You will get RAG: Production RAG System — 85% Accuracy, <5% Hallucination Rate
Project details
Most RAG systems work in the demo.
Mine work in production.
At 3D Smart Factory, I built and deployed a hybrid RAG agent
evaluated on 150 real production queries:
→ 85% global accuracy
→ Under 5% hallucination rate
→ Adopted for daily use by the company director
That system didn't come from tutorials. It came from building
the eval layer after watching an earlier version fail silently
— wrong answers, no alerts, no crash.
What you get:
— Hybrid retrieval (semantic + keyword) tuned to your documents
— Anti-hallucination layer with real evaluation metrics
— Docker deployment, production-ready
— MLflow monitoring so you can track quality over time
— Full source code + documentation
Message me before ordering. I'll review your use case and
confirm which tier fits — I won't let you buy the wrong one.
Mine work in production.
At 3D Smart Factory, I built and deployed a hybrid RAG agent
evaluated on 150 real production queries:
→ 85% global accuracy
→ Under 5% hallucination rate
→ Adopted for daily use by the company director
That system didn't come from tutorials. It came from building
the eval layer after watching an earlier version fail silently
— wrong answers, no alerts, no crash.
What you get:
— Hybrid retrieval (semantic + keyword) tuned to your documents
— Anti-hallucination layer with real evaluation metrics
— Docker deployment, production-ready
— MLflow monitoring so you can track quality over time
— Full source code + documentation
Message me before ordering. I'll review your use case and
confirm which tier fits — I won't let you buy the wrong one.
AI Development Type
Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$500
|
Standard
$900
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
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TZ
Thomas Z.
May 9, 2026
Spark Paid Test Assignment
Wahab respected the assignment and created a Spark pipeline. Wahab showed good problem solving skills and was easy to communicate with.
About Wahab
AI Engineer | Industrial AI (Segula) | Production-Ready RAG & Agents
Agadir, Morocco - 1:20 pm local time
I don't just "prompt" AI; I architect industrial-grade systems that are observable, testable, and secure.
Why trust me with your roadmap?
=>Industrial Authority: Architected the SupplyMind platform for Renault Technocentre, automating ingestion via Apache Airflow and deploying 6 ML models with RandomForest (97% accuracy).
=>Measured RAG Precision: At 3D Smart Factory, I deployed a hybrid RAG agent (LangGraph + FAISS) with 85% global accuracy and <5% hallucination rate on 150 real production queries.
=>Infrastructure & Security: Certified in AWS Cloud Technical Essentials and Google Cybersecurity (June 2026), ensuring your data pipelines are production-ready and secure .
=>Award-Winning Vision: Winner of the FEECRA 2025 Entrepreneurial Potential Award, recognized for bridging the gap between technical AI and real-world business impact .
What I actually build for you:
Production RAG systems with real evaluation layers (not just "vibes").
Agentic Workflows using LangGraph, n8n, and FastAPI backends.
Real-time Data Pipelines (Spark + Kafka) at industrial scale.
LLM Integrations that survive contact with real users.
Logistics & Communication:
Trilingual: Fluent in English, French, and Arabic.
Timezone: Based in Morocco (GMT+1) excellent overlap with the US East Coast and all of Europe.
Tools:
Expert in Python, Docker, Kubernetes, and MLflow.
If your AI project needs to move from "it kind of works" to "it works for 25,000 users," let's talk. Shoot me a message to discuss your roadmap or book a quick 10-minute discovery call.
Portfolio: wahab-hammoud.vercel.app
Steps for completing your project
After purchasing the project, send requirements so Wahab can start the project.
Delivery time starts when Wahab receives requirements from you.
Wahab works on your project following the steps below.
Revisions may occur after the delivery date.
1
You share your documents/data + use case description
2
I design the RAG architecture and confirm approach