Varied drawings
Plans used different visual conventions and levels of detail.
Anonymous construction AI case study
Turning architectural floor plans into AI-assisted construction estimates
Computer vision, neural networks, and OCR turned architectural drawings into reviewable regions and structured quantities for AI-assisted construction estimating and approval.
The situation
Construction estimating depended on manually reading varied architectural plans and translating them into measurable quantities.
Rooms, doors, windows, fixtures, and other regions had to be identified before cost work could begin.
The process needed acceleration without removing the cost engineer’s authority to review and approve results.
The challenge
Turn visual plans into structured estimating inputs while keeping uncertain detections reviewable.
Recognition, measurement, reliability, and approval needed one human-controlled workflow.
Structured quantities
Detected regions had to become data suitable for measurement and estimating.
Human approval
Cost engineers needed visible evidence and reliability cues before accepting output.
The solution
AI vision and OCR outlined plan regions, extracted quantities, and connected them to estimating review.
The system treated recognition as the beginning of the budgeting workflow—not an autonomous final answer.
- 01
Recognize the drawing
Detect and outline rooms, doors, windows, fixtures, and other plan regions.
- 02
Extract measurable data
Convert recognized plan content into structured quantities and attributes.
- 03
Estimate and approve
Present reliability checks, budget inputs, cost estimates, and approval controls.
How the work was structured
Every extracted quantity remained connected to the plan evidence that produced it.
Visual overlays made detected regions easy to inspect.
Structured output carried plan information into downstream quantity and cost work.
Reliability review and approval kept the estimator in control of the final decision.
01Upload
Receive the architectural plan.
02Recognize
Detect regions and labels.
03Estimate
Build quantities and cost inputs.
04Approve
Review evidence and result.
The result
Estimators gained a connected path from architectural drawings to structured, reviewable cost inputs.
The documented product combined plan recognition, quantity extraction, AI-assisted budgeting, and human approval.
Plan recognitionfor key architectural regions
Structured quantitiesfor downstream estimating
Human approvalfor cost decisions
Repetitive annotation decreased
The system identified candidate regions and labels for review.
Visual content became data
Recognized plan elements moved into measurable estimating inputs.
Estimators retained control
Reliability cues and approval steps stayed part of the workflow.
Case taxonomy
Searchable by industry, technology, product, and business need.
Industry and product
- Construction
- Cost Engineering
- Architectural Plans
- Estimating Software
Technology and delivery
- Computer Vision
- Neural Networks
- OCR
- Image Recognition
- Structured Extraction
Business need
- Floor-plan Analysis
- Quantity Takeoff
- Reliability Review
- Cost Estimation
- Approval Workflow
Turn drawings into reviewable estimating data
Need AI vision connected to a construction-cost workflow?
Get in touch
Ready to build software that fits your business?
Tell us what you need to build, modernize, automate, or augment with AI. We can start with a focused discussion or a no-risk 1-week trial.
“A fantastic company to work with.” After the initial rapid development project, American Shipping Co. kept two Shinetech developers embedded for nearly four years, supporting internal and external tools and new AI initiatives.
CEO, American Shipping Co. - 5-star Google Review