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
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.
For covering the risk of mistakes I suggest considering ways of "visually quoting" the documents.
If the summary says "closing timeline: X" but there's an icon I can click that pops open an overlay with a visual cropped screenshot of that part of the original PDF - maybe even with a red circle around that detail - I can trust those summaries a whole lot more.
Gemini 2.5 has image bounding box and masking features that can help with this (sadly missing from Gemini 3.)
I've never owned a home and would like to try to buy one in the next year or two. There doesn't seem to be much in the way of API's/software tools that let you analyze historical data and prices of listings in specific areas.
How can I get my hands on the right information to make sure I don't get ripped off?
Just a heads-up : the "about" page content doesn't seem to match what's with the rest of the website. Why is it about automated resume review when the front page is about real estate ?
codingdave|1 month ago
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.
beechwood|1 month ago
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.
simonw|1 month ago
If the summary says "closing timeline: X" but there's an icon I can click that pops open an overlay with a visual cropped screenshot of that part of the original PDF - maybe even with a red circle around that detail - I can trust those summaries a whole lot more.
Gemini 2.5 has image bounding box and masking features that can help with this (sadly missing from Gemini 3.)
jgalt212|1 month ago
When sold out vacation home, we had multiple offers, but I seem to recall the offer letters being 1 pagers. Does offer letter length vary by region?
gavinray|1 month ago
I've never owned a home and would like to try to buy one in the next year or two. There doesn't seem to be much in the way of API's/software tools that let you analyze historical data and prices of listings in specific areas.
How can I get my hands on the right information to make sure I don't get ripped off?
TekMol|1 month ago
https://news.ycombinator.com/showhn.html
pietz|1 month ago
2026: "This app could have been a prompt"
beechwood|1 month ago
exhost|1 month ago
867-5309|1 month ago
beechwood|1 month ago
kassas|1 month ago
beechwood|1 month ago