AI PDF Text Validator Tool
Budget: £750 – £1,500 GBP
Project Overview:
We require the development of a desktop application that can intelligently compare up to four mortgage-related PDF documents side by side. These PDFs may contain both text-based and scanned/image-based content. The software must extract key information (via OCR where needed), interpret and normalise variations in field names, and utilise a local AI model running in LM Studio to assess and highlight matches or discrepancies.
The output must be a clean, professional PDF report summarising agreed key fields and custom sections like “Special Conditions.” All processing must be fully offline and GDPR compliant — no external API usage is permitted.
Key Requirements
1. Offline Operation / GDPR Compliance
Must operate 100% offline
All LLM (AI) processing must route through LM Studio running locally on the same machine via http://localhost:1234
No internet connection or cloud API usage allowed
2. User Interface (GUI)
Simple interface allowing the user to:
Select up to 4 PDF files
Click a button to "Compare Documents"
View results in a scrollable UI and optionally export them to a PDF report
GUI must handle blocking operations in background threads to avoid freezing
3. Document Handling
Must accept any combination of:
Text-based PDFs
Scanned/image PDFs
Password-protected PDFs (optional bonus)
Must automatically perform OCR where needed (using Tesseract or similar) to make all content readable for AI
4. Key Field Comparison (via LLM)
Use LM Studio's OpenAI-compatible API to process the documents locally using a hosted model (e.g. LLaMA3 or Mistral)
Pass extracted text to the LLM along with a structured prompt to compare specific fields such as:
Name
Address
Loan amount
Term
Fixed period end date
Expiry date
Purchase price
Valuation
+others (editable list)
5. Field Variations / Help Dictionary
Allow a local dictionary or alias list to assist the AI with variations in field labels (e.g. “Loan Amount” = “Amount Borrowed” = “Principal”)
Ideally, make this list user-editable (e.g. via JSON or embedded GUI editor)
6. Pre-defined Section Extraction
Extract and include free-text sections from the PDFs where found, such as:
"Special Conditions"
"Additional Notes"
This should be editable fields also. These should be clearly labelled and included in the final report and LLM output
7. PDF Report Output
Final output must be saved as a clean, printable PDF
The report must include:
Side-by-side or tabular comparison of all specified fields
Clear indication of matched and mismatched values
Sectioned output of any “Special Conditions” or notes from each document
Document filenames and timestamps
Deliverables
Full working Python project (including GUI, LLM interaction, OCR, and report output)
A user guide / README
Sample output report (PDF) using dummy data
Optional: Installer or packaged .exe (bonus)
Ideal Freelancer Will Have:
Strong Python development skills
Experience with OCR and PDF parsing
Familiarity with local LLMs (especially LM Studio)
Awareness of GDPR constraints in software design
Strong communication and ability to deliver well-commented code
Additional Notes
If LM Studio API changes or is unavailable, the system must fail gracefully and notify the user
We require the development of a desktop application that can intelligently compare up to four mortgage-related PDF documents side by side. These PDFs may contain both text-based and scanned/image-based content. The software must extract key information (via OCR where needed), interpret and normalise variations in field names, and utilise a local AI model running in LM Studio to assess and highlight matches or discrepancies.
The output must be a clean, professional PDF report summarising agreed key fields and custom sections like “Special Conditions.” All processing must be fully offline and GDPR compliant — no external API usage is permitted.
Key Requirements
1. Offline Operation / GDPR Compliance
Must operate 100% offline
All LLM (AI) processing must route through LM Studio running locally on the same machine via http://localhost:1234
No internet connection or cloud API usage allowed
2. User Interface (GUI)
Simple interface allowing the user to:
Select up to 4 PDF files
Click a button to "Compare Documents"
View results in a scrollable UI and optionally export them to a PDF report
GUI must handle blocking operations in background threads to avoid freezing
3. Document Handling
Must accept any combination of:
Text-based PDFs
Scanned/image PDFs
Password-protected PDFs (optional bonus)
Must automatically perform OCR where needed (using Tesseract or similar) to make all content readable for AI
4. Key Field Comparison (via LLM)
Use LM Studio's OpenAI-compatible API to process the documents locally using a hosted model (e.g. LLaMA3 or Mistral)
Pass extracted text to the LLM along with a structured prompt to compare specific fields such as:
Name
Address
Loan amount
Term
Fixed period end date
Expiry date
Purchase price
Valuation
+others (editable list)
5. Field Variations / Help Dictionary
Allow a local dictionary or alias list to assist the AI with variations in field labels (e.g. “Loan Amount” = “Amount Borrowed” = “Principal”)
Ideally, make this list user-editable (e.g. via JSON or embedded GUI editor)
6. Pre-defined Section Extraction
Extract and include free-text sections from the PDFs where found, such as:
"Special Conditions"
"Additional Notes"
This should be editable fields also. These should be clearly labelled and included in the final report and LLM output
7. PDF Report Output
Final output must be saved as a clean, printable PDF
The report must include:
Side-by-side or tabular comparison of all specified fields
Clear indication of matched and mismatched values
Sectioned output of any “Special Conditions” or notes from each document
Document filenames and timestamps
Deliverables
Full working Python project (including GUI, LLM interaction, OCR, and report output)
A user guide / README
Sample output report (PDF) using dummy data
Optional: Installer or packaged .exe (bonus)
Ideal Freelancer Will Have:
Strong Python development skills
Experience with OCR and PDF parsing
Familiarity with local LLMs (especially LM Studio)
Awareness of GDPR constraints in software design
Strong communication and ability to deliver well-commented code
Additional Notes
If LM Studio API changes or is unavailable, the system must fail gracefully and notify the user