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Automated vehicle inventory scraping and reporting system. Scrapes dealership websites for inventory data, processes and structures the output, and generates daily reports.
Technologies: Python, Playwright, BeautifulSoup, SQLite, cron
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An AI-powered operations assistant designed to help property management teams quickly find answers from their property, maintenance, and work-order data.
Instead of manually searching through spreadsheets and work-order records, a property manager can ask questions in natural language and get structured answers based on the underlying operational data.
The system can identify unresolved maintenance requests, high-priority issues, properties with the most open work orders, and work orders that have been open the longest. Relevant supporting records are displayed alongside each answer.
Workflow:
Property & work-order data → Natural-language question → Data retrieval → AI interpretation → Answer + supporting records
Key features:
AI property operations assistant
Natural-language business data queries
Maintenance and work-order analysis
Open issue identification
High-priority issue detection
Property-level operations analysis
Supporting records for AI answers
Structured operational database
Local/offline fallback
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, SQL-based data retrieval, and AI-assisted question interpretation.
This project demonstrates how AI can be applied to automate information retrieval and support day-to-day property management operations.
I built an AI agent that handles customer operations — refunds, order lookups, and support tickets — with a human approval gate built into the workflow. The agent proposes the action, pauses, and waits for a human to approve before anything executes. The LLM never makes the final call. Role-based access and a full audit trail are enforced in code, not prompts. Deployed live on Azure Container Apps.
The pause for human sign off keeps refunds safe while still offloading the lookup work to the agent.