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Field Inspector
Field inspection app for automating field ops to office admin
Field Inspection App: AI-Powered Field-to-Office Automation
Developed specifically for technical field teams operating in high-consequence industries like aviation and helicopter service, this application leverages AppSheet and AI Tasks to automate the inspection process. By replacing complex, manual data logging with an intelligent "one-tap" photo analysis, it maximizes safety compliance while drastically reducing technician data entry.
The Workflow: Photo to Corrective Action in Minutes
Traditional inspections on complex avionics are slow and prone to documentation errors. This app seamlessly integrates Gemini to automate the entire lifecycle, reducing critical AOG (Aircraft On Ground) time:
Site & Asset Travel: Navigate to the specific inspection location (shown via integrated map view for "Tail N145AB").
Instant Capture: A field tech performs a check (e.g., testing an avionics radio panel) and captures a single photo with their mobile device.
AI Data Extraction: The app-integrated Gemini AI Task instantly processes the photo to "read" visual indicators:
It extracts critical, technical readings from equipment (e.g., 00.8 V, DATABUS ERROR).
It determines binary PASS/FAIL status based on strict, predefined aviation criteria (bypassing the need for physical tags).
It generates high-fidelity comments and actionable fixed suggestions.
Automated Logging & Closeout: The app automatically logs the inspection, updates the parent equipment record, and generates/posts a Corrective Action workflow for any detected failure—no manual entry required.
Key Capabilities & Benefits:
Integrated with Core Data: View site maps, equipment lists, and a full history of inspections and corrective actions via intuitive bottom navigation.
Drastically Reduced AOG Time: Eliminate hours of manual report generation and data entry for technicians in the field.
High-Fidelity Documentation: Ensure accuracy on critical safety reports by extracting exact, real-world data from photos.
Instant Repair Dispatches: Failures trigger immediate corrective actions, ensuring problems are fixed faster.
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Also see:
tablet mode & fullscreen mode
How we built this app
AppSheet Design & Architecture
1. Relational Data Model
The app is built on a strict relational architecture to ensure data integrity and proper task routing across complex aviation assets:
Users: Manages app access, technician roles, and enables dynamic data filtering (e.g., ensuring techs only see their assigned repair tasks).
Sites: The primary parent table mapping physical locations and aircraft (e.g., "Tail N145AB") using LatLong coordinates.
Equipment: Tracks individual assets (e.g., VHF Radio Panels) linked to specific Sites via alphanumeric IDs.
Inspections: The central transaction table logging high-level inspection events.
Inspection Items: A child table to Inspections, capturing granular, step-by-step checks and photo uploads for specific equipment.
Corrective Actions: A dedicated table that houses automated repair tasks dispatched to specific personnel in the Users table.
2. User Interface & Navigation
Optimized for field technicians, the UI prioritizes minimal typing and rapid visual scanning:
Bottom Navigation: One-tap access to Sites, Inspections, Inspection Items, Corrective Actions, and Equipment.
Interactive Maps: Automatically converts LatLong data into visual map pins so techs can locate precise hangars or aircraft.
Inline Related Records: Site and Equipment profiles feature embedded dashboard decks, displaying thumbnail histories of all past inspections for rapid context.
3. Gemini AI Integration
The core innovation is the "Process for Bot" automation, which eliminates manual field reporting:
Image-Triggered AI: Uploading a photo to an Inspection Item instantly triggers a custom Gemini AI task.
Strict Binary Logic: A specialized prompt evaluates complex visual evidence (multimeter voltages, background MFD error screens) against rigid PASS/FAIL aviation criteria.
Automated Data Entry: The AI seamlessly extracts and maps readings directly into the AppSheet form, auto-populating the [Status], [Readings], [Comments], and [Suggested Fix] columns.
4. Automated Workflows
Data Validation: Ref columns enforce strict matching rules, preventing technicians from assigning incorrect equipment to sites.
Instant Dispatch: If the AI outputs a FAIL status, an AppSheet Bot automatically generates a child record in the Corrective Actions table and notifies the assigned user with the AI's suggested fix.
This is a preview of the data sets used in the app.
This is a high-level model of both the data entities and the UI elements in the app.
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