A property management group was manually reviewing budget vs. actuals and building proposed budgets for 7 subsidiaries every year. We automated the entire workflow performing the analysis, and making a ready-to-use proposed budget — in under 4 minutes per company. Impact at a glance: - 140 hours saved per year (20 hrs/company × 7 companies) - $6300 saved per year (at analyst rate of $45/hr) - Under 4 minutes per company — was 20 hours manually Two automations in one workflow: 1. Lead Magnet — Free budget analysis for prospects - Prospect submits their budget PDF via a web form - AI validates it's a genuine budget vs. actuals document — invalid files trigger an automatic re-submission prompt - A deep financial analysis runs covering gaps, risks, over/under-allocations, and cost saving opportunities - A formatted Google Doc report is auto-created & shared, and in the prospect's inbox within 2 minutes 2. In-House — Proposed budget & budget analysis - Team member submits the yearly budget PDF via a form - AI extracts every income and expense line item from the document - A budget analysis is sent covering under-allocated & over-allocated areas, oppurtunity gaps, line-by-line item description, operation & efficiency gaps, etc - A fully revised proposed budget is generated with recommended new allocations - Delivered as a color-coded Google Sheet — green (increased), orange (reduced), blue (new) - Slack notification sent instantly with the file link Bottom line: A task that consumed 140 hours and $980 in labour every year — across 7 companies — now runs in under 2 minutes per company, with zero manual effort. The system validates documents, performs CFA-level financial analysis, builds a color-coded proposed budget, and delivers everything automatically to the right place. One automation. Two use cases. Infinite runs

Every month, a finance team of a real Estate company manually pulled QuickBooks reports, calculated metrics, built charts by hand, and formatted everything before sending. That also for 2 seperate company divisions. THE MANUAL PROCESS: - Log into QuickBooks manually - Pull P&L and Balance Sheet separately - Export to Excel - Calculate 15+ financial metrics by hand - Build trend charts manually - Write a performance summary - Format the whole thing - Send it out Time per month: 8–12 hours One person. Recurring. Every single month. THE COST CALCULATION: Average finance admin salary: $45,000/year Hourly rate: ~$22/hour 10 hours/month × $22 = $220/month $220 × 12 months = $2,640/year Spent on copy-pasting numbers into Excel. THE HIDDEN PROBLEM: It wasn't just time. Reports were late. Numbers were sometimes wrong. Charts were inconsistent month to month. And the person doing it dreaded the 11th of every month. No one was catching financial gaps because by the time the report was done, everyone just wanted it over with. THE SYSTEM I BUILT: Automated reporting pipeline in n8n + QuickBooks Online API: - Scheduled trigger fires on the 11th automatically - Pulls live P&L and Balance Sheet directly from QBO - Calculates 15+ metrics across both divisions simultaneously - Generates 3-month trend line charts automatically - AI layer analyzes the full report and flags gaps, anomalies, and problem areas - Fully formatted report hits their slack — zero human involvement THE RESULTS: Time spent on reporting: 10 hrs → 0 hrs Annual hours reclaimed: 120 hours Annual cost recovered: $3,000+ Report consistency: Error rate: Eliminated — data pulled live from source system

Managing expenses across a remote team is a nightmare. Receipts come in every format. Approvals get delayed. Things fall through the cracks. I built an automation that handles the entire flow — automatically: - Receipts emailed in → data extracted instantly - Everything logged to a clean Google Sheet, no manual entry - Expenses over $100 flagged to the right person on Slack for approval - Everything under $100 auto-approved and confirmed For teams processing 50-500+ expenses a month, this eliminates 40+ hrs/mo on back-and-forth and gives finance a clean, real-time record without chasing anyone. DM me "EXPENSE" if you want to save 40+hrs/mo for your company by implementing this system

This week, I reviewed 20 resumes, many of them AI-generated. Here’s the problem: Most fail not because of lack of experience, but because of positioning. Common issues I noticed: • AI-generated resumes often feel generic and not tailored to the job • Lack of measurable achievements — results matter, not just tasks • Missing ATS keywords — your resume may never reach a human • Confusing formatting — easy to skip The good news? These problems are fixable with a professional review and targeted improvements. I’m offering resume reviews where I’ll point out exactly what’s holding you back and give actionable recommendations to improve your resume, AI-generated or not. Message me or email: [email protected]