You will get Custom RAG System Built on Your Company Data
Project details
Most RAG systems fail in production because retrieval is broken, chunks are wrong, or the model ignores the context entirely. I build RAG systems that actually work, accurate retrieval, grounded responses, zero hallucinations, built directly on your company documents and data.
With 4+ years building production AI systems and a 100% Job Success Rate on Upwork, I deliver clean, documented code that your team can maintain and scale.
With 4+ years building production AI systems and a 100% Job Success Rate on Upwork, I deliver clean, documented code that your team can maintain and scale.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging FaceAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$149
|
Standard
$299
|
Advanced
$599
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 12 days |
Number of Revisions | 2 | 3 | 5 |
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 |
Frequently asked questions
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AS
Ahmed S.
Jun 12, 2026
AI Developer Needed for Document-Based Q&A System (RAG MVP)
Great work. Hassan delivered a working RAG MVP on time and the solution performed well on our real data. Communication was clear throughout and the process was smooth. Highly recommend.
JA
Jhi A.
Mar 26, 2026
AI Automation - Reddit Research
Highly recommend. Went above and beyond and did everything I wanted. Thank you
AS
Ahmed S.
Mar 10, 2026
Optimize OCR accuracy for Pdf based RAG System
Hassan did an excellent job optimizing our OCR pipeline for a PDF-based RAG system. He significantly improved document text extraction, which helped increase the overall accuracy of our RAG workflow. He has strong expertise in RAG systems and OCR processing, communicates clearly, and delivered high-quality work on time. Highly recommended for AI, document processing, and RAG-related projects.
About Hassan
AI Engineer | RAG Expert | AI Agents | Document AI & OCR | Gen AI
100%
Job Success
Islamabad, Pakistan - 9:22 pm local time
Over 4+ years, I've built AI systems including RAG pipelines, document automation, Generative AI applications, MVPs, and AI agents that handle real-world data, edge cases, and production load.
✅ 100% Job Success Rate
✅ All 5-Star Reviews
✅ Free 15-minute technical audit on every new project
WHAT I BUILD:
1. RAG Systems & Knowledge Bases
- Most RAG projects fail at retrieval, not the LLM. I fix the actual problem: chunking, embedding, reranking.
- Every response grounded in your real data. No hallucinations, no guessing.
2. Document AI & OCR
- Scanned PDFs, bad image quality, messy layouts. I've seen it all and built pipelines that handle it.
- Clean, structured output from documents your current tools can't process.
3. Generative AI Apps & MVPs
- If you have an AI idea and need it built properly, I can take it from concept to working product.
- Clean architecture from day one so you're not rewriting everything 3 months later.
4. AI Agents & Automation
- Agents that actually work in production, not just in demos. Built to handle the edge cases you didn't plan for.
- Full automation with LangGraph, n8n, and MCP. Every step logged and traceable.
WHY WORK WITH ME
✅ Production-first → I build for real-world data, not ideal scenarios.
✅ Clean code → Documented, tested, zero technical debt on every project.
✅ No surprises → Transparent communication, no scope creep without discussion.
✅ Full ownership → Architecture to deployment.
TECH STACK:
LLMs: OpenAI · Anthropic Claude · Google Gemini · LLaMA · Mistral · DeepSeek
Frameworks: LangChain · LangGraph · LlamaIndex · FastAPI · Django
Vector DBs: Pinecone · ChromaDB · FAISS · pgvector · Weaviate
Databases: PostgreSQL · MongoDB · Redis
Languages: Python · TypeScript · JavaScript
Automation: n8n · MCP · Custom Pipelines
DevOps: Docker · GitHub Actions · GitLab CI/CD · AWS · GCP
WHO I WORK BEST WITH:
→ Founders who tried a demo and now need a production-ready system
→ Teams dealing with real documents, messy data, or broken pipelines
→ Startups that need an AI MVP built fast and built right
→ Businesses that want a technical partner, not just a developer
LET'S BUILD IT RIGHT:
Message me with your challenge. I offer a free 15-minute technical audit so we can identify your exact bottleneck before any work begins.
What's your biggest AI challenge right now?
Steps for completing your project
After purchasing the project, send requirements so Hassan can start the project.
Delivery time starts when Hassan receives requirements from you.
Hassan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Document Review
I review your documents, data sources, and sample queries to plan the right architecture.
Ingestion & Chunking Pipeline
Build document parsing, smart chunking, and embedding pipeline for accurate indexing.