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Engineering Prototype

Contractor AI Job Site: local vision for field documentation

A supervisor needs a repeatable way to capture job-site evidence, keep findings tied to project history, and share a useful report—without making unsupported claims about what an image can prove.

Native iOS implementation Native Android implementation Prototype demonstration

Problem

Job-site photos are easy to capture and hard to organize. The prototype explores a structured workflow for documenting work, applying local image analysis, comparing recent captures, recording notes and issues, and producing reports while the context is still available.

Constraints

The workflow must work on mobile hardware, preserve useful local history, respect privacy, keep platform behavior aligned, and describe analysis as documentation support rather than trained defect diagnosis or code inspection.

Approach

Each capture stays connected to its job, note, timestamp, analysis summary, comparison context, and issue history. Local-first operation supports immediate use while an explicit mode boundary leaves room for a future backend.

Contractor AI Job Site architecture with native mobile capture, on-device analysis, local storage, reporting, and optional backend boundary
Implemented prototype architecture for iOS and Android, with a local-first workflow and an optional future backend boundary.

Engineering

  • Native SwiftUI iOS and Jetpack Compose Android experiences.
  • Camera and photo-library input, field notes, project history, and local JSON/image persistence.
  • On-device signals including labels, readable text, people, rectangles, brightness, sharpness, and prior-capture context on iOS.
  • Native multi-page PDF report generation and platform sharing.

Validation / Results

  • The public project includes current iOS screens and separate platform capability descriptions.
  • The implemented local demo path demonstrates capture/import, persistence, history, analysis summaries, and report export.
  • The analysis language explicitly avoids code-inspection and trained defect-diagnosis claims.

What This Demonstrates

  • Cross-platform mobile product architecture with aligned workflows.
  • Practical on-device computer vision integrated into an operational task.
  • Local-first storage, history, privacy communication, and report generation.
  • Responsible boundaries around uncertain image interpretation.

Relevant HerbDev Services

Computer Vision DevelopmentAI & Machine Learning DevelopmentMobile App Rescue

Explore Deeper

The project site shows the current product experience and separates the iOS and Android implementations. Those pages are project evidence, not customer outcome claims.

Project overview Explore Contractor AI Job Site See the field workflow, current screenshots, analysis boundaries, and platform overview. Platform evidence Compare iOS and Android Start with the iOS implementation, then move to the linked Android capability page. Learn Embedded AI vs Cloud AI Compare local-first, remote, and hybrid inference around connectivity, privacy, latency, cost, and maintenance.

Need a more repeatable mobile field workflow?

HerbDev can help connect capture, local analysis, review, history, and reporting around the real decision your team needs to make.

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