AI-powered extraction can dramatically accelerate Commercial Real Estate operations.
However, the quality of AI extraction is heavily influenced by the quality and structure of the input documents.
Understanding a few operational best practices can significantly improve:
- extraction accuracy
- workflow efficiency
- review time
- confidence scoring
- downstream automation
1. Use Native PDFs When Possible
Native PDFs typically produce higher extraction accuracy than scanned images.
Native PDFs preserve:
- text structure
- table formatting
- layout metadata
- readable fonts
Scanned documents often require additional OCR processing, which may introduce inaccuracies.
2. Avoid Low-Resolution Documents
Low-quality scans can reduce extraction reliability.
Common issues include:
- blurry text
- skewed pages
- compressed images
- faded content
- cutoff tables
Whenever possible:
- upload original source files
- avoid screenshots of documents
- avoid fax-quality scans
3. Keep Financial Tables Structured
Financial sections such as:
- rent rolls
- operating expenses
- NOI tables
- tenant schedules
are easier to process when tables remain clearly structured.
Merged cells, handwritten edits, or inconsistent spacing may reduce confidence scores.
4. Minimize Handwritten Annotations
Handwritten notes can create ambiguity for AI systems.
If annotations are required:
- keep them separate from core property documents
- avoid writing over important financial tables
- use digital comments where possible
5. Organize Multi-Document Uploads Clearly
Many CRE workflows involve multiple supporting documents:
- OMs
- brochures
- surveys
- floor plans
- financials
Clearly naming files improves workflow organization and downstream automation.
Example:
PropertyName_OM.pdfPropertyName_RentRoll.xlsxPropertyName_Financials.pdf
6. Human Review Remains Important
Even advanced AI systems benefit from human validation.
High-performing workflows combine:
- AI acceleration
- human oversight
- confidence scoring
- operational review
This creates systems that improve continuously over time.
7. Use Feedback to Improve Future Workflows
One of the biggest opportunities in AI-native workflows is learning from execution.
Corrections and edits can help improve:
- extraction quality
- recommendations
- workflow routing
- operational consistency
Over time, workflows become more intelligent through usage.
The Future of AI Extraction
The future of CRE operations is not fully manual workflows or fully autonomous AI.
It is collaborative operational systems where:
- AI handles repetitive extraction tasks
- humans provide strategic judgment
- workflows continuously improve from feedback
This is the direction we believe CRE technology is heading.