Opportunities › AI plan review for construction drawings › Proposed app
Sheetwise
AI review that flags conflicts across your construction drawing set
- Score
- 44
- confidence 45
- Evidence
- 7
- items on the page
- Rivals
- 1
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Teams review complex multi-sheet conflict findings, coordinate across disciplines, and share detailed markup reports during planning and preconstruction work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New project
Quote ready412 sheets
About this appAI proposal
Sheetwise reads a full set of construction drawing PDFs, sorts the sheets by discipline, and checks them against each other. It flags dimension conflicts, coordination gaps, material mismatches, blocked clearances and missing details. Each finding names the sheets and the spot to look at.
It is for architects, engineers, MEP contractors, preconstruction teams, developers and plan reviewers. It does not replace the reviewer. It gives them a short, sourced list to confirm or dismiss, so they do not have to scroll through hundreds of sheets.
It uses no per-customer rules. Sheet names, layer habits and title blocks differ between firms, and the model reads them as they come. You upload a set, see a quote for the project, and pay per project.
- Tagline
- Find the drawing errors before the crew does.
- Voice
- PreciseCalmPractical
- Why this name
- Sheetwise: a plain two-part word. It means being wise about sheets, the pages of a drawing set, and it sounds like a careful reviewer.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Full-set upload
Drop in a PDF set of any size. Sheets are split out and sorted by discipline without a naming template.
No member yet
Cross-sheet conflict checks
Compares dimensions, notes, schedules and details across sheets and disciplines to find where they disagree.
No member yet
Findings with locations
Every flag gives the sheet numbers, a marked area on the page and a plain-language reason.
No member yet
Human review queue
Confirm, dismiss or comment on each finding. Nothing is final until a person decides.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12303FBlueprint Ink · primary#F6F3EADrafting Paper · surface#1E2528Graphite · text#E2661FMarker Orange · accent#4E8B6BSurvey Green · data#B9B5A8Pencil Grey · border
Typography
Aa
Sheetwise
PricingAI proposal
The material gives no competitor prices. Pay-per-project follows the opportunity's monetization idea; prices are unset and should be tested against set size, not guessed.
Sample check
Free
- One small set up to 20 sheets
- Findings list with sheet references
- No report export
Per project
Contact us
- Instant quote after upload
- Full findings with marked locations
- Review queue and shareable report
- One revision compare included
Firm account
Contact us
- Volume pricing on many projects
- Shared team workspace
- Contact us for a quote
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. A reviewer exports a findings report with sheet references and sends it to the design team, the contractor or the city. Each recipient opens a link to the flagged sheet locations and can answer there.
- Do they become customers?
- Recipients are often other firms that review drawings themselves, so they could become buyers. Test how many report viewers upload a set of their own within 60 days.
- First customers
- Preconstruction teams at small and mid-size general contractors building commercial projects, and MEP coordinators who check sets before bid.
- Where to reach them
- Hacker News and construction tech forums: Post a demo that reviews a public drawing set, and offer free reviews to commenters who work in preconstruction.
- LinkedIn outreach: Message preconstruction managers and MEP coordinators with a sample findings report from a public set.
- Local contractor associations: Run a short session where members bring a set to review live.
- First milestone
- Five paying preconstruction teams each run at least two real projects, and confirm that flagged issues were real.
Why people already pay for this
- VerifiedHacker News post received 59 points and 56 comments
- Public claimConstruction errors go undetected until construction begins, when correction costs are 10–100x higher.
- Public claimManual drawing review processes are brittle and inefficient, with reviewers scrolling through hundreds to thousands of PDF sheets.
- Public claimCommon drawing errors include dimension conflicts, coordination gaps, material mismatches, missing details, blocked clearances, missing safety details, and specification gaps.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Document processingPDF parsing and page rendering serviceLarge sheets need vector and text extraction plus page images for the model.
- This app · AI modelsMultimodal vision-language model APIReads sheets, notes and details and reasons across disciplines without per-customer rules.
RisksAI inference
- AIFalse positives or missed errors on dense drawings could erode trust fast. Accuracy has to be measured on real sets with reviewers before launch.
- TechnicalSets of 500 to 2000 sheets, scanned PDFs and mixed vector or raster content are costly to process and may be slow or fail on poor files.
- CommercialPer-project pricing gives lumpy revenue, and the cost of a model run per set must stay well below the quote.
- DataDrawings can be confidential or under NDA. Customers may need clear rules on storage, deletion and use of their files.
- RegulatoryUsers may treat findings as code compliance or a stamped review. The product must state that it assists and does not replace a licensed professional.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.