Interactive Query System Development
Budget: £250 – £750 GBP
I am seeking developers to build an MVP for an interactive query system that allows suppliers to upload product documentation and enable customers to ask questions about these products, primarily using voice interaction. The goal is to create a platform where suppliers can register products and provide related documentation, while customers can ask questions about these products and receive accurate responses via a chatbot or voice assistant powered by an LLM AI.
The system will consist of two key components:
Supplier-side platform (Web):
Allows suppliers to register new products.
Suppliers will upload product documentation (e.g., Word, PDF, URL) and add descriptions and additional notes.
The system will use a Large Language Model (LLM) AI to process and understand the product documentation for answering customer queries.
Each product's documentation will be handled independently to avoid cross-product interference.
Customer-side platform (Mobile: iOS & Android):
Allows customers to ask questions about specific products.
Interaction options will include both chat and voice, with a focus on voice queries.
Customers will receive voice responses from the AI based on the product's documentation.
Requirements
Supplier Side (Web Interface)
User Authentication:
User and password login system for suppliers.
Option to register new users with email and password.
Product Registration:
Form for suppliers to register each product, including fields for product name, description, and other relevant metadata.
Ability to upload documentation in formats such as Word, PDF, and via URL links.
Documentation Analysis:
The LLM AI should be able to analyze the uploaded documentation and extract key information to answer future customer queries.
Each product's data and AI processing should be isolated from other products.
Dashboard for Suppliers:
A dashboard showing the list of registered products.
Option to update or delete product information and re-upload documentation.
Customer Side (iOS/Android App)
User Interaction:
Simple, intuitive interface for customers to ask questions.
Options for both text input and voice input.
Voice Interaction:
Customers can ask voice-based queries about products.
The system will return responses in voice format, leveraging text-to-speech technology.
Chat Option:
In addition to voice, customers should be able to ask questions via chat.
Chat responses will also come from the LLM AI, based on the registered product’s documentation.
Product Selection:
Customers can select the product they are inquiring about.
The AI will only consider the documentation relevant to the selected product when answering queries.
AI Personas:
The AI should have two persona options to be selected. The first to act as a tech consultant to provide answear about techinical documentations (datasheet, user guide, etc) and the other focused on kids for queries about toys, games, books, etc.
Deliverables
Web platform for suppliers to register products and upload documentation.
Mobile app for customers to ask questions via chat or voice, with responses powered by AI.
LLM integration for parsing and answering queries based on documentation.
Deployment of both platforms for initial testing and feedback.
Future Enhancements (Beyond MVP) - for information only
Contextual Understanding: The AI should understand related terms even if they aren’t explicitly mentioned in the documentation. For example, if a customer asks about "encryption" but the document only refers to "TLS/SSL", the AI should infer that encryption is supported.
Advanced NLP features to interpret more complex or nuanced queries.
Analytics for suppliers: Insights into the types of questions asked about their products.
The system will consist of two key components:
Supplier-side platform (Web):
Allows suppliers to register new products.
Suppliers will upload product documentation (e.g., Word, PDF, URL) and add descriptions and additional notes.
The system will use a Large Language Model (LLM) AI to process and understand the product documentation for answering customer queries.
Each product's documentation will be handled independently to avoid cross-product interference.
Customer-side platform (Mobile: iOS & Android):
Allows customers to ask questions about specific products.
Interaction options will include both chat and voice, with a focus on voice queries.
Customers will receive voice responses from the AI based on the product's documentation.
Requirements
Supplier Side (Web Interface)
User Authentication:
User and password login system for suppliers.
Option to register new users with email and password.
Product Registration:
Form for suppliers to register each product, including fields for product name, description, and other relevant metadata.
Ability to upload documentation in formats such as Word, PDF, and via URL links.
Documentation Analysis:
The LLM AI should be able to analyze the uploaded documentation and extract key information to answer future customer queries.
Each product's data and AI processing should be isolated from other products.
Dashboard for Suppliers:
A dashboard showing the list of registered products.
Option to update or delete product information and re-upload documentation.
Customer Side (iOS/Android App)
User Interaction:
Simple, intuitive interface for customers to ask questions.
Options for both text input and voice input.
Voice Interaction:
Customers can ask voice-based queries about products.
The system will return responses in voice format, leveraging text-to-speech technology.
Chat Option:
In addition to voice, customers should be able to ask questions via chat.
Chat responses will also come from the LLM AI, based on the registered product’s documentation.
Product Selection:
Customers can select the product they are inquiring about.
The AI will only consider the documentation relevant to the selected product when answering queries.
AI Personas:
The AI should have two persona options to be selected. The first to act as a tech consultant to provide answear about techinical documentations (datasheet, user guide, etc) and the other focused on kids for queries about toys, games, books, etc.
Deliverables
Web platform for suppliers to register products and upload documentation.
Mobile app for customers to ask questions via chat or voice, with responses powered by AI.
LLM integration for parsing and answering queries based on documentation.
Deployment of both platforms for initial testing and feedback.
Future Enhancements (Beyond MVP) - for information only
Contextual Understanding: The AI should understand related terms even if they aren’t explicitly mentioned in the documentation. For example, if a customer asks about "encryption" but the document only refers to "TLS/SSL", the AI should infer that encryption is supported.
Advanced NLP features to interpret more complex or nuanced queries.
Analytics for suppliers: Insights into the types of questions asked about their products.