This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
Shakti 🌱 — A Small Business Assistant Built for the People Who Need It Most
What if a vegetable seller could manage customer orders, track stock, and prepare WhatsApp messages without spending hours doing everything manually?
Not every small business has the budget for expensive software. Some sellers manage their entire business through handwritten notes, memory, and WhatsApp conversations.
I wanted to build something that could make their day a little easier.
That's how Shakti started.
What I Built
Shakti is an AI-powered business assistant designed for small vegetable sellers who want to spend less time managing orders and more time running their business.
Here's what it brings together:
- 🥦 Stock management: Keep track of available vegetables and quantities.
- 💬 Natural-language order parsing: Turn informal customer messages into structured order previews.
- 🌍 Multilingual interaction: Designed for everyday messages in Marathi, Hindi, English, and transliterated Marathi.
- 👥 Customer management: Maintain customer details and order history.
- 📱 WhatsApp-ready messages: Prepare personalised customer messages with click-to-send links.
- ✅ Seller confirmation: Review extracted items and quantities before confirming an order.
Imagine receiving a message like:
"Udya 2 kilo tomato ani 1 kilo kanda pathva."
Instead of manually interpreting and rewriting the order, Shakti is designed to extract the items and quantities into a structured preview that the seller can review.
The goal isn't to replace the seller's judgement. It's to remove repetitive work.
Who could pay for it?
Independent vegetable vendors, home-based sellers, and small produce businesses that need a lightweight way to organise orders and communicate with customers.
A future business model could be a free basic tier with paid features for higher order volumes, reports, and advanced customer management.
Demo
🎥 Watch Shakti in action:
At https://github.com/DishaKhanapurkar/shakti
Or
https://drive.google.com/file/d/136YrImOoEITWwG1fJPeSJeUEOGMeXXco/view?usp=sharing
Code
💻 GitHub repository: https://github.com/DishaKhanapurkar/shakti
Built with open-source tools and designed to be practical, understandable, and extensible.
How I Built It
Shakti uses a Python backend built with FastAPI and SQLite, with a lightweight HTML, CSS, and JavaScript dashboard.
The AI integration is built around Gemma 3 1B Instruct, Google's open-weight model, using Hugging Face Transformers.
Here's the architecture:
- Frontend: A dashboard for stock, customers, and order previews.
- Backend: FastAPI endpoints handle inventory, customer data, and order workflows.
- Database: SQLite stores the application's business data.
- AI layer: Gemma is integrated to interpret natural-language orders and return structured information.
- Fallback parsing: A rule-based parser provides a simpler alternative when AI parsing is unavailable.
- Human-in-the-loop workflow: The seller reviews the result before an order is confirmed.
One design decision matters here: language models can misinterpret quantities, item names, or informal messages. That's why the order workflow includes seller review instead of treating every AI-generated result as automatically correct.
For a small business, a confident-looking mistake can be more expensive than a little extra manual work.
Why Does Open Innovation Matter?
For Shakti, open innovation is about more than using a different model.
It's about making useful AI more adaptable to real-world needs.
1. Language is part of the product.
Customers don't always write polished English. They use regional languages, abbreviations, mixed-language messages, and transliterated text. An open-weight model gives developers room to experiment with prompts, evaluation examples, and model configurations for these situations.
2. Developers have more control over the AI layer.
Using Gemma through Transformers gives the project a model integration that can be inspected, tested, and adapted. It also creates the possibility of running inference locally after the model is downloaded, depending on the deployment and available hardware.
3. Cost matters to small businesses.
A tool for a small vendor needs a realistic path to affordable operation. Open-weight models give developers more flexibility to evaluate hosting costs, local inference, and deployment options instead of depending entirely on a closed AI API.
4. Open tools make experimentation accessible.
FastAPI, SQLite, and Transformers make it possible to build and iterate on the complete application without starting with an expensive infrastructure stack.
The bigger idea is simple: useful AI shouldn't be limited to businesses that can afford a large software budget.
What I Want to Improve Next
Shakti is a starting point, not a finished business platform.
My next priorities are to test order extraction against a representative set of Marathi, Hindi, English, and mixed-language messages; measure how often the model correctly identifies products and quantities; and gather feedback from actual sellers.
I'd also like to explore voice-based ordering and better inventory suggestions.
The most important metric won't be how impressive the AI sounds. It will be whether a seller can complete everyday work with fewer mistakes and less effort.
Prize Categories
- Best Use of Gemma — for the Gemma-based natural-language order parsing integration.
Thanks to the Hacktoberfest team and the open-source AI community for encouraging developers to build tools around real problems, not just demos.
If you run a small business or know someone who sells vegetables, I'd love to hear from you:
What's the most repetitive task in your business that you'd want AI to take off your hands?
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