GlowUp Local: Using Open-Source AI to Help Local Businesses Improve Their Online Visibility

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

TL;DR

What: GlowUp Local is a working prototype for AI-powered local business visibility and competitor intelligence.
Problem: Small business owners may struggle to understand their online presence, evaluate nearby competitors, and decide which improvements to prioritize.
AI: Gemma via Hugging Face is the language-model component intended to turn structured business information into actionable recommendations.
Tech stack: Python, FastAPI, React, Vite, Gemma, and a planned Google Places API integration.
Target users: Hotels, guesthouses, restaurants, cafés, local shops, and other small businesses in India.
Business potential: Pay-per-audit reports, subscriptions, and partnerships with local marketing professionals.

What if A small business owner could understand their nearby competition, identify gaps in their online presence, and get practical suggestions to improve their visibility on Google Search and Google Maps without becoming an SEO expert?

That's the idea behind GlowUp Local, an AI-powered local visibility and competitor intelligence tool I'm building.

I'm exploring how open-source AI can help local businesses make better-informed decisions about their online presence, while also exploring a potential side hustle that solves a real business problem.

My initial target market is India, starting with businesses such as hotels, guesthouses, cafés, restaurants, and other local businesses in specific neighborhoods and areas.

What I Built

GlowUp Local is a working prototype designed to help local businesses understand their online presence, learn from available information about nearby competitors, and discover possible improvements they can make.

The idea started with a place I know: Varanasi, India.

When people visit Varanasi, they often have questions. Where should they stay? Which hotel or guesthouse should they consider? Which restaurants are worth exploring? Where can they find local products?

Discovering a city can be confusing when you're unfamiliar with it.

But this got me thinking about the other side of the same problem: the businesses that visitors might never discover.

Varanasi has hotels, guesthouses, cafés, restaurants, saree shops, and many other local businesses. Across India, countless small businesses are trying to reach customers in their own neighborhoods.

Some businesses may already offer excellent products or services but struggle to communicate their value online. Others may have incomplete business information or lack the time and expertise to understand how they compare with nearby competitors.

Traditional SEO tools can provide plenty of metrics, but understanding those metrics and turning them into practical actions can be difficult for someone who is busy running a business.

I wanted to explore a simpler approach.

What if a business owner could get a clearer picture of their online presence and a prioritized list of possible improvements in one place?

How GlowUp Local works

The intended workflow is straightforward:

1. Business information: The owner provides details such as their business name, category, address, and the products or services they offer.
2. Nearby competitor discovery: The application uses Google Places API to retrieve available information about relevant nearby businesses.
3. Structured comparison: The application organizes available business information into data that can be used for an evidence-based comparison.
4. AI-powered recommendations: Gemma processes the structured data to help generate prioritized recommendations and practical action steps.
5. Actionable insights: The goal is to help business owners understand what they could improve and decide what to work on first.

For example, a restaurant might receive suggestions to communicate its menu more clearly, improve available business information, describe its services more effectively, or create a better process for asking satisfied customers for honest reviews.

The planned recommendation workflow also includes evidence references, confidence levels, and local marketing content drafts.

The important distinction is that GlowUp Local is not intended to generate generic SEO advice and present it as a personalized competitor analysis. Its recommendations should be grounded in the information available to the application.

And I'm not promising anyone the number-one position on Google. Search visibility depends on many factors, and no tool can guarantee that outcome.

The goal is to make local business intelligence easier to understand and turn it into practical next steps.

Why could this become a side hustle?

GlowUp Local addresses a problem that local businesses may be willing to pay to solve: understanding their online presence without having to perform all the analysis themselves.

Potential customers include:

• Hotels and guesthouses that want to improve how they present themselves online.
• Restaurants and cafés that want to communicate their offerings more effectively.
• Local shops and service providers that want to understand their nearby competitive landscape.
• Freelancers and small marketing agencies that want to assist local businesses with data-informed recommendations.

I'm exploring several possible business models: pay-per-audit reports, a freemium offering, recurring subscriptions, or partnerships with local marketing professionals.

These are potential business models, not validated revenue streams. I haven't acquired my first paying customer yet.

Before monetizing the product, I need to establish whether its recommendations are useful, whether business owners act on them, and whether they are willing to pay for the value provided.

Demo

Code Repository

GitHub: https://github.com/AnmolCanCodes/GlowUp-Local

Why Open Innovation Matters

Open innovation matters to GlowUp Local because I want to explore how accessible AI technology can be used to solve a specific problem for small businesses.

I'm using Gemma via Hugging Face as the language-model layer for generating business recommendations from structured local business and competitor data.

The intended workflow is to retrieve available business information, organize it into structured data, and use Gemma to help produce prioritized recommendations, actionable steps, evidence references, confidence levels, and local marketing content drafts.

Instead of treating an AI model as a black box that simply produces impressive-sounding answers, I want the application to connect its recommendations to available evidence and make the resulting advice useful to a business owner.

Open-weight models such as Gemma give developers an opportunity to experiment with AI-powered applications without depending exclusively on proprietary model providers. Depending on the model's license, deployment setup, and available computing resources, developers can explore different ways to run models and build workflows around their specific use cases.

This is particularly relevant to my project because my target customers are small businesses that may not have the budget to hire SEO specialists or purchase expensive marketing tools.

If I can make business information easier to understand and turn it into useful actions, GlowUp Local could help make certain forms of business analysis more accessible.

Open-source tools also let developers inspect and improve the surrounding application code, experiment with alternative approaches, and invite contributions from the wider developer community.

However, open-source AI is not automatically free to operate, accurate, or commercially viable. Inference costs, data quality, model limitations, and the reliability of the recommendations still need to be addressed.

For me, open innovation is about using accessible technology to build something useful, testing it against a real-world problem, and improving it through experimentation and community feedback.

Prize Categories

Gemma: Gemma helps this project by acting as the AI decision-support layer for the business audit.
The web app:

finds the business and nearby competitors using Google Places
compares the business against them
passes that structured data to Gemma
asks Gemma to generate:
    prioritized recommendations
    action steps
    evidence references from the audit
    confidence levels
    content drafts for local marketing

So Gemma is what turns raw comparison data into useful advice like:

“Improve your GMB profile”
“Add service-area keywords”
“Create more location-specific content”
“Improve review and rating signals”

This is exactly what the project is about: helping local businesses understand where they stand and what to do next.

Traction: Where the Project Stands

GlowUp Local is currently at the working-prototype stage.

I haven't acquired my first real user or test customer yet, so I can't claim validated customer feedback, paying users, or measurable business results.

I want to learn:

• Which recommendations they find genuinely useful.
• Whether the available competitor information helps them identify meaningful improvements.
• Whether the reports are understandable to someone without SEO expertise.
• Which features they would actually pay for.
• What information is missing or unreliable.

This feedback will help me decide what to improve before investing more time in advanced features or monetization.

I'd rather validate the product with real businesses than assume that generating AI recommendations automatically creates value.

Tools & Frameworks

Here's the technology stack behind GlowUp Local:

• Python: Backend development and application logic.
• FastAPI: Backend API layer.
• React + Vite: Frontend application.
• Google Places API: Planned integration for nearby-business discovery and available place information.
• Gemma via Hugging Face: Open-weight language model used for AI-powered business recommendations.

These tools bring together full-stack development, business data, and open-source AI to explore a practical local business use case.