Christianson Web Studio
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AI / Outdoor Technology•In Development

FieldMonocle

AI-assisted outdoor identification platform for flora, fauna, and field safety.

CLIENT / ROLEFieldMonocle (Internal Studio Product)
LOCATIONMassachusetts
PRIMARY ARCHITECTURENext.js PWA & Computer Vision AI
PROJECT STATUSIn Development
Live Deployment: fieldmonocle.com
Visit Live Webpage (fieldmonocle.com)
fieldmonocle.com
FieldMonocle hero view
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01 // OVERVIEW

Project Context & Purpose

FieldMonocle is a specialized outdoor companion application engineered for hikers, foragers, and outdoor enthusiasts. Rather than returning naive single-answer guesses, the engine presents ranked candidates, lookalike hazards, and explicit safety disclaimers.

02 // THE CHALLENGE

The Operational Problem

Many consumer AI vision tools give confident yet dangerous identifications for wild mushrooms and plants. In the outdoors, mistaking a toxic specimen for an edible one carries severe health risks, and spotty cell service frequently breaks conventional web apps.

03 // THE STRATEGY

Studio Approach

Designed a Progressive Web App capable of offline observation caching, coupled with structured prompts that prioritize toxicity cautions, morphological key verification, and clear confidence intervals.

04 // IMPLEMENTATION

The Delivered Solution

Built a full-stack platform featuring photo upload analysis, user field journals, species classification databases, safety annotations, and Stripe subscription billing for advanced query volumes.

Delivered System Capabilities:

  • Multi-specimen classification: plants, mushrooms, insects, trees, animal tracks
  • Toxic lookalike warnings and strict caution prompts for foraged items
  • Progressive Web App (PWA) with offline capture and home screen installation
  • User observation history logged in PostgreSQL database
  • Tiered subscription access powered by Stripe customer billing
05 // SYSTEM ARCHITECTURE & DATA PIPELINE

Engineering Architecture & Decisions

Full-stack Progressive Web App (PWA) combining client-side offline observation staging with vision AI inference, PostgreSQL persistence, and Stripe subscription lifecycle management.

Core Architectural Implementation:

Local-first IndexedDB storage (idb-keyval) queueing field captures until network connectivity returns.
Client-side image compression before multipart or JSON upload to /api/identify.
Zod schema validation enforcing structured candidates, Green/Yellow/Red safety tiers, and toxic lookalikes.
Cryptographic Stripe webhook signature verification alongside USFWS conservation authority checks.
06 // INTERFACE ARTIFACTS

Work In Detail

FieldMonocle mobile identification UI

Mobile field capture and multi-candidate species identification

FieldMonocle observation details and toxicity alerts

Detailed field observation card with safety warnings and lookalikes

FieldMonocle logo mark

Brand mark and field iconography system

07 // FACTUAL OUTCOME

System Delivery & Results

Built a robust full-stack AI web application proving how custom vision pipelines can be paired with defensive safety guardrails and scalable cloud databases.

TECHNOLOGIES DEPLOYED:Next.js PWAComputer Vision AIPostgreSQL / SupabaseStripeTailwind CSS

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