Day 6: Building Cursor for PDFs - Edit Mode for Documents

What if you could edit PDFs like you edit code? We're building it.

The PDF Problem Nobody Solved

Here's a scenario that happens millions of times per day:

You receive a 30-page insurance application. It's a scanned PDF: some fields are pre-filled, most are blank. You need to fill it with information from your CRM, HR database, and previous submissions, in a secure, privacy-respecting manner.

Current workflow:

Print the PDF (yes, really)
Fill it by hand (30-60 minutes)
Scan it back in
Hope the recipient can read your handwriting
Repeat when they inevitably ask for corrections

Or, if you're digital:

Open PDF in Adobe Acrobat
Manually click each field
Copy-paste from 5 different systems
Miss fields because they're on page 27
Submit and pray

This is 2025. We have AI that can write code, generate images, and pass the bar exam. Why are we still filling forms like it's 1995?

The Cursor Insight: What if PDFs Had an Edit Mode?

If you've used Cursor, you know the magic: you describe what you want, the AI suggests code changes, you review and accept. It's collaborative. It's fast. It's delightful.

What if PDFs worked the same way?

Upload a PDF (scanned, native, or handwritten. Doesn't matter)
Paste your context: "Applicant: John Smith, Employer: ACME Inc., Start date: 01/01/2024"
AI detects all fillable fields, maps your context to the form, and auto-fills
You review in a clean UI, make corrections, and export

No printing. No manual field-by-field clicking. No context switching between 5 systems.

That's what we're building. And we're calling for beta users.

How It Works: The Technology Behind the Magic

We're building this for production use, not as a demo. Here's how it works at a high level:

OCR + Layout Detection

Smart field detection:

If a PDF has native form fields, we use those (perfect accuracy, no OCR needed)
For scanned PDFs, we use modern OCR that understands document layout
We detect fillable areas by looking for underlines, checkboxes, and empty boxes

Intelligent mapping:

OCR labels are messy: "Applicant Name", "Name of Applicant:", "Primary Applicant:" all mean the same thing
We use a hybrid approach: fast fuzzy matching for obvious cases, AI-powered semantic matching for edge cases
This ensures we map your context data to the right fields, even when labels don't match exactly

PDF filling:

Native PDF forms: Fill directly into form fields (perfect fidelity)
Scanned PDFs: Overlay text at the exact detected locations (preserves layout)

Audit trail:

Every auto-fill decision is logged with confidence scores
Essential for compliance (healthcare, finance, legal) and debugging
Your corrections help improve the system over time

The Hidden Complexity: What Makes This Hard

Building "Cursor for PDFs" sounds simple. It's not. Here's what we're solving:

1. Scanned PDFs Have No Structure

Unlike native PDFs (which have text layers and form fields), scanned PDFs are just images. We need to:

Extract text using OCR
Understand document structure (where are the fields?)
Match labels like "Applicant Name:" with the nearby space

Modern OCR gives us text with location information; that's the difference between "a string of characters" and "a document you can reason about."

2. Handwritten Forms Are Ambiguous

Handwritten text is hard for computers to read. Is that a '1' or an 'l'? We use specialized OCR trained on handwriting and context-aware correction (if the field says "Year" and we see "l999", we correct it to "1999").

3. Mapping is a Semantic Problem

Form labels vary wildly: "Applicant Name", "Name of Applicant:", "Primary Applicant:" all mean the same thing. We need to map these to your context fields. This requires understanding meaning, not just string matching.

4. Multi-Page Context Requires Memory

A 30-page form might have applicant info on page 1, employer info on page 15, and beneficiary info on page 23. Fields on later pages need context from earlier pages. We process the entire document first, then fill it intelligently.

5. Tables and Repeating Sections

Forms often have tables ("List all dependents") or repeating sections ("Previous Employers"). These require structured extraction, i.e., detecting the pattern and filling multiple rows from your data.

Production Considerations: What We're Building For

Privacy & Compliance

Documents contain sensitive information, including SSNs, medical records, and financial data. We support:

Local processing for documents that can't leave your infrastructure
Audit logs: Who filled what, when, with what confidence
Redaction: Auto-redact sensitive fields in logs (SSNs, credit cards) with our state-of-the-art Sentinel PII model

Performance

A 30-page form processes in under a minute. Real-time editing feels instant. We're building for scale and to keep costs reasonable.

What We've Built (And What's Next)

Current State (Week 1)

✅ Core pipeline working:

PDF upload and processing (works with native and scanned PDFs)
Automatic field detection
Smart mapping from your context to form fields
PDF filling with audit trails

✅ Testing:

End-to-end pipeline tested
Generates filled PDFs with quality assurance records

In Progress (This Week)

🚧 Three-column UI:

Clean interface for context input, PDF editing, and quality review
Real-time field editing
Status indicators to show which pages need attention

🚧 Multi-page optimization:

Fast processing for 30+ page forms
Progress tracking

Coming Soon

🔜 Table extraction:

Detect repeating sections (dependents, employers, etc.)
Extract to structured JSON arrays
Auto-fill from context lists

🔜 Template library:

Pre-trained on common forms (W4, I9, insurance applications)
Zero-shot performance on new templates
Community-contributed templates

🔜 Handwriting specialization:

Better OCR for handwritten forms
Smart handling of ambiguous characters
Workflow for reviewing low-confidence handwriting

🔜 Enterprise features:

SSO/SAML authentication
Role-based access control
Team collaboration (multiple reviewers)
Version control (track form changes over time)

Join the Beta: We Need Your Forms

We're looking for early beta users to test Cursor for PDFs.

Ideal beta users:

Process 10+ multi-page forms per week
Deal with scanned PDFs, government forms, or legacy documents
Have organizational context in CRMs, databases, or spreadsheets
Care about accuracy, compliance, and audit trails

What you get:

Early access
Priority feature requests
Direct line to the engineering team
Free credits for OCR API usage

What we need from you:

Sample forms (we'll sign NDAs for sensitive documents)
Feedback on UI/UX
Accuracy reports (which fields work, which don't)
Use cases we haven't thought of

Apply for Beta Access

Email: dev@cernisintelligence.com

Include:

Your name + organization
Use case (what forms do you fill out?)
Volume (forms per week/month)
Pain points (what's broken today?)
Sample form (optional, but helpful)

We'll onboard 50 beta users in the next couple of weeks.