Reduced Manual Floor Plan Analysis by 65% and Delivered Database-Ready Spatial Data at Scale

An AI-powered backend system designed to automatically extract structured spatial and dimensional information from architectural floor plan images. The system processes multi-floor floor plan images, identifies individual floors, segments rooms within each floor, and extracts room-level metadata such as room names, polygon boundaries, scaling, and total floor area. Using a combination of computer vision, OCR, and AI models, the system converts unstructured floor plan images into accurate, database-ready structured data for downstream real estate, architecture, and analytics use cases.

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Business Problem
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Architectural floor plans contain critical spatial intelligence—but almost all of it exists as unstructured images or PDFs:
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- Manual inspection required to identify floors and rooms
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- Room boundaries traced by hand with inconsistent accuracy
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- Area calculations prone to scaling and interpretation errors
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- Multi-floor plans required repetitive, floor-by-floor analysis
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- Extracted data was rarely structured or reusable
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This resulted in high operational cost, slow turnaround times, frequent errors, and data that could not be reliably used for analytics, listings, or downstream automation.

NeuraMonks Solution
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NeuraMonks built an AI-Powered Floor Plan Details Extraction System that automatically converts unstructured architectural drawings into accurate, structured, database-ready spatial data.
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Key capabilities delivered:
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- Automated detection and separation of multiple floors from a single plan
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- Room-level image segmentation with precise polygon boundary extraction
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- OCR-based extraction of room names and scale annotations
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- AI-driven normalization and validation of extracted text and labels
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- Accurate conversion of pixel measurements into real-world dimensions
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- Floor-wise and room-wise area computation with consistency checks
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- Structured persistence of all spatial metadata in a relational database
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The system operates fully backend-driven, enabling large-scale batch processing without manual intervention.

Challenges Solved
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- Multi-floor detection in single images with overlapping or unclear layouts
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- High-precision room segmentation across varying drawing styles and noise levels
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- OCR accuracy on cluttered, scanned architectural drawings
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- Reliable scaling interpretation for real-world dimension mapping
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- Data integrity validation across polygons, labels, and computed areas

Why Choose us
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- Outcome-driven AI delivery focused on operational efficiency
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- Pre-GPT era AI expertise in computer vision and spatial intelligence
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- Production-grade backend systems built for scale and reliability
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- On-prem / air-gapped deployment capability for sensitive design data
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- Cost-efficient automation without sacrificing accuracy
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- Domain-aware implementation aligned with real-world AEC workflows

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