The Property Platform Indexed Listings on Seller Tags. We Built the Extractor that Reads the plan.
Property search ran on whatever a seller typed into a form. Neuramonks built an AI floor plan data extraction system that reads the floor plan image and returns room labels, dimensions, and fixtures as database-ready metadata
Delivered for the property platform
- 65%
- Less manual analysis
- Structured
- Data from an image
- Room-level
- Dimensions extracted
- Search
- Filters on real data
Delivered for the L&D team
- 65%
- Less manual analysis
- Structured
- Data from an image
- Room-level
- Dimensions extracted
- Search
- Filters on real data
The Client's Problem
Search Filters Built on What Someone Typed, not What the Plan Shows.
- Listings indexed on seller-entered tags
- Metadata extracted from the plan itself
- Room sizes only where a field was filled
- Dimensions read per room, at scale
- Plans analysed by hand for each listing
- Manual analysis down 65%
What We Delivered
Six Pieces of Work, one Podcast Automation Pipeline.
- 01 · Computer Vision
- Plan structure detection
- Finds walls, rooms, doors and openings in the image.
- 02 · Applied AI
- Room labelling
- Names each space from its layout and annotations.
- 03 · Voice AI
- Human-sounding narration
- Natural delivery, not a robotic document readout.
- 04 · Audio Engineering
- Assembly and mastering
- Segments stitched, levelled and topped and tailed.
- 05 · Product Engineering
- Review console
- L&D approves each episode before the team hears it.
- 06 · Integration
- Distribution to sales
- Episodes and transcripts pushed to where reps listen.
- Across all six · Listing and plan data
- Encrypted end to end
- Plan images and extracted records encrypted in transit and at rest.
- Access-controlled by role
- Agents and platform staff see only their own scope.
- Provenance retained
- Every attribute traceable to the plan it was read from.
Search Running on Data Nobody Verified?
We scope which pieces your build actually needs, in 30 minutes.
The Result
Search that Runs on the Property, not the Paperwork.
Once every listing carries room labels, dimensions and fixtures read from its own plan, a buyer can filter on what a property actually is. The platform stopped depending on whatever a seller chose to type, and dimensional accuracy came in roughly 35% higher than the manual takeoff it replaced.
65%
Less manual floor plan analysis, with structured spatial data produced for every listing at scale.
How The Engagement Ran
Four Phases, Image to Search Index.
- Phase 1
- Discovery
- Listing data gaps mapped with the platform team.
- Phase 2
- Detection model
- Wall, room and opening detection trained on real plans.
- Phase 3
- Attribute extraction
- Labels, dimensions and fixtures read per room.
- Phase 4
- Integration and rollout
- Records wired into the listing database and search.
- Computer Vision
- Deep Learning
- OCR
- Data pipeline
- Search integration
Why Neuramonks
Why did the Property Platform Choose Neuramonks?
- Outcome-driven delivery
- Extraction accuracy targets set before development started.
- Spatial AI since 2018
- Eight years building OCR and computer vision development models for property and AEC platforms.
- Built for catalogue scale
- Every listing processed, not a sample set.
- Deployable on your terms
- On-premises or air-gapped where policy requires it.
Common Questions
What do Teams ask about this Build?
What does floor plan extraction produce?
Room labels, per-room dimensions and marked fixtures, returned as structured records a property database can index and query.
Why not rely on seller-entered tags?
Tags reflect what someone chose to type. Reading the plan gives every listing the same attributes, measured the same way.
Does it work on plans from different sources?
Plans vary in style and annotation, so detection was tuned to hold up across the formats the platform actually receives.
What did the engagement include?
Plan structure detection, room labelling, dimension extraction, fixture detection, the database-ready output and search integration.
How does this compare to manual takeoff or generic OCR floor plan software?
Manual takeoff carries roughly 30-40% more dimensional error and repeats the same work per listing. Generic OCR reads text but not room geometry. This database-ready floor plan extraction system combines detection, labelling and dimension extraction in one pass, cutting manual analysis 65%.
