AI Case Study · Document Processing · Nonprofit
Illinois Fatherhood Initiative (IFI)
Removing Friction So Volunteers Can Focus on What Matters
A batch document processing pipeline that converts essay contest submissions into structured records — eliminating manual data entry so volunteers focus on evaluation, not administration.
Batch
Document Processing
PDFs + Scans
Input Types Supported
Google Vision
OCR Layer
Groq LLM
Field Extraction
Human-in-the-Loop
Validation
The Challenge
IFI's essay contest depends on volunteers reading and scoring student essays. The bottleneck wasn't the reading — it was the manual data entry required before volunteers could even begin.
The Illinois Fatherhood Initiative runs an annual essay contest where students submit essays about their relationship with their father. Volunteers review each submission and score it. To manage the contest at scale, IFI needs clean, consistent submission metadata — student name, school, teacher, father name, and other required fields — so entries can be tracked, organized, and reported.
As participation increased, volunteers and staff spent a disproportionate amount of time on pre-review administration: manually extracting submission details from each essay and typing them into a tracker. This step was repetitive, slowed down the review pipeline, and introduced avoidable errors (misspellings, inconsistent formatting, missing fields) that later required cleanup. The goal was to reduce administrative overhead without compromising data quality, auditability, or the fairness of the review process.
The Solution
We implemented an assisted document-processing workflow that converts submissions into structured records. The workflow has two primary stages: 1) extract the raw text reliably from PDFs, scans, and handwriting, and 2) identify and normalize the specific fields IFI needs for tracking. The output is a structured record per submission that is ready for review, with confidence signals and flags where manual verification is required.
Pipeline stages
- 1OCR & Text Extraction — Google Cloud Vision for mixed-quality scans, varied formatting, and handwriting
- 2Field Identification — Groq-hosted LLM to map unstructured text → required contest fields
- 3Validation & Review — Rule-based checks + human-in-the-loop verification before finalization
Document processing workflow
Placeholder for workflow screenshot
Pipeline Capabilities → Volunteer Impact
How each technical capability serves the mission
| Capability | Impact |
|---|---|
| OCR Text Extraction | Reliably extracts text from PDFs, scans, and handwritten documents — no fixed templates required |
| LLM Field Identification | Identifies and normalizes required fields (student, school, teacher, father) from unstructured essay text |
| Validation Rules | Flags missing fields, inconsistent formatting, and conflicts before data reaches reviewers |
| Human-in-the-Loop Review | Reviewers confirm or correct extracted values quickly — auditability and data quality preserved |
| Exception Routing | Submissions that fail validation route to manual correction before finalization |
AI & Technical Architecture
OCR & Text Extraction
Google Cloud Vision
Extracts machine-readable text from PDFs, scans, and handwriting
Field Identification
Groq-hosted LLM
Identifies and extracts required metadata from unstructured essay text
Validation
Rule-based checks
Missing fields, name validation, format consistency, conflict detection
Review Interface
Custom workflow
Presents extracted fields alongside original document for verification
Input Types
PDF, scanned images, handwriting
Handles mixed-quality documents common in nonprofit workflows
Results
The primary impact was time reclaimed from repetitive data entry. Volunteers were able to move more quickly into the actual review work because submissions were already organized with the required metadata.
- ✓Volunteers focus on evaluation instead of manual keying
- ✓Improved throughput and reduced cost of cleanup work later in the process
- ✓Increased program capacity by keeping volunteer effort focused on meaningful review
- ✓Extraction validated and verified — not accepted blindly; auditability preserved
Relevant to Insurance & MGA Submission Intake
Batch ingestion of scanned, unstructured documents — extracted, validated, and routed automatically with zero manual entry. This is exactly the problem MGAs face with high-volume PDF and ACORD submissions arriving daily.
Same pipeline, same outcome: your team reviews decisions, not data. The same principles that eliminate manual essay metadata entry for IFI eliminate manual ACORD field entry for underwriting teams.
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