We Chose Human Intelligence Over AI — And It Worked Better Than We Hoped


At a recent AI for Good Hackathon, our team, Rooted in Resilience, set out to solve a real-world challenge brought to us by Refugee & Immigrant Transitions (RIT) — a nonprofit that supports immigrants and refugees through English language and job-readiness programs.
Their process for collecting feedback was painfully analog:
Distribute printed surveys.
Students handwrite their responses.
Volunteers manually enter data into spreadsheets.
Occasionally, mistakes creep in.
Always, it takes too much time.
So we asked the obvious question: Can AI help?
Why AI Wasn’t the Best Answer
Like most teams at the hackathon, we started with modern LLMs — GPT-4, Claude, Gemini — to interpret scanned forms. The results?
Text fields: surprisingly accurate.
Checkboxes: 40–60% failure rate, even with top-tier models.
Cost: way too high for a nonprofit running on limited funds.
We realized quickly that no matter how sophisticated the AI, checkbox detection was too flaky — and definitely not budget-friendly.
So We Did the Unthinkable:
We Chose to Be Intelligent Instead
Instead of brute-forcing AI into a problem it wasn’t designed to solve, we designed around it.
We built SnapScan — a complete survey automation system that works without needing advanced AI for every input. Here's how it works:
SnapScan: Our Solution
1. Custom Form Generator
We built a tool that lets volunteers recreate RIT’s paper forms — preserving the layout and structure, but also adding two major upgrades:
Exact box coordinate mapping (crucial for visual detection)
A unique QR code to tag each form to the right spreadsheet
2. Scan and Detect
Once forms are filled and scanned:
The QR code tells the system which questions and spreadsheet to sync with
OpenCV checks for checkbox marks using pixel-perfect box mapping
OCR handles the occasional handwritten free-text fields
3. Auto-Sync to Google Sheets
Every response is instantly logged into a shared spreadsheet — no sorting, no manual entry, no fuss.
A Better Demo Than AI
Here’s how we introduced it at the hackathon:
"This is the form RIT currently uses — built in Word. Here’s what it looks like in our system. Same format, but now there’s a QR code."
"Alberto — our fictional RIT volunteer — prints and distributes the forms. Once they’re filled out, he uploads all scanned forms to a Google Drive folder."
"SnapScan reads the QR code, detects the checkboxes, extracts the handwriting, and updates the spreadsheet. All automatically."
Why It Worked
100% checkbox accuracy thanks to custom layout and OpenCV
Zero per-form AI costs — a huge win for nonprofits
Fully automated pipeline — from scan to sheet
Volunteer-friendly — no training needed, no tech knowledge required
We didn’t win the hackathon.
We didn’t use the flashiest LLM stack.
We didn’t even qualify under the "AI requirement" strictly.
But we built something that actually works — accurately, affordably, and at scale — for a nonprofit that truly needed it.
And sometimes, that’s worth more than a trophy.
The Team:
Sanath Swaroop Mulky
Aditi Dani
Manav Chandani
Prithvi Elancherran
Armin Foroughi
The Tech Stack:
OpenCV (checkbox detection)
pytesseract OCR(text extraction)
React + Firebase (frontend & auth)
Google Drive + Sheets API (storage & sync)
Node.js (backend)
Want to Try SnapScan or Contribute?
We're exploring open-sourcing the tool for other nonprofits.
Leave a comment or DM if you're interested in collaborating or piloting it with your organization.
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