OCR + geometric logic = catch drawing errors before they cost thousands in rework.
I was scrolling through r/Construction last week and saw estimators complaining about AI-generated drawings with impossible dimensions. One guy got a floor plan where the stairs were wider than the entire house. Another found dimension chains that added up wrong by three feet.
This happens constantly. Not just with AI drawings, but with any quick sketch or PDF that gets passed around. The estimator gets blamed when the bid is wrong, but they're working with garbage inputs.
The fix is simple: automated sanity checking before the drawing gets shared. Upload PDF, get back a report that flags obvious problems. Takes 30 seconds.
Don't write code yet. Construction people are skeptical of new tools, so you need proof they actually want this.
Make a landing page with a Typeform. "Upload your drawing, get dimension errors flagged automatically." Post it in r/Construction and r/estimators with a genuine question about whether this would be useful.
Better yet, offer to manually check drawings for the first 20 people. You'll spend an hour per drawing doing OCR screenshots and math in a Google Sheet, but you'll learn exactly what errors matter and how people talk about them.
I'd aim for 10 people willing to pay $49 for lifetime access within three weeks. If you can't hit that, the demand isn't there.
Your first version does three things:
Most drawing errors are basic math mistakes or copy-paste failures. You don't need computer vision magic to catch those.
Next.js frontend hosted on Vercel. Supabase for database and auth. OpenAI Vision API for the OCR work.
The file upload flow is straightforward: user drops a PDF, you convert it to images, send each page to the vision API with a prompt asking for all visible dimensions, then run your consistency logic on the results.
For dimension checking, you're basically parsing text and doing arithmetic. If a wall is labeled as 12 feet total and has windows at 3 feet and 4 feet with 2 feet of wall space marked, that should add up to 9 feet, not 12.
The hardest part is writing prompts that reliably extract structured data from the vision API. Expect to iterate on that for a week.
Week 1: File upload, PDF to image conversion, basic OpenAI integration. Get one drawing processed end-to-end, even if the logic is buggy.
Week 2: Improve the OCR prompts and build the dimension checking logic. This is where you'll spend most of your time debugging edge cases.
Week 3: Reports generation and basic UI polish. Make it look professional enough that someone would actually use it.
Week 4: Stripe integration and deploy. Launch the beta.
If you're decent with React and have used OpenAI's API before, this is totally doable in a month of weekend work.
Free tier: 5 uploads per month with watermarked reports. Gets people hooked.
Solo plan: $29/month for unlimited uploads and clean reports. This is your bread and butter.
Team plan: $79/month for sharing and multiple users. Probably 10% of your customers but higher LTV.
At 500 paying customers you're at $15k monthly revenue. The CAC should be low since construction people share tools they like, and the retention is probably decent since this slots into existing workflows.
The OCR accuracy might be terrible on real drawings. Estimators work with sketches, photos of whiteboards, and scanned documents that were photocopied three times. If your tool flags everything as suspicious or misses obvious errors, people will stop using it immediately.
That's why you validate with real drawings first. Get a feel for what quality you're dealing with before you commit to building.
The other risk is Bluebeam or Autodesk shipping this feature. They have the distribution and the existing workflows. But they're slow, and you can probably get 12-18 months of runway before they notice.
Vision models got good enough in the last year to reliably read text from construction drawings. GPT-4V can look at a floor plan and tell you what dimensions it sees and where they are.
Two years ago you would have needed custom computer vision models and a team of ML engineers. Now it's an API call.
The trick is prompting the model to think spatially. Don't just ask for dimensions, ask it to identify which dimensions are part of the same measurement chain and whether they're consistent with each other.
Post a 90-second Loom video in construction subreddits showing a real example. "Found this floor plan with impossible dimensions, built a tool to catch these automatically."
DM people who comment. Offer to check one of their drawings manually in exchange for feedback.
Reach out to ASPE chapter leaders and offer beta access for their members. These are professional estimators who deal with this problem daily.
The goal is 10 paying customers in the first month. If you hit that, you've got something worth scaling.
Construction people don't adopt SaaS easily, but they will pay for tools that save them from looking stupid in front of clients. A tool that catches dimension errors before the bid goes out hits that need directly.