AI window takeoff for enterprise estimating teams — auditable accuracy, your tenant, your data
Markovate’s AI Blueprint Classifier, powered by CADIAM™ Vision, automates exterior window detection across full architectural drawing sets, computes per-window dimensions against the drawing’s own scale reference, and delivers structured takeoff spreadsheets — on a tenant-isolated, region-resident, retention-controlled deployment with per-detection audit traceability built into every output.
Key Features
Automated Exterior Window Detection Across Full Drawing Sets
The AI scans every page of an uploaded blueprint PDF and detects exterior windows — punch and storefront — across the architectural scales used in commercial plan sets. Multi-page drawing sets with mixed scales are processed in a single run, with the estimator selecting which pages are in scope.
Per-Window Dimension with Auditable Trace to Source Annotation
Each detection’s width is computed from the drawing’s own scale reference and is traceable back to a source annotation on the drawing. Every dimension is independently verifiable — your estimator can see exactly which annotation, on which page, drove every number in the output. No black-box detections, no unexplained measurements.
Confidence Scoring on Every Detection
Every detection ships with a confidence score. Lower-confidence detections are flagged for estimator attention in the review interface. The estimator decides — the AI surfaces, never overrides.
Structured Excel / CSV Export, Ready for Quoting
Outputs are delivered as structured Excel or CSV files — window location, width, page number, confidence score per detection — ready for direct import into existing quoting workflows. The data format is open and portable: you own your detection outputs and can export at any time without re-ingestion into a proprietary schema.
Benefits
Manual blueprint scanning that took hours per drawing set is replaced with minutes of AI processing plus targeted estimator review on flagged detections only.
Every detection traces back to a source annotation on the drawing. No black-box guesses — each output is independently verifiable against the estimator’s own reference.
Deployed in your own tenant on Azure US or Azure EU per your residency requirement. In-memory drawing handling, customer-defined retention on detection metadata, and no cross-customer model training.
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