Document Information Search using Azure OpenAI
MLAI Digital
Document Information Search using Azure OpenAI
MLAI Digital
Document Information Search using Azure OpenAI
MLAI Digital
Gen AI based Document Information Search using Open AI
Introduction
MLAI Digital built a ChatGPT based chatbot that reads the documents provided by you and answer users questions based on the documents. Companies in today’s world are always finding new ways of enhancing clients’ service and engagement. Making a chatbot that can rapidly and accurately respond to client inquiries is one method to do this.
- Azure Bot Framework used to build chatbot interface.
- Luis cognitive services used to defined the intent and it will help to define the score and identified keywords.
- QnAMaker cognitive service used to define the FAQ based questions and answers
- Azure App API used to build search api and return data based on defined keywords.
- SharePoint Online is used as content repository.
- Adaptive Card used to design the card based on API results.
Use cases and scenarios:
- Increase website engagement with personalization: There’s no better time to start a conversation than when a buyer or a customer is exploring a large document. Chatbots engage with visitors at the moment of highest intent through personalized conversations.
- Leverage visitor intelligence for better conversations: With visitor intelligence, chatbots can identify site visitors and deliver an experience tailored to them. They also take in additional information from the visitor and build them into future conversations.
- Give high-value accounts the red carpet experience: You’ll never miss an opportunity to engage a target account again — not with a chatbot that personally greets the buyer and gives them a fast track to sales. Plus, with a chatbot in place, you won’t have to worry about losing valuable opportunities when your sales team is offline.
- Prioritize customer prospecting with real-time notifications: You only want to chat with those accounts that are ready to buy. Chatbots streamline your prospecting by notifying sales reps when a high-intent, priority account is on your website so they can jump straight into chat.
- Generate more qualified leads: It would be nice if we could talk to every lead and ensure they’re a good fit before scheduling a meeting. But that’s impossible to do at scale. Chatbots can do most of the heavy lifting by qualifying your leads in real time and improving sales acceleration.
- Combat customer churn: Chatbots are the perfect solution to high-volume support inquiries. Instead of forcing customers to navigate cluttered knowledge bases, you can use a customer service chatbot to deliver support information instantly, 24/7.
- Chat live when needed: Sometimes customers just need human support. Customer service chatbots will automatically route in your team for high-level issues without distracting them with easily-answered questions.
- Increase lead to pipeline conversion rates by up to 100%: Can grow annual recurring revenue (ARR) in target accounts by up to 17.5% and experience up to a 670% ROI. That’s a lot of impact on pipeline.
Key takeaways for Document search Chatbot
When talking about designing user experience with ChatGPT, this includes not only interface design but prompt design and engineering — it’s a science of its own.
- To ensure a positive user experience, we have considered the chatbot’s tone and personality, the user interface, and the chatbot’s ability to understand and respond to user queries.
- Document search chatbot offers tremendous opportunities for businesses to automate customer service, personalize customer interactions, and improve communication with customers and partners around the world.
- By leveraging the power of Chatbot and prioritizing user experience, it improves customer satisfaction, increased engagement, and drive business growth.
*Additional channels and modules are licenced separately. Setup and implementation costs depends on the scope, requirements and the complexity. Integration cost with any external system that isn’t out-of-the-box for MLAI is defined based on the estimated effort. 3rd party services such as Google Cloud, WhatsApp, OpenAI, etc. are paid additionally, and depend on the traffic (volume of usage).