Platform, real estate deal analysis
DealSharp
Clear numbers for property investors and operators, before the deal, and long after it.
DealSharp is a real estate deal-analysis platform I built from direct experience with property finance and the bookkeeping behind it. It turns scattered numbers into decisions.
Investors make six-figure calls on envelope math; operators run portfolios on books that lag months behind reality. DealSharp closes both gaps, sharp numbers before you buy, and sharp numbers long after.
The leak it was built to close
Real estate runs on numbers, but the numbers live everywhere except where decisions get made, in PDFs, in a broker's spreadsheet, in someone's head. So the actual decision gets made on a quick calculation that feels rigorous and isn't.
Then the deal closes and a second leak opens: the operating numbers fall behind. Bookkeeping is monthly at best, so problems surface in the accounts months after they started in the building. By the time the spreadsheet shows it, it's already expensive.
Both leaks have the same root: there's no system turning real estate reality into clear, current, decision-ready numbers, before and after the deal.
How I built it
My bookkeeping and finance background isn't a footnote here, it's the reason DealSharp works. I've sat with the spreadsheet and I know what it hides, so I built the tool to surface exactly that.
Calculators handle the numbers that decide a deal. Stress tests model the scenarios nobody wants to think about until they happen. A portfolio dashboard tracks what you already own, and a CFO-style reporting layer with a bookkeeping suite keeps the after-the-deal numbers as current as the before.
An AI deal advisor sits across the top for the questions that fall between the calculators, so the platform answers, instead of just storing inputs.
What's inside
Deal calculators
The core numbers that make or kill a deal, structured so the math is rigorous, not hopeful.
Stress testing
Model the downside scenarios, rate moves, vacancy, cost overruns, before they model you.
Portfolio dashboard
Everything you already own in one view, current instead of months behind.
AI deal advisor
Answers the in-between questions a calculator can't, in plain language.
CFO-style reporting
Investor-grade reporting that turns raw operations into decisions.
Bookkeeping suite
The ledger underneath, so the after-the-deal numbers stay as sharp as the before.
“Every messy process eventually becomes messy numbers. Build the system before the numbers force you to.”
Fabian Janiszewski, founder of OpsByFabian
Tech & logic
- Financial modeling engine (deal-level math)
- Scenario & stress-test logic
- Portfolio data model
- AI advisory layer over structured financials
- CFO-style reporting suite
- Integrated bookkeeping ledger
What it proves
- My finance background is a build advantage
- DealSharp exists because I understand both the spreadsheet and what it's hiding. Domain depth shows up as better software.
- Before and after are one system
- Decision math and operating books aren't separate problems, they're the same numbers at two moments, and they belong in one tool.
- Messy processes become messy numbers
- The most reliable law of operations I know, and the reason to build the system before the numbers force you to.
Numbers you can't quite trust?
If decisions in your business run on guesswork and lagging books, there's a system for that. Let's scope it.