Are you or your team still spending hours answering the same customer questions like “Where is my order?”
I build smart AI automation systems that handle these repetitive queries instantly—so you can save time, reduce workload, and focus on growing your business.
Using tools like n8n and AI models, I create custom workflows that automatically process customer requests, extract key information (like order IDs), fetch real-time data from your database, and generate accurate responses without human involvement.
For example, a customer can ask about their order, and the system will instantly check the status and respond—no manual effort needed. This can be extended to multiple use cases such as customer support, lead handling, notifications, and more.
Each system is fully customizable based on your business needs, whether you want integrations with Google Sheets, APIs, messaging platforms, or internal tools.
What You Get
Custom AI automation workflow
Order/status tracking system
Integration with your database (Sheets/API)
Smart response generation using AI
Scalable system you can expand anytime
Use Cases
E-commerce order tracking
Customer support automation
Lead response systems
Internal workflow automation
Why This Matters
Reduce up to 50–70% of repetitive work
Faster response time for customers
Lower operational costs
Better customer experience
Custom Solutions
This is just a basic logic setup—I can fully customize the system based on your requirements, tools, and workflow.
Call to Action
Have a specific idea or problem?
Message me and I’ll help you design the perfect automation for
your business.
Manual work is always better than ai slop you can use ai for some clips but full video made with ai is unprofessional and a waste of money and resources
The point about people quietly training AI on today's manual tasks is the part most of us underestimate. Do you think the craft shifts to directing and curating those models, rather than disappearing entirely?
Congrats on getting it approved. Drafting instead of auto-sending is the right default. One edge case worth a guard: calls where the transcript comes back empty or cut short, otherwise the model writes a confident follow-up from almost nothing.