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I will build custom rag pipeline and pinecone vector database hubs
Faraz Ahmed
Über diesen Service
Are you tired of your AI chatbot hallucinating or failing to understand your internal company data? Standard LLMs don't know your businessuntil you connect them to a custom vector knowledge base.
As a Full Stack AI Engineer, I architect secure, high-performance Retrieval-Augmented Generation (RAG) pipelines that turn your private PDFs, CSVs, Notion pages, and databases into structured, searchable intelligence.
What I Do:
- Production RAG Pipelines: Custom chunking strategy and hybrid search (semantic + keyword) for ultra-accurate model responses.
- Vector Database Integration: Production-grade setup using Pinecone, ChromaDB, or Qdrant.
- Data Ingestion & Connectors: Securely pipeline enterprise data using LangChain and LlamaIndex.
- Front-End Dashboards: (Premium) Sleek Next.js or React dashboards for complete document management and monitoring.
Why Me?
I build production-ready Python architectures with data privacy in mind. No cookie-cutter wrappersjust secure internal knowledge hubs that scale.
Bring accuracy to your enterprise AI data. Please contact me to review your custom blueprint before placing an order!
Lerne Faraz Ahmed kennen
Faraz Ahmed
Full Stack AI Engineer, React, NestJS, Python Agents
- AusPakistan
- Mitglied seitJuni 2020
- ⌀ Antwortzeit1 Stunde
Sprachen
Urdu, Englisch
I am an AI Integration Specialist focused on helping businesses automate their workflows using RAG and LLMs. With a background in modern web stacks (MERN) and a passion for AI Agents, I bridge the gap between complex AI technology and practical business solutions
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FAQ
How do you handle data privacy and security in the RAG pipeline?
Data security is the highest priority. I set up the pipeline to keep your data encrypted, using secure enterprise API connections and local/cloud vector storage (like Pinecone private indexes). Your private files are never used to train public LLM models.
What vector databases do you recommend for enterprise data?
I primarily work with Pinecone for fully managed cloud scaling, ChromaDB for lightweight or local deployments, and Qdrant for advanced vector search. The choice depends entirely on your current data infrastructure and budget.
