Work/Finding Neno
Case Study

FindingNeno

Role
Full-stack engineer
Timeline
2024
Type
Lost-pet reunion platform
Stack
ReactFlaskPostgreSQLGeolocationPytest
Github repo
Replica Model - Case study details belowReplica Model Only

Dashboard

🐾

Max

Golden Retriever

Brooklyn, NY • 3d lost

Active
🐾

Luna

Siamese Cat

Manhattan, NY • 1d lost

Active
✓

Buddy

Black Lab

Queens, NY • 7d lost

Found
New Missing Report
New Sighting
Overview

Helping reunite lost pets through location-led workflows

Finding Neno was built to streamline the path from first sighting to owner response. The app combines reporting, map context, and profile data in one operational flow.

The product focus is practical coordination: less time posting scattered updates, more time acting on verified location information.

Geo
Location-aware reporting model
Web + API
Frontend and backend separation
Tested
Service-level backend validation
Problem

Lost-pet coordination is usually fragmented and delayed

Families and communities often rely on disconnected social posts and manual follow-up to track sightings, creating delays and duplicate effort.

"When every minute matters, reporting and verification workflows need to be immediate and clear."

The goal was to provide one structured workflow where reports, map context, and profile records stay connected.

Design

Designed around urgency, clarity, and action

The interface emphasizes fast report creation, clear status indicators, and map-first context so users can prioritize likely matches quickly.

Navigation and data grouping were built to support rapid updates from multiple contributors without overwhelming users.

Engineering

Full-stack workflow with geospatial context

The architecture pairs a React client with Flask APIs and PostgreSQL-backed domain data for users, pets, and sighting records.

Service-level separation and testing support maintainability as workflows evolve across authentication, report handling, and location logic.

Challenge

Sighting data quality varies and can create noisy signals for owners.

Solution

Structured report inputs and location context reduced ambiguity and made it easier to triage likely matches.

Challenge

Cross-screen coordination between map state and profile status introduced complexity.

Solution

Implemented predictable API and state flows so updates remain synchronized across the reporting and tracking experience.

Re
React
Frontend product experience
Fl
Flask
Backend API services
PG
PostgreSQL
Persistent relational data
Geo
Geospatial logic
Map and distance context
Py
Pytest
Backend test coverage
UX
Workflow design
Urgent task orchestration
Lessons

What this project reinforced

Finding Neno strengthened my ability to design and build systems where human urgency and software reliability need to align.

01
Clear data capture beats clever assumptions. In high-pressure workflows, explicit report structure is more valuable than trying to infer meaning later.
02
Map context improves decision speed. Spatial framing helps users quickly prioritize plausible sightings and reduce unnecessary follow-up.
03
Service boundaries improve scale-readiness. Keeping domain services separated made it easier to expand features without rewriting core flows.
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