WellNest AI

Inspiration

We have heard a lot about using AI to further drive customer satisfaction, but what about employees? This thought sparked the idea for our project "WellNest AI".

What it does

Our primary purpose is to boost productivity by increasing employee happiness. We have a chatbot that works as an AI-powered Wellness Officer. Employees can anonymously raise concerns or share their personal thoughts in a virtual safespace with the chatbot which will try to encourage them and help them feel better. Along with the chatbot, the employees have an option to share proper feedback with the organisation. This feedback is then run through a sentiment analysis program that generates a report defining areas of concerns and areas where the organisation is doing relatively well.

How we built it

We have used a RoBERTa model fine-tuned for sentiment analysis and the Pinecone client to create an index to store vector representations of our passages which we can retrieve using another vector (the query vector). Then, we generate embeddings for all the text (feedback) in the dataset. Alongside the embeddings, we also include the sentiment label and score in the Pinecone index as metadata. We use this data to understand employee feedback. For the WellNest Chatbot, we have used the ChatGPT and Pinecone API along with LangChain, Streamlit, Cohere, Torch and Tiktoken Software Development Kits.

Challenges we ran into

It was challenging to bring together the different features as a coherent and streamlined product.

Built With

• chatgpt
• cohere
• langchain
• pinecone
• python
• roberta
• streamlit
• tiktoken
• torch