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Title: Client Data Standardization & Intake Matrix
Description: Engineered a standardized data intake and cleansing framework to transform unstructured, error-prone client records into a validated master database. Built to streamline manual data entry, enforce data integrity, and accelerate operational reporting.
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This AI receptionist replies to new leads before you can even open your inbox.
Here's the full system:
Lead comes in → n8n picks it up → AI checks business info and writes a personalised response instantly → lead scored as Hot, Warm or Cold → everything saved to Airtable → if they book, Google Calendar updates and confirmation sent automatically → reminders sent → unbooked leads followed up → no-shows chased → past clients reactivated.
And one system serves multiple businesses. Add a new company by adding one row. No rebuilding.
7 workflows running like one always-on front desk.
Would you use this for your business? Comment FRONT DESK below and I'll reach out directly.
N8NaiautomationaireceptionistAI Agent DevelopmentAI AutomationBusiness Workflow AutomationGoogle CalendarGoogle Sheets
Built a 4-page Power BI dashboard analyzing hotel booking performance for AtliQ Grands across 25 properties in 4 Indian cities (Delhi, Mumbai, Bangalore, Hyderabad), covering May–July 2022.
Designed a star schema data model (dim_date, dim_hotels, dim_rooms, fact_booking, fact_aggregated_bookings) and built pages covering Executive Summary, Occupancy, Customer Insights, and a dedicated Caveats page documenting data limitations and QA methodology.
Key finding: weekend occupancy (51.85%) exceeded weekday occupancy (35.92%), and Delhi recorded both the highest occupancy (42.42%) and highest average guest rating (3.8/5).
🔗 GitHub: github.com/firdous-cloud-analyst/AtliQ-Grands-Hospitality-Performance-Dashboard
Cleaned 500+ client record. Removed 12 duplicated fixed 28 email errors. Improved data accuracy for weekly report
