Small online sellers often handle everything themselves: managing orders, talking to customers, arranging deliveries, and resolving complaints. When something goes wrong with an order, organizing the available information and writing a professional response can become difficult.
That's why I built DisputeDeck Lite, a simple AI-powered assistant that helps online sellers prepare for customer disputes.
🚀 Live Demo: https://disputedeck-lite.streamlit.app/
💻 GitHub Repository: https://github.com/jahnavist04/DisputeDeck-Lite
💡 The Problem
Imagine running a small Instagram business. A customer says their order never arrived, but the order status says delivered. What should you check? What information is still missing? How can you respond professionally without making assumptions?
DisputeDeck Lite is designed to help organize these situations instead of leaving sellers to figure everything out from scratch.
✨ What Does DisputeDeck Lite Do?
The application provides four main capabilities:
Case analysis: Separates the information provided from uncertain points.
Missing-evidence checklist: Suggests potentially useful evidence to collect.
Professional reply generator: Creates an editable, polite response for the customer.
Case summary export: Allows the seller to download the case details and analysis as JSON.
The workflow is intentionally simple: enter the order details, describe the complaint, select the evidence already available, and generate an analysis.
🤖 How Open-Weight AI Fits In
DisputeDeck Lite uses a hosted open-weight instruction model through Hugging Face Inference Providers.
Instead of training a model from scratch, I integrated an existing model into a focused application. The model helps structure the supplied information, identify unanswered questions, and draft a response.
For me, this demonstrates how open-weight AI can be used to build practical tools without needing to develop a foundation model independently.
🛠️ Tech Stack
Python for application logic
Streamlit for the web interface
Hugging Face Inference Providers for hosted model inference
GitHub for version control and open-source collaboration
Streamlit Community Cloud for deployment
🧪 A Simple Example
Suppose a customer reports that a notebook they ordered has not arrived, even though the order status is marked as delivered.
DisputeDeck Lite can help the seller identify what is known, recognize that the delivery status alone may not resolve the complaint, consider checking tracking or delivery confirmation, and draft a calm response asking for clarification.
The goal is not to automatically decide who is right. It is to help the seller approach the situation more systematically.
🌍 Why Open Innovation Matters
Open-weight models and open-source tools make it possible for developers to experiment, customize existing technology, and build solutions for specific problems.
A small business does not necessarily need a complicated enterprise platform to benefit from AI. Focused tools can make useful workflows more accessible.
DisputeDeck Lite is a small example of applying open-weight AI to a practical problem faced by independent online sellers.
🔐 Responsible Use
AI-generated results can be incomplete or incorrect. DisputeDeck Lite does not establish whether a claim is true, provide legal advice, or guarantee dispute outcomes.
Sellers should verify important details, collect actual evidence, and review generated replies before sending them. Because the application uses hosted inference, submitted case text is sent to the configured provider. Avoid entering sensitive customer or payment information.
🚧 What's Next?
There is room to improve the project further, including better error handling, automated tests, multilingual support, and additional ways to export case summaries.
I would love to keep improving the application and learn from feedback from other developers.
🙌 Try It Out
Give the demo a try using fictional order information, explore the workflow, and check out the source code.
Live Demo: https://disputedeck-lite.streamlit.app/
Source Code: https://github.com/jahnavist04/DisputeDeck-Lite
Feedback, suggestions, and contributions are welcome!
Built with Python, Streamlit, and open-weight AI. 🧾🤖
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