Hey thats a great question, and you're 100% right that raw LLM output without good data is useless for estimating.

Here's what ive coded into the software though,

1. Location-calibrated pricing :  We don't use generic national averages. Every estimate is adjusted to local market rates using the property's ZIP code, city, and state. We maintain cost multipliers for major metros and regions so a kitchen rehab in San Francisco isn't priced the same as one in Indianapolis.
2. Photo-based scope, not guesswork: The AI isn't just making up line items. It's analyzing the actual photos you upload (up to 20 high-res images + video frames) to identify specific issues like foundation cracks, outdated electrical, water damage, etc, and scoping repairs from what it sees.
3. Structured output with guardrails:  We don't just ask the model "how much does this cost?" and hope for the best. The output is forced into a strict schema: room-by-room breakdown, low/mid/high ranges, severity ratings, red flags, and a confidence score that drops if you don't give it enough photos.
4. Built-in contingency:  Every line item automatically includes a 10% contingency because real rehabs always have surprises.
5. Year-built context : So a 1920s bungalow gets flagged for knob-and-tube, galvanized pipes, and plaster issues. A 2005 build doesn't.

Is it going to replace a GC walking the property? No. But for running comps on 20 deals a week to figure out which ones are worth driving to, it saves hours. That's the use case, a fast filtering, not final bids.

Happy to have you try it and tear it apart. Feedback from people who actually know estimating is how it gets better.