Seriously, yesterday you posted a show HN about comparing resumes. Today you are comparing real estate offers. You are just rapid-fire fishing for a market that is willing to pay for a chatGPT wrapper around doc comparisons.

Even if this wasn't just a string of low-effort attempts, comparing real estate offers does not take hours. Deciding between them might, but the comparisons can be done quite quickly, so there is almost zero value here.

Hello HN, I’m a solo developer building tools for real estate workflows. I built OfferGridAI after watching listing agents repeatedly struggle with the same problem during hot markets.

When a property gets multiple offers, each offer usually comes in as a 10–20 page PDF. Under tight time pressure, agents have to manually dig through each document and rebuild a spreadsheet to compare things like price, net to seller, contingencies, financing, closing timeline, escalation clauses, etc. It’s not conceptually hard, but it’s stressful, time-consuming, and easy to miss details buried deep in the PDFs.

I wanted a way to make that moment less chaotic.

The idea: Upload multiple offer PDFs → extract the key terms → generate a clean, side-by-side comparison grid that’s easy to walk through with a seller.

Instead of just dumping text, the tool normalizes the information into comparable fields (price vs net, contingencies, financing strength, days to close) and adds a short summary highlighting tradeoffs (e.g. highest price vs highest certainty to close).

What it focuses on:

Structured extraction of common purchase-agreement terms

Normalizing offers so sellers can compare apples to apples

Surfacing risk factors (financing type, contingencies, timeline)

Producing a seller-ready grid rather than raw AI output

What it intentionally does not do:

Make decisions for agents or sellers

Replace professional judgment

Integrate with MLS or transaction management systems (at least for now)

The goal is to be a fast decision-support tool for a very specific, high-pressure moment.

I’m early and still refining the scope, especially around:

Which fields matter most in practice

How to communicate “risk” without over-claiming

How tolerant users are of “best effort” extraction vs perfection

I’d love feedback from anyone who’s worked with complex PDFs, document comparison, or decision-support tools under time pressure, or from anyone who’s built vertical SaaS in heavily regulated industries.

Happy to answer questions and learn from the community.

Sign-up pages are not Show HNs:

https://news.ycombinator.com/showhn.html

2006: "This meeting could have been an e-mail"

2026: "This app could have been a prompt"

title should indicate American market

how many paying customers do you have?

[dead]