RAG pipelines, APIs, and modern AI tooling.
Can you deploy the solution?
Yes. Deployment options can include local environments, Dockerized setups, servers, and production-ready configurations depending on the package.
Do you offer support after delivery?
Yes. Post-delivery support and additional improvements can be arranged if needed.
What if I don’t know which package to choose?
Send me your requirements and I’ll recommend the best package before ordering.
Can you work with custom LLMs or local models?
Yes. I can support local and hosted model deployments depending on compatibility and project requirements.
Is model fine-tuning included?
No. This gig focuses on GraphRAG architecture and retrieval systems. Fine-tuning can be discussed separately if required.
Will the system work with private/internal company data?
Yes, GraphRAG systems can be designed to work with internal knowledge bases and private datasets.
