You will get your team building production Claude Code agents in live working sessions
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Project details
Most teams try Claude Code, get something impressive in a demo, then watch it do something dumb on real work and quietly stop using it.
These are working sessions on your repo, not a course. You share your screen, you drive, I correct in real time. By the end you have shipped something that runs.
What we cover: setup that fits your codebase (skills, hooks, MCP servers, permissions), and the patterns that separate a demo from production. Readiness gates so an agent asks instead of guessing. Tool boundaries. Credential isolation so no agent ever holds your keys. What to do when output drifts.
Why me: I run an 11-agent pipeline in production for an ERP integrator that turns tickets into GitHub pull requests, and a 12-automation lead funnel for a travel operator. The patterns I teach are the ones keeping those alive.
You keep everything: prompt pack, config and house rules committed to your repo, sessions recorded.
Good fit for dev teams, technical founders, and automation consultants who want their own team building agents instead of outsourcing every one.
These are working sessions on your repo, not a course. You share your screen, you drive, I correct in real time. By the end you have shipped something that runs.
What we cover: setup that fits your codebase (skills, hooks, MCP servers, permissions), and the patterns that separate a demo from production. Readiness gates so an agent asks instead of guessing. Tool boundaries. Credential isolation so no agent ever holds your keys. What to do when output drifts.
Why me: I run an 11-agent pipeline in production for an ERP integrator that turns tickets into GitHub pull requests, and a 12-automation lead funnel for a travel operator. The patterns I teach are the ones keeping those alive.
You keep everything: prompt pack, config and house rules committed to your repo, sessions recorded.
Good fit for dev teams, technical founders, and automation consultants who want their own team building agents instead of outsourcing every one.
AI Algorithms
Large Language ModelAI Applications
AI-Generated CodeAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$450
|
Standard
$1,800
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 3 days | 14 days | 30 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 | - | - | - |
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SG
Skyna G.
Jun 28, 2026
Looking for French (Canadian) language person sentences recording task
Great job 👍
VC
Vincent C.
May 8, 2026
Automatisation des livraisons, gestion des courriels et des factures
Great help to automate my flow on n8n. Julien worked well and surpassed my exceptations.
About Julien
AI Agent Developer | CRM Automation | GoHighLevel & n8n
100%
Job Success
Sherbrooke, Canada - 11:47 pm local time
⚡ Built an autonomous AI developer for an ERP integrator reducing 40hrs of dev work to less than an hour: 6 Claude agents that read a user story out of an ERP, audit whether it's ready, write the code, open the GitHub pull request, and post the status back into the ERP. Custom Python MCP server, so no agent ever holds client credentials. Live in production.
⚡ Automated delivery dispatch for a $3.2M medical transport business, using AI to detect delivery from faxes and dispatch it to the relevant employee depending on the region, reducing a full time job to 1-2 hrs of work weekly.
I build AI agents and CRM automation that run in production, not in a demo video. GoHighLevel, Pipedrive, n8n, Make, Python, Claude & OpenAI.
What I build:
• AI agents that capture, qualify, and follow up with leads across email, chat, and SMS
• Agentic systems wired into your real tools (ERP, CRM, ticketing, GitHub) with credentials isolated behind an MCP server
• GoHighLevel (GHL): sub-accounts, pipelines, calendars, automations, snapshots, A2P/10DLC, white-label
• CRM automation & integrations: sync leads, contacts, deals across GHL, Pipedrive, HubSpot, and your stack
• RAG / AI search over your document catalog: ingestion, embeddings, citation-backed answers
• Lead-gen & nurture pipelines: intake → scoring → routing → automated follow-up
• Document processing: extract, classify, push data into your CRM
• Reporting dashboards pulling every source into one view
How I keep agents safe in production: readiness gates before an agent acts, human-approved drafts for anything customer-facing, confidence thresholds that route to a person instead of guessing, and monitoring you can actually read.
My approach: start with your sales/ops process, find the highest-ROI automation, ship a working prototype fast, then iterate. No bloated proposals, just results.
Tech: Claude API, OpenAI API, MCP, GoHighLevel, Pipedrive, n8n, Make, Python, FastAPI, Supabase, PostgreSQL, vector databases, REST APIs, webhooks, Telegram/SMS, email automation.
Steps for completing your project
After purchasing the project, send requirements so Julien can start the project.
Delivery time starts when Julien receives requirements from you.
Julien works on your project following the steps below.
Revisions may occur after the delivery date.
Session 1: set up on your real repo
We install and configure on your actual codebase: skills, hooks, MCP servers, permissions. No sandbox demos.
Build one agent together, live
You drive, I correct. We cover the patterns that keep agents safe: readiness gates, tool boundaries, credential isolation, and what to do when the agent guesses.