FetchUpdate – AI Pet Sitter Assistant

FetchUpdate – The AI Assistant for Local Pet Sitters 🐾

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

FetchUpdate is an AI tool designed for independent dog walkers and local pet sitters.

Pet sitters often spend 1–2 hours every evening manually texting individual updates to anxious owners. FetchUpdate automates this chore: the sitter takes a daily photo of the pet, selects their profile, and the tool uses a vision model paired with stored pet care details to instantly draft a warm, highly personalized SMS update ready to copy and send.

Demo

👉 Live App: FetchUpdate on Streamlit Cloud

📁 GitHub Repository: github.com/2AJ0/fetchupdate

How I Built It

• Database (MongoDB Atlas): Stores structured pet profiles, including names, breeds, special instructions (e.g., "Loves walks on paved trails"), owner details, and tone preferences.
• Multimodal AI Pipeline (OpenRouter / Gemini / Open Models): Accepts the uploaded image along with the retrieved MongoDB profile and prompts an open-weight vision model to describe the activity and generate a tailored SMS update.
• Frontend & Deployment (Streamlit): Built using Python and Streamlit, deployed seamlessly on Streamlit Community Cloud.

Why Does Open Innovation Matter?

For local side hustles and micro-businesses, open innovation makes software viable in two key ways:

1. Privacy First: Sitter notes contain sensitive client information (home locations, daily schedules, dog routines). Running open-weight vision models ensures client data stays secure without being harvested by closed corporate APIs.
2. Near-Zero Cost Structure: Closed API pricing quickly kills margins for small solo sitters charging $15–$30 per walk. Leveraging open models keeps operational overhead low enough to run a profitable local SaaS product.

My First User

I tested FetchUpdate with a local test profile for Merku, a Golden Retriever who loves long walks on paved trails.

Uploading a photo of Merku generated the following update in seconds:

"Hey Athul! Merku is sitting patiently, looking like the goodest boy. He's probably dreaming of soft treats and a nice long walk on the paved trails! 🐶"

The prompt correctly synthesized both the visual context from the photo and the background care instructions from MongoDB.

Prize Categories

• Best Use of MongoDB Atlas: Used MongoDB Atlas to store long-term pet care instructions, owner metadata, and personality traits.
• Best Use of Open Models / Gemma: Used open-weight vision models to generate contextual text updates from images.