Google Vertex AI Hackathon

Welcome to Opening Stream for Google Cloud Vertex AI Hackathon! Join us for opening Talks and Hackathon Guide.

Hackathon Kick-Off

Ksenia Nekrasova and Katharina Beck from Google Cloud will open the Hackathon with inspirational words and onboarding into the Google Cloud products

Google Cloud's Opening

We will guide you through the website, and Discord community - to make sure you will succeed at the Hackathon 

Hackathon Guide

Let's go through the Technology for the Hackathon and how you can get access to it.

Technology and Challenge

Alex Erfurt, the representer of Google Cloud will join the stage to guide you through the AI models and the use cases for your Hackathon projects 

Guide into Google Cloud

After the Kick-Off stream, focus on finding your team for the Hackathon and start brainstorming your idea!

Team Formation time!

5-min tutorial for non-tech participants on the creation of GitHub account

GitHub for non-tech participants

Start of the Team Formation

Join Q&A space on lablab.ai Discord server for open session to discuss any questions you may have

Open Q&A with mentors

Submission Deadline

A team of two passionate developers, who wants to explore new technology

TechnoCouple

pritisharma127

shivaprasad

Today, we are presenting an idea which can help in routing the Chat/Voice interaction in the contact center to the right skilled agent.
As we all know Contact center plays a bigger role in any organization when providing the best customer service to the users.
Ideally, in any digital interaction in a contact center ecosystem, like voice, chat or email involves a virtual agent which is the
first layer to interact with the customer. VA tries to welcome the user and understand the Intent. Based on the intent,
query the VA tried to have the conversation. If the Intent is complex we might need human assistant to look upon,
we have a routing strategies traditionally build. Based on the parameters collected by virtual agent, routing strategy executes 
and determines a skilled agent but might not be appropriate everytime.
So today, we thought this can be resolved by AI or provide AI prediction as an additional parameter in determining the right Queue and right agent. In this demo, We have defined 5 Queue and given a Name as shown here. 
Q1_GB_CHAT: Agents belonging to this queue, can help with general banking queries like account information, online bank, branch address, contact number only. And this agent can speak english, spanish.
Q1_GB_CHAT_ESP: Agents belonging to this queue, can help with general banking queries like account information, And this agent can speak only spanish.
Q2_HL_CHAT_ENG: Agents belonging to this queue can help with mortgage loans, home loans, home loan eligibility. The agent belonging to this queue can only speak english.
Q3_CC_CHAT: Agent belonging to this queue can help anything related to credit card.
Q4_FR_CHAT: Agent belonging to this queue helps with fraudulent activity on credit card.
So we have given simple description or defining this Queue.
We have created a Prompt template, with this description & chat summary injected dynamically into Prompt template. This prompt is sent to Text-bison model, which responds with the Queue ID.

We have developed a demo chatbot called RichieBot assisting Richie Bank customers.We call this as Smart Routing, which summarises the interaction & determines the best Queue for handling complex queries from user using Text-Bison, Chat-Bison-001 AI models

"TechnoCouple team on Google Vertex AI Hackathon Hackathon"

Team Idea

Submission