You will get From basic RAG to Agentic RAG implementation tailored for your use case
Project details
Is your current RAG system hallucinating or giving irrelevant answers when data is missing? I will implement a Self-Reflective Agentic RAG system that mimics true human reasoning to ensure 100% relevance and accuracy.
Unlike a standard RAG, this "Agentic" approach uses a sophisticated feedback loop:
Routing Agent: Decides if the local vector store has the answer or if it needs to trigger a Search Engine fallback.
Document Filtering: A specialized agent checks if retrieved documents are actually relevant before they reach the generator.
Self-Correction: If all documents are irrelevant, the agent automatically broadens its search.
Hallucination Guardrail: A final check verifies that the generated response is grounded in the source material and directly answers your query.
Unlike a standard RAG, this "Agentic" approach uses a sophisticated feedback loop:
Routing Agent: Decides if the local vector store has the answer or if it needs to trigger a Search Engine fallback.
Document Filtering: A specialized agent checks if retrieved documents are actually relevant before they reach the generator.
Self-Correction: If all documents are irrelevant, the agent automatically broadens its search.
Hallucination Guardrail: A final check verifies that the generated response is grounded in the source material and directly answers your query.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation SystemAI Tools
PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$25
|
Standard
$35
|
Advanced
$45
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 4 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.
Knowledge Graph + Graphrag
(+ 1 Day)
+$100
Langsmith
+$10About Mohamed Khalil
AI Engineer | NLP Specialist
Monastir, Tunisia - 6:34 am local time
What I Offer:
✅ NLP & Generative AI: Fine-tuning transformers (e.g., T5, Mistral), custom tokenizers, semantic search, prompt engineering, and integration with LangChain, LangGraph, LangSmith, Atomic-Agents, Instructor, ReAct agents, etc.
✅ Conversational & Voice AI: Real-time speech-to-text (Whisper-timestamped), text-to-speech (Piper), speaker identification, offline assistants powered by Ollama LLMs, and streaming APIs with FastAPI/WebSockets.
✅ AI Agents & Automation: Custom workflows using n8n, AI agents powered by LLM providers like Google Gemini, GroqCloud, and OpenRouter, web scraping/RSS/Tavily Search integration, personalized content scoring, and multi-channel delivery (email, Telegram, WhatsApp).
✅ RAG & Knowledge Graphs: Retrieval-augmented generation systems (including GraphRAG), knowledge graph extraction and generation (Neo4j/Cypher), enhanced document comprehension, and research pipeline integrations.
✅ Machine Learning Systems: Recommendation engines using cosine similarity, data pipelines with preprocessing/optimization, model fine-tuning, quantization, and flash attention techniques.
✅ Full-Stack AI Integration: Building end-to-end applications with frontends (Angular, React, Gradio, Streamlit) and backends (Flask, FastAPI, Spring Boot), connecting AI models to intuitive UIs (including Unity-based interfaces).
✅ Deployment & DevOps: Docker containerization, cloud hosting on Azure/Railway, secure APIs with authentication, and persistent storage using Supabase, PostgreSQL, or NoSQL databases (ChromaDB, Neo4j).
Let's collaborate to bring your AI vision to life with innovative, high-performance solutions tailored to your needs! 🚀
Steps for completing your project
After purchasing the project, send requirements so Mohamed Khalil can start the project.
Delivery time starts when Mohamed Khalil receives requirements from you.
Mohamed Khalil works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture Review
We discuss your specific data sources and desired accuracy thresholds.
Vector Store Setup
Ingestion of your data with optimized chunking and embedding.