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I made this short video in ElevenLabs to show one small part of what I’m building at Sell.Systems.
The idea is simple:
too much work still disappears in handoffs.
research → planning → building → testing → delivery
every step protects quality, but it also leaks context.
So I’m building a different setup:
a private AI workspace with a web terminal,
separate execution lanes for different jobs,
and one protected core that stays under control.
One lane can handle web work.
Another can test ideas.
Another can do lead research, content, or reporting.
They can connect when needed.
They can stay isolated when they should.
The goal is not “more AI everywhere.”
The goal is better architecture:
clear boundaries,
controlled execution,
and a system that gets more useful after every run.
Sell.Systems is the build layer.
Automation.Sell.Systems is the managed layer.
Not another chatbot.
More like a private operating layer for real AI work.
Which workflow in your business still has too many handoffs?
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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Related posts
Why AI Automation Fails Without a Real Workflow Strategy
AI automation is powerful, but many businesses fail with it because they start with tools instead of workflows.
They say, "Let’s use ChatGPT, Make, Zapier, or n8n."
But the real question should be:
Where is the business losing time every week?
Most automation problems are not only technical. They are workflow problems.
For example, a company may want to automate sales follow-ups. But before building anything, we need to understand:
Where does the lead come from?
What data needs to be collected?
When should the follow-up happen?
What should AI write?
When should a human review it?
What happens if the API fails?
Where should the final result be saved?
A good automation system is not just a chain of tools.
It needs clear inputs, clean data, fallback logic, error handling, logging, and human approval where needed.
That is the difference between a simple automation and a reliable business system.
My approach is simple:
First, understand the workflow.
Then, remove unnecessary manual steps.
After that, build the automation using tools like n8n, Make, Zapier, OpenAI API, LangChain, or custom APIs.
AI should not replace business logic.
It should make the workflow faster, cleaner, and easier to scale.
#AIAutomation #WorkflowAutomation #BusinessAutomation #AIWorkflowStrategy #n8n #Make #Zapier #OpenAIAPI #LangChain #APIIntegration #HumanInTheLoop #ErrorHandling #ScalableAutomation #AIAgents #OperationsAutomation
I made some progress with my Framer plugin "AI Design to Framer" that I'm working on.
Designs from Claude Design are now importing correctly most of the time.
"60% of the time it works every the time". - Brian Fantana
Everyone talks about AI models.
Few talk about what makes AI actually work in production. ⚡
A production-ready AI system is more than just plugging into an API.
It needs:
🔍 High-quality retrieval (RAG)
🧠 Smart orchestration & model selection
🔌 Real integrations with tools & APIs
🛡️ Guardrails, permissions & safety layers
📈 Monitoring, observability & cost control
That’s the difference between:
❌ an impressive AI demo
and
✅ an AI product real users can depend on
As an AI Engineer, I help businesses build intelligent systems that are reliable, scalable, and built for production—not just prototypes. 🚀
Which layer do you think most teams underestimate? 👇
#AI #GenerativeAI #LLMAgents #RAG #AIEngineering #LangChain #PythonDeveloper #ArtificialIntelligence #MachineLearning #AIProducts #Automation #SaaS #TechInnovation #Contra
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