You will get an n8n Workflow Rebuilt in Clean Python
Rising Talent
Rising Talent
Project details
No-code tools are great until they aren't. The moment the work gets complicated or messy, or an AI step has to be reliable, you end up fighting the tool instead of using it. That's where I take over.
I rebuild the workflow in code. Python, your repo, your keys. You keep every bit of logic that already worked, and the parts that kept breaking get rebuilt from scratch.
This isn't "rip everything out." Sometimes a no-code tool is favorable. I will tell you if yours is. But when the breakage is logic the tool can't express, code is the upgrade.
Common reasons people bring me a stalled automation:
• Logic the visual builder can no longer express cleanly
• Steps that have to remember what happened across runs
• Error handling with real retries, fallbacks, and an audit trail
• An AI or LLM step that is unreliable and needs to be built properly
• An AI step that is unreliable and needs to be built properly
You work directly with me throughout. I spent real time in both worlds, the no-code ceiling and the code above it, so I know what's worth keeping and what to leave behind. This applies whether you're on n8n, Make, Zapier, or any other visual builder: the logic outgrew the tool.
I rebuild the workflow in code. Python, your repo, your keys. You keep every bit of logic that already worked, and the parts that kept breaking get rebuilt from scratch.
This isn't "rip everything out." Sometimes a no-code tool is favorable. I will tell you if yours is. But when the breakage is logic the tool can't express, code is the upgrade.
Common reasons people bring me a stalled automation:
• Logic the visual builder can no longer express cleanly
• Steps that have to remember what happened across runs
• Error handling with real retries, fallbacks, and an audit trail
• An AI or LLM step that is unreliable and needs to be built properly
• An AI step that is unreliable and needs to be built properly
You work directly with me throughout. I spent real time in both worlds, the no-code ceiling and the code above it, so I know what's worth keeping and what to leave behind. This applies whether you're on n8n, Make, Zapier, or any other visual builder: the logic outgrew the tool.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
Natural Language UnderstandingAI Development Language
PythonAI Models
OpenAI CodexWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 14 days | 21 days | 28 days |
Number of Revisions | 1 | 2 | 3 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$350 - $450
Additional integration / data source
(+ 3 Days)
+$400Frequently asked questions
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Alan M.
Jun 15, 2026
Senior Data Engineer: Scale a Multi-Marketplace Catalog & Normalization Engine (PostgreSQL / Python)
Muhannad is great to work with and is a clear communicator which I value a lot. Will hire him again.
About Muhannad
Claude Code & AI Agent Engineer | Python Automation, LLM Pipelines
Cairo, Egypt - 5:59 am local time
Recent builds:
- A 7-skill, 18-state Claude Code offboarding engine: 4 sub-agents, hook-enforced guardrails, and a JSONL event log as the single source of truth so every run is auditable.
- Commission reconciliation across 7 insurance providers, each with its own format, delivered as reusable Claude skills the client now runs himself.
- A cross-marketplace product matcher at 99.2% precision, with an "abstain rather than mismatch" rule so it stops instead of guessing wrong.
- A 17-gate Python QA pipeline plus a two-pass LLM-judge review fleet for a fine-tuning dataset.
- A GEO upgrade for a news publisher, built with Claude Code and automated schema verification.
- A multi-source data pipeline delivered solo through an agentic Claude Code workflow: 3 databases → one audited, queryable product, solo.
When a no-code tool hits its ceiling (branching logic, state, real error handling), that's where I take over. I rebuild the workflow in code so it does what you actually meant, keeping the parts that worked.
I work best on projects where:
- The input and output are clear, even if the data in between is messy
- There is real API integration, data wrangling, or an AI/LLM workflow involved
- You need something built, tested and handed over, not just advised on
Why Claude-native matters: it's the model family I design around full time, so I know its failure modes and build around them instead of being surprised by them. If your project needs a different model for one step, I will tell you and wire it in. The goal is a system that holds up, not loyalty to a logo.
Stack: Claude Opus/Fable, GPT-5.5, Python, Node.js, OpenRouter, Tesseract, faster-whisper, Playwright, Pandas, SQL.
Steps for completing your project
After purchasing the project, send requirements so Muhannad can start the project.
Delivery time starts when Muhannad receives requirements from you.
Muhannad works on your project following the steps below.
Revisions may occur after the delivery date.
Audit the current workflow
I map what the no-code version does and exactly where it hits its ceiling, then confirm with you what is worth keeping.
Rebuild in code
Python with real branching, persistent state, retries, and an audit trail. A reliable Claude step replaces any flaky AI node.