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MaisonOS — AI Operating System for Real Estate
Overview
MaisonOS is an AI-powered real estate operations dashboard that centralizes lead management, property tracking, agent performance, and business activity into a single interface. Built with n8n automation workflows, it transforms raw operational data into real-time business insights.
The Challenge
Real estate businesses often manage leads, listings, documents, and internal updates across multiple disconnected tools. This makes it difficult to monitor performance, track property status, and understand business activity in real time.
The Solution
I designed and developed MaisonOS, a lightweight operational dashboard powered by automation. Data flows automatically through n8n workflows into Google Sheets, where it is processed and exposed through a webhook that feeds a live dashboard.
The result is a centralized system that provides instant visibility into key business metrics without manual reporting.
Key Features
• Real-time KPI dashboard
• Automated lead tracking
• Property status monitoring
• Agent performance analytics
• Live business activity feed
• Automated brochure generation
• Google Sheets as a lightweight database
• Webhook-powered live dashboard
Tech Stack
• n8n
• JavaScript
• HTML5
• CSS3
• Chart.js
• Google Sheets
• Webhooks
• REST APIs
My Role
I designed the system architecture, built the automation workflows, integrated Google Sheets as the backend, developed the dashboard frontend, connected live webhook data, and created the reporting interface for real-time business monitoring.
Outcome
MaisonOS demonstrates how automation can replace manual reporting with a centralized AI-powered operations dashboard that gives real estate teams instant visibility into their business.
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AI Business Operations Dashboard & Workflow Automation Platform
Overview
This project focused on creating a centralized business operations platform that brings customer data, sales activity, automation workflows, and operational visibility into one intelligent dashboard. The objective was to reduce fragmented processes and provide a single source of truth for daily business operations.
The Challenge
The business managed information across multiple disconnected tools, making it difficult to track leads, monitor workflow progress, assign tasks, and understand operational performance.
My Solution
I designed an integrated platform that combines automation, CRM workflows, and business intelligence into one connected system.
Core capabilities:
Centralized business dashboard
Lead and customer management
AI-assisted workflow automation
Task and activity tracking
CRM synchronization
Real-time operational visibility
Automated notifications and routing
Connected reporting system
Workflow
Data enters from websites, WhatsApp, and internal processes.
Automation routes information to the appropriate workflow.
AI assists with classification and operational tasks.
The dashboard provides a live view of business activity.
Teams take action from one centralized workspace.
My Role
Business systems architecture
Workflow analysis
Dashboard UX planning
Automation design
CRM integration strategy
Business intelligence structure
Technologies
Business Dashboards
AI Automation
CRM Systems
Workflow Automation
Data Integration
Business Intelligence
Outcome
The completed platform transformed multiple disconnected operational processes into one connected digital system, giving the business better visibility, improved workflow coordination, and a more scalable operational foundation.
Skills
Business Intelligence
Dashboard Design
AI Automation
CRM Integration
Workflow Architecture
Digital Transformation
Klaviyo Email Flow Setup & Automation
I designed and structured automated Klaviyo flows for ecommerce brands to engage customers at key moments throughout the buying journey.
The flows are built around customer behavior, with strategic timing, segmentation, messaging, and conversion-focused email design.
Flows include:
Welcome Series
Abandoned Cart
Browse Abandonment
Post-Purchase
Winback
Customer Review
Cross-Sell & Upsell
The goal is simple: deliver the right message at the right time, recover missed opportunities, and create more repeat purchases through automation.
AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
