You will get AI Agent Setup and Hardening: OpenClaw or Hermes
Rising Talent
Rising Talent
Project details
Two serious self-hosted agent frameworks are racing for this space, they overlap a lot, and the right pick for your case is not obvious. The cheap gigs install both and walk. I help you choose, then set it up secure and integrated.
I build production agents for companies, so yours is the easy end of what I do.
Why setup is not one-click: these runtimes run shell commands and read your files, skills are an unvetted supply chain, and self-hosted agents get left exposed. Yours gets vetted skills, no open ports, scoped keys, and pinned versions so a bad update cannot take it down silently.
Proof: a live incident-response agent with its own tool server, a company-brain agent with isolated per-user memory, and my bilingual real estate agent over a WhatsApp bridge. I have authored three tool servers, run Claude Code daily, and hold six contracts on this profile, five-star rated on completed work.
This is framework choice, setup, and hardening, not a custom build. Need a bespoke agent from a spec? That is my spec-first build gig and I will point you to it.
Not sure which fits? Tell me what the agent should do and I will give you a straight read before you spend.
I build production agents for companies, so yours is the easy end of what I do.
Why setup is not one-click: these runtimes run shell commands and read your files, skills are an unvetted supply chain, and self-hosted agents get left exposed. Yours gets vetted skills, no open ports, scoped keys, and pinned versions so a bad update cannot take it down silently.
Proof: a live incident-response agent with its own tool server, a company-brain agent with isolated per-user memory, and my bilingual real estate agent over a WhatsApp bridge. I have authored three tool servers, run Claude Code daily, and hold six contracts on this profile, five-star rated on completed work.
This is framework choice, setup, and hardening, not a custom build. Need a bespoke agent from a spec? That is my spec-first build gig and I will point you to it.
Not sure which fits? Tell me what the agent should do and I will give you a straight read before you spend.
AI Algorithms
Multimodal Large Language ModelAI Applications
Conversational AI, Natural Language UnderstandingAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$300
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 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
+$200 - $300
Additional integration / data source
(+ 3 Days)
+$300Frequently 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 - 11:16 pm 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.
Pick the framework and scope the agent.
We agree what the agent should do, which framework fits (Hermes, OpenClaw, or your existing stack), and a safe place to run it, so it is never installed loose on your main machine with your real keys.
Set it up, locked down.
I install and configure the framework, wire your model and your channels, and vet every skill or tool before it goes near your data.