You will get a custom stateful AI agent or advanced RAG pipeline in Python
Project details
Get a production-ready, custom AI application tailored to your business needs without context loss, slow response times, or hallucinations.
WHAT I BUILD:
• Stateful LangGraph Agents: Multi-node workflows equipped with human-in-the-loop authorization gates, SQLite checkpointers, and crash recovery.
• Advanced RAG Architectures: Hierarchical auto-merging chunking and MongoDB Atlas hybrid search to maintain context precision across complex documents.
• High-Speed Low-Latency Inference: Powered by open-weights models (Llama 3.3 70B) running on Groq LPU hardware for sub-second execution.
• Guardrails & Reliability: Corrective RAG (CRAG) patterns to trigger live web-search fallbacks and AI reviewer nodes to validate outputs.
DELIVERABLES YOU RECEIVE:
1. Fully tested, clean Python codebase with clear setup documentation.
2. Optimized vector store index configurations and data ingestion scripts.
3. Post-delivery support to ensure seamless deployment and integration.
Let's eliminate context fragmentation and build a reliable AI workflow for your data today!
WHAT I BUILD:
• Stateful LangGraph Agents: Multi-node workflows equipped with human-in-the-loop authorization gates, SQLite checkpointers, and crash recovery.
• Advanced RAG Architectures: Hierarchical auto-merging chunking and MongoDB Atlas hybrid search to maintain context precision across complex documents.
• High-Speed Low-Latency Inference: Powered by open-weights models (Llama 3.3 70B) running on Groq LPU hardware for sub-second execution.
• Guardrails & Reliability: Corrective RAG (CRAG) patterns to trigger live web-search fallbacks and AI reviewer nodes to validate outputs.
DELIVERABLES YOU RECEIVE:
1. Fully tested, clean Python codebase with clear setup documentation.
2. Optimized vector store index configurations and data ingestion scripts.
3. Post-delivery support to ensure seamless deployment and integration.
Let's eliminate context fragmentation and build a reliable AI workflow for your data today!
Machine Learning Tools
PythonWhat's included
| Service Tiers |
Starter
$80
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 2 | 3 | Unlimited |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 0 | 1 | 2 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
About Rodney
AI Engineer | Custom RAG Pipelines
Kampala, Uganda - 2:27 am local time
I specialize in building production-ready Retrieval-Augmented Generation (RAG) systems and autonomous AI agents designed to handle complex, real-world data workflows.
WHAT I BUILD:
• Stateful AI Agents: Autonomous multi-step agents built with LangGraph, equipped with human-in-the-loop permission gates, memory checkpointers, and self-correcting logic.
• Advanced RAG Pipelines: Hierarchical auto-merging retrieval, hybrid search (MongoDB Atlas / Pinecone), and document parsing for complex PDFs and tables.
• Low-Latency Architecture: High-speed inference setups using Llama 3.3 70B via Groq LPU hardware and local open-weights deployments.
• Guardrails & Reliability: Corrective RAG (CRAG) patterns and secondary AI reviewer nodes to catch hallucinations before outputs reach users.
MY TECH STACK:
• Core: Python, Async IO, REST APIs
• Frameworks: LangGraph, LangChain, LlamaIndex
• Vector DBs & Memory: MongoDB Atlas, Pinecone, Weaviate, ChromaDB, SQLite
• Models: Llama 3.3 70B, Groq API, OpenAI API
Whether you need a full prototype built from scratch, an upgrade to an existing RAG pipeline, or a custom tool-calling agent, I deliver clean, well-documented code designed for production environments.
📩 Click "Invite to Job" or "Contact" to discuss your project requirements!.
Steps for completing your project
After purchasing the project, send requirements so Rodney can start the project.
Delivery time starts when Rodney receives requirements from you.
Rodney works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Architecture Setup
Review your data format, configure API access, and establish the system architecture flow.
Pipeline Development & Agent Logic
Implement state machines, auto-merging retriever nodes, and tool routing in python.