You will get a production-ready RAG system over your documents
Top Rated
Top Rated
Project details
I build RAG systems with the full architecture layer done properly — not just a basic chatbot over documents. Your system will include reliable document ingestion, tuned chunking, vector search, hybrid retrieval where needed, source citations, relevance scoring, fallback handling, and clear documentation so your team can maintain it confidently after delivery.
AI Development Type
Deep Learning, Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Deeplearning4j, TensorFlow, TheanoAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$650
|
Advanced
$1,100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - |
Optional add-ons
You can add these on the next page.
Chat UI
+$150
Slack / WhatsApp
+$200
Streaming + Citations
+$100
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Complete professional and very diligent. Helped us immensely with his expertise in ML/AI solutions. Was a pleasure to work with and I look forward to another opportunity in the near future.
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Big thanks to Muhammad, he developed the solution straight right what I expected and very quickly as well as always supported me with my questions.
About Muhammad
Production AI Engineer | RAG Systems, AI Agents & LLM Automation
100%
Job Success
Lahore, Pakistan - 5:39 am local time
Most teams reach out after a prototype has already failed in production, usually because of poor chunking logic, absent memory management, weak retrieval quality, or no fallback handling. I rebuild those systems correctly: clean ingestion pipelines, hybrid retrieval, structured agent memory, proper observability, and deployment that holds under real load.
What I build:
— RAG systems for internal documents, knowledge bases, and enterprise search
— AI agents with tool use, memory, and multi-step reasoning (LangChain, LlamaIndex, LangGraph)
— Automation workflows that replace manual operations (n8n + LLM + your existing APIs)
— Production chatbots for web, WhatsApp, and Slack with escalation and analytics
— Voice AI pipelines (STT + TTS) for customer-facing applications
— Fine-tuned models for domain-specific classification and generation tasks
Stack: Python · LangChain · LlamaIndex · LangGraph · OpenAI · Claude API · Pinecone · Weaviate · FastAPI · n8n · AWS · Docker
If you're ready to move from prototype to production or want it built right from the start, I can map out your system architecture in a 20-minute scoping call. No prep needed on your end.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and data sources
I’ll review your documents, use case, expected answer format, data sources, and deployment needs before finalizing the RAG architecture.
Design RAG architecture
I’ll define the ingestion flow, chunking strategy, embedding model, vector store, retrieval method, citations, and fallback logic.