Case Study · Analysis without a language model · Real estate
FlipFinder.
Deterministic underwriting for real estate investors
FlipFinder. helps resellers spot undervalued properties and turn them into profit.
Modeo built FlipFinder end to end, from the underwriting model to the live web app: a deterministic engine that scores listings on flip and rental economics against comparable sales.
What did Modeo build?
Point FlipFinder at a market and it scans the for-sale inventory, pulls property detail and neighbor comparables for each listing, and underwrites the whole set. Results stream to the browser live as the scan runs, land on a map and in scored report cards, and export to a server-generated PDF.
- Comparable-sales analysis with type filtering, size banding, outlier trimming, and size adjustment to the subject property
- An after-repair value with a stated source and a confidence rating
- An age-and-condition-based rehab estimate and a full flip P&L: closing, holding, carry costs, selling costs, projected profit and margin
- An independent rental pass: rent, NOI, cap rate, and cash flow against a 30-year cash-out refinance
- A verdict with a 0 to 100 score clamped so it can never contradict its own label, plus disclosed risk flags
- Per-user accounts, daily run caps, and per-run cost telemetry
Why we did not use AI
FlipFinder contains no model calls, and that is the point. Underwriting is arithmetic that has to be reproducible: the same listing, the same comps, and the same assumptions must produce the same number every time, and an investor has to be able to trace where that number came from. A generative model adds variance exactly where the product needs none.
The engineering discipline went into honesty about uncertainty instead. Every comparable is tagged with what its price actually means, sold price versus estimate versus asking. A valuation resting on zero real sales gets an explicit warning and an automatic confidence downgrade. Two independent sanity caps stop any data source from manufacturing a fake profit. The famous 70 percent rule is computed and shown, but the gate is the engine's own margin math.
Knowing when not to reach for a model is part of building with them well. The result is a tool whose outputs are estimates by design, and which says so on every report.
Why it matters for enterprise buyers
The same judgment applies to your systems: some workflows need a model, some need deterministic code with disciplined data handling, and most need both in the right places. Modeo builds the whole spectrum and will tell you honestly which is which.
Where else this pattern pays back
Where the pattern applies
- Loan and credit pre-screening
- Insurance pre-quote rating
- Supplier bid scoring
- Property and asset valuation
Illustrative payback: An underwriting team screening 200 applications a month
- Screening time today
- 45 minutes each, 150 hours a month
- Loaded underwriter cost
- $60 an hour, $9,000 a month
- Manual review of the top 20% only
- 120 hours a month saved, $7,200
- Data fees
- $0.50 an application, $100 a month
$7,100 a month net. A $28,000 build pays back in about 4 months.
Payback figures are illustrative models built on the assumptions shown, not client results. They exclude Monitor and Improve ($5,500 a month) and your team's time during the build. The AI Opportunity Assessment replaces every input with your measured baseline.
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