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This is a prototype vision of how a future government service could work. It's not a real service yet, but we're exploring what it could look like. Your feedback will help shape the real service.

Step 4: Review Extracted Fields - Planning AI Walkthrough

Review the AI-extracted planning application fields and verify accuracy

Walkthrough progress

Step 4 of 4 • 3 minutes

Step 4 3 minutes

Review Extracted Fields

Examine the AI-extracted fields and see how they match the original planning application document.

Find your Planning AI application URL in CloudFormation Outputs
The application is ready to process planning documents

Expected outcome

  • All key fields extracted correctly (applicant, address, description)
  • Confidence scores show 95%+ accuracy
  • Extracted data matches original document content
  • You understand time savings vs manual entry

What you should see

After processing completes, you'll see a results section with extracted fields. The exact fields depend on which sample document you uploaded:

Understanding the results

Each extracted field includes:

Field Name
The type of information extracted (e.g., "Applicant Name", "Site Address"). These map to standard planning system fields.
Extracted Value
The actual text extracted from the PDF. This is what would be automatically populated in your planning database.
Confidence Score
AI's confidence level (0-100%). Scores above 95% are highly reliable. Lower scores may need human review.

Verify accuracy

Compare the extracted fields against the original PDF document:

  1. Open the sample PDF you downloaded

    Find it in your downloads folder and open it in a PDF reader

  2. Locate the applicant name in the PDF

    Usually on page 1, in a section like "Applicant Details" or "Personal Information"

  3. Compare with extracted "Applicant Name" field

    Does it match exactly? Notice the AI found this even if it wasn't labeled clearly.

  4. Check the address extraction

    Look for "Site Address" or "Application Site" in the PDF. The AI should have captured the full address including postcode.

  5. Review the development description

    Find the "Proposed Development" or "Description of Works" section. The AI extracts the key summary, not just copying entire paragraphs.

What's impressive: The AI understood document structure and planning terminology. It didn't just scan text - it knew what information to look for and where it typically appears in planning applications.

Key observations

Notice these important capabilities:

Format independence

AI extracted fields regardless of where they appeared on the page. No template matching required - it works with any planning application format.

Accuracy on typed text

97-99% confidence scores on typed PDF documents. Better than human transcription accuracy, with no typos or misread characters.

UK-specific recognition

Correctly identified postcodes, understood "Land adjacent to..." addresses, and recognized planning terminology like "Householder" and "Change of Use".

Speed vs accuracy tradeoff

8-20 seconds to extract 5-6 fields vs 45 minutes manual entry. That's 135-270× faster with higher accuracy.

Real-world integration

In a production system, these extracted fields would:

  • Auto-populate planning database: Extracted values flow directly into your planning system's application record
  • Trigger validation rules: Check extracted postcode against planning zones, verify applicant details against land registry
  • Enable officer review: Low confidence scores (<95%) flag for human verification while high confidence fields auto-approve
  • Link to case management: Automatically create case files, assign to planning officers, generate acknowledgment letters
  • Audit trail: Original PDF stored with extracted metadata for compliance and appeals

Calculate your time savings

Based on what you just saw, estimate your council's savings:

Troubleshooting

Extracted fields don't match document

If extracted values are incorrect or incomplete:

  • Check the source document quality - blurry scans reduce accuracy
  • Ensure text is machine-readable, not handwritten
  • Verify document uses standard planning application format
  • Look at confidence scores - <95% indicates uncertainty
  • Try re-uploading with a higher quality scan
  • Check if PDF contains actual text or is just images

Our sample documents should extract with 97-99% accuracy. If not, there may be a service configuration issue.

Some fields missing entirely

If expected fields aren't extracted at all:

  • The field may not exist in this document type (e.g., outline applications lack detailed descriptions)
  • Check if field is labeled clearly in the PDF - ambiguous labels confuse AI
  • Some fields may need custom extraction rules beyond Textract's default capability
  • Verify the extraction Lambda function includes logic for all required fields
  • Check CloudWatch logs to see what Textract returned
Confidence scores very low (<90%)

If confidence scores are unexpectedly low:

  • Low-quality scans or poor image resolution
  • Unusual fonts or formatting in the PDF
  • Handwritten text (Textract OCR works best on typed text)
  • Document skewed or rotated (Textract auto-corrects but this reduces confidence)
  • Field values in unexpected document locations

In production, you'd set a confidence threshold (e.g., 95%) and flag lower scores for human review.

Note Screenshot placeholder: In production, this page would include screenshots showing the extracted fields display with confidence scores and a side-by-side comparison with the original PDF.

Need help?

If you're stuck, check the troubleshooting section or contact us.