← Results and examples·Client work / Aviation·1 week·Feb 2026

Flying Club Invoice Automation

Client work. Replaced manual paper slip entry with AI-powered extraction — from 10 minutes per slip to 10 seconds.

Document AIMobile AppWorkflow Automation

Key Results

Time per Slip
10 sec
Accuracy
95%
Monthly Hours Saved
8+
Setup Cost
$0*

Client work. This note describes a delivered engagement. Results relate to this project and are not a forecast for another business.

The Problem

A small flying club treasurer was spending hours each month manually transcribing paper flight slips into Quicken for invoicing. Each slip contained handwritten pilot names, Hobbs meter readings, destinations, and charges. Every slip meant:

  • Squinting at handwriting
  • Calculating flight time (Hobbs out → Hobbs in)
  • Multiplying by hourly rate
  • Manually typing into accounting software

With 30-50 flights per month, this meant 5-10 hours of tedious data entry. Time the volunteer treasurer didn't have.

Pain points

  • Handwritten data: Varied penmanship, sometimes illegible
  • Manual calculations: Easy to make arithmetic errors
  • No digital trail: Paper slips get lost, damaged, or misfiled
  • Context switching: Jumping between paper, calculator, and software

The Intervention

We built a mobile-first app that lets the treasurer snap a photo of a flight slip and get structured data back instantly.

How it works

  1. Snap or upload: Take a photo or drag-and-drop on desktop
  2. AI extraction: Vision model reads all fields (pilot, aircraft, times, route)
  3. Review & edit: Verify extracted data, fix any errors
  4. Export to Quicken: One-click CSV export in the exact format Quicken expects

Key insight: Train the model where to look

During testing, the AI grabbed the wrong "name" field. It found the signature line instead of the pilot name. That surfaced three things that mattered:

  • Field-specific prompts: Tell the model exactly what to look for and where
  • Negative examples: "Don't extract from the signature line"
  • Validation rules: Pilot name should match known member list

Architecture

Photo → Vision AI → Structured JSON → Review UI → Quicken CSV
          ↓              ↓
    GPT-5/Gemini   Field validation

Stack

  • App: React Native + Expo (web, iOS, Android)
  • AI: GPT-5 Vision, Gemini 3.1 as fallback
  • Backend: Supabase Edge Functions
  • Export: Quicken-compatible CSV format
FIG. 1.1
Flight Log Home Screen
Home screen with scan, entry, and export
FIG. 1.2
Flight Log History
Flight history with searchable digital records
FIG. 1.3
Flight Log Scan
AI-powered document scanning and extraction
FIG. 1.4
Flight Log Settings
App settings and export configuration

The Outcome

Before → After:

  • Time per slip: 10 minutes → 10 seconds
  • Monthly time: 5-10 hours → 15-30 minutes
  • Error rate: ~5% (calculation mistakes) → under 1%
  • Data retention: Paper in a box → Searchable digital history

Unexpected benefits

  • Historical analysis: "Who flew the most this year?"
  • Faster reconciliation: Match slips to payments instantly
  • Member self-service: Pilots could eventually log their own flights

Key Learnings

  1. Start with the export format: We built backward from Quicken's CSV spec, ensuring perfect compatibility
  2. Handwriting varies wildly: Multi-model approach (try GPT-5, fall back to Gemini) handles edge cases
  3. Human review is a feature: 95% accuracy + quick human verification beats 99% accuracy that takes 10x longer
  4. Small clubs have big problems: Volunteer-run organizations desperately need automation

*Ongoing cost is approximately $0.02-0.05 per slip for AI API calls.

Engagement type: Agent Prototype → Production app
Timeline: 1 week from concept to working app
Demo: flight-log-psi.vercel.app

Tech Stack

React NativeExpoGPT-5GeminiSupabaseTypeScript