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Fiverr Pro
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I will build advanced rag, graph rag and agentic rag with citations
Junaid
À propos de ce service
Generic "chat with your PDFs" tools fall apart on complex enterprise data. You need RAG that handles relationships, multi-hop questions, and ambiguous queries.
I'm Junaid, a UK-based full-stack developer. I build advanced, graph, and agentic RAG systems - including a multi-tenant knowledge base live in my portfolio.
What I build:
Advanced RAG: hybrid retrieval (semantic + keyword + reranking), query rewriting, multi-doc fusion, source citations with confidence scores.
Graph RAG: knowledge graph extraction, entity-aware retrieval, multi-hop reasoning over Neo4j or in-memory graphs.
Agentic RAG: LLM agents that decide what to retrieve, tool use, self-correcting loops via LangGraph.
Every order includes:
- Production-ready system, deployed and live
- Clean, commented source code, 100% yours
- Architecture diagram and setup guide
- 14-day post-delivery bug-fix guarantee
Tech: Next.js, Node.js, FastAPI, OpenAI / Claude, LangChain, LangGraph, ChromaDB / Qdrant
Best fit for enterprise teams, consulting firms, AI startups, and anyone whose docs are too complex for naive RAG.
Message me your use case + sample docs. I'll reply with an architecture proposal within 24 hrs.
Découvrez Junaid
Junaid
AI chatbots, RAG systems and MERN SaaS apps
- DeRoyaume-Uni
- Membre depuisoct. 2025
- Temps de réponse moy.1 heure
Langues
Anglais
I help SaaS founders and ops teams ship AI features that actually work in production — not weekend prototypes.
Recent work:
• AI chatbot — cut support tickets ~40%
• Multi-tenant RAG knowledge base
• Gmail → Slack / Notion automation
• Full-stack AI SaaS MVPs
Stack: React/Next.js, Node.js, FastAPI, MongoDB, OpenAI, Claude, RAG (Chroma/Qdrant), Docker.
London. UK & US time zones. Reply under 2 hrs.
Free 15-min scoping call — message me to start.
Mon portfolio
FAQ
What's the difference between advanced RAG, graph RAG, and agentic RAG?
Advanced RAG improves retrieval with hybrid search and reranking. Graph RAG models entity relationships for multi-hop questions like 'which engineer worked on which project'. Agentic RAG uses LLM agents to decide retrieval and reason in steps. I'll choose based on your data.
Which type do I need?
Independent docs and direct queries → Advanced RAG. Data with relationships (org charts, products linked to suppliers) → Graph RAG. Queries needing reasoning across multiple sources → Agentic RAG. Share sample queries and I'll recommend the right one.
What document formats do you support?
PDFs, Word, plain text, Markdown, HTML pages, Notion exports, Confluence exports, Google Docs (via export), and any source with an API.
Is my data secure?
Yes. Your data runs on infrastructure you control. No training data is sent to OpenAI / Anthropic for model training (only inference). I can deploy fully air-gapped using open-source models (Llama, Mistral) on request.
Can you improve or migrate an existing RAG implementation?
Yes — I offer architecture audits and migration as a Premium add-on. Send me your repo or stack and I'll quote.
