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An AI-powered automation workflow built with n8n that intelligently processes incoming requests and executes the appropriate API actions.
The workflow receives requests through a webhook and uses an AI Agent to understand the user's intent and determine the required action. It can dynamically route requests to different operations such as retrieving properties, posting URLs, deleting resources, and returning structured results.
The system also includes AI-powered output parsing and automatic output correction to ensure responses follow the expected structure and format.
Key features:
AI-powered request understanding
Webhook-based automation
Intelligent request routing
API integration and dynamic actions
Structured JSON responses
Automatic output parsing and correction
Modular and scalable n8n architecture
Skills / Tools
n8n · AI Agents · Google Gemini · API Integration · Webhooks · JSON · Workflow Automation · AI Automation
Seeing the AI agent pick the right API call from the webhook payload cuts a lot of manual triage.
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AI Home Services App — Smart Technician Booking & Repair
A premium AI-powered home services app designed to simplify how people diagnose home problems, find the right service, and book qualified technicians.
The product uses AI to turn a simple problem description into a clear service recommendation — helping users move from “What’s wrong?” → “What do I need?” → “Who can fix it?”
Key Experience
→ AI-powered problem diagnosis
→ Text, voice & photo input
→ Smart service recommendations
→ Technician discovery & booking
→ Transparent service information
→ Real-time technician tracking
→ Premium, intuitive mobile experience
The concept is designed for founders building AI home-service platforms, technician marketplaces, repair apps, and SaaS products where reducing friction and improving the booking experience can create a stronger customer journey.
Role: Product Designer
Focus: AI Product Design · UX/UI · Mobile App Design · Service Marketplace
AetherOS is a web desktop for AI agents. The entire project sits inside a single HTML file. It starts with a boot screen. Then it opens into a dark workspace. The background has black animated particles. Everything runs on plain CSS and vanilla JavaScript. The site loads zero external dependencies and zero sound files.
The bottom dock holds seven apps. These tools manage four AI agents: Atlas, Sage, Bolt, and Vega. The Agents app shows them chatting and passing tasks. Files lets you browse a drive. Memory shows a live graph of what the system knows. Users can also modify a history timeline. Studio lets you test prompts. Flows builds node pipelines, and Cost tracks token usage.
Every window can be moved, resized, minimized, or maximized. The top menu bar has a model switcher. It routes tasks to Claude Opus 4.7, GPT-5.2, Gemini 3 Pro, DeepSeek V4, or Llama 4. The OS also has Spotlight search, sound toggles, and local API key inputs. The interface uses dark glass panels with blue highlights.
Building the node graphs in one single HTML5 file was tough. At first, the physics nodes pushed apart too hard. They floated right off the screen. Dragging extra lines while getting zoomed in also threw off the precise positions. I fixed the center gravity math. Then I divided mouse positions by the zoom factor. That stopped the unnecessary movement and kept the OS stable.😊
Normal work disappears. Genuine judgment surfaces.
I built Northline Flow for @Lovable’s #lovablechallenge — a fictional owner-operated home-services business in Surrey, BC.
I didn’t want to build another prettier scheduler. The problem I wanted to attack was the handling around the booking.
Routine requests can continue toward booking. Genuine ambiguity reaches the owner as one framed decision. Unsafe or wrong-fit requests stop instead of being forced into the normal booking path.
I also built a deterministic test harness and a playable Morning Shift proof mode: three fictional requests enter the same routing rules, but only one requires a human decision.
The part I’m most excited about is that the challenge itself became reusable. We captured the process from brief → working flow → adversarial testing → cinematic donor → bounded visual integration → playable proof → final freeze.
Built in Lovable.
AI AutomationBusiness Workflow AutomationProduct DesignChatGPTGoogle AntigravityLovablelovablechallenge
