## Inspiration

One of our teammates had an internship at a company where they were exposed to the everyday operations of the sales and customer service (SCS) industry. During their time there, they discovered how costly and time consuming it was for companies to properly train, manage and analyze the performance of their SCS department. Even current technologies within the industry such as CRM (Customer Relation Management) softwares were not sufficient to support the demands of training and managing.

Our solution encompasses a gamified platform that incorporates an AI tool that’s able to train, analyze and manage the performance of SCS employees. The outcome of our solution provides customers with the best support tailored towards them at a low-cost for the business.

## What it does

traint is used in 4 ways:

Utilize AI to facilitate customer support agents in honing their sales and conflict resolution skills
Provides feedback and customer sentiment analysis after every customer interaction simulation
Ranks customer support agents on a leaderboard based on “sentiment score”
Matches top performers within the leaderboard in their respective functions (sales and conflict) to difficult clients.

traint provides businesses with the capability to jumpstart and enhance their customer service and sales at a low cost. traint also interfaces AI agents and analysis in a digestible manner for individuals of all technological fluency.

## How we built it

traint was built using the following technologies:
Next.js
React, Tailwind and shadcn/ui
Voiceflow API
Groq API
Genesys API

The **Voiceflow** API was used mainly to develop the two customer archetypes. One of which being a customer looking to purchase something (sales) and the other being a customer that is unsatisfied with something (conflict resolution).

**Genesys** API was utilized to perform the customer sentiment analysis - a key metric in our application. The API was also used for its live chat queue system, allowing us to requeue and accept “calls” from customers.
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The **Groq** API allowed us to analyze the conversation transcript to provide detailed feedback for the operator.

Most of our features were interfaced through our web application which hosts action buttons and chat/performance analytics like the average customer sentiment score.

## Challenges we ran into

There was a steep learning curve to Genesys’s extensive API. At first we were overwhelmed with the amount of API endpoints available, and where to begin. We went to the many workshops, including the Genesys workshop, which helped us get started, and we consulted with the teams when we ran into issues with the platform.

Initially, we would run into issues with setting up and getting access to certain endpoints since they required us to be granted permissions explicitly from the Genesys team, but they were very prompt and friendly with getting us the access we need to build our app.

We also ran into many issues with the prompt engineering for the AI agent on Voiceflow. When building the customer archetypes, the model wasn’t performing as expected so it took a lot of time to get it to work like we wanted.

Although we had many challenges, we were all proud of the end product and the work we did despite the many roadblocks we faced :)

## Accomplishments that we're proud of

In a short span of time, we were able to familiarize ourselves with several tools, namely the Genesys’ extensive API and the Voiceflow platform. We integrated these two tools together to create a product that serves both the customer as well as the customer service representatives.

## What we learned

We learnt a lot about API integration, namely the Genesys API and Voiceflow software. Both the API’s were completely new to us and it took alot of research and trial and error to get our product to work.

We also learned about the link between frontend and backend in Next.js, and how to transfer information between the both of them via API routes, client and server side components, etc.

Our team came into this hackathon not really knowing much about each other. Coming from 4 different universities, we learnt a lot working with each other.

## What's next for traint

We wish that we had more time to fully develop our leaderboard process through the gamification/employee performance API. We ran into some issues with accessing the API and given more time, we would have implemented this feature.

Another feature we wanted to add was the ability to assign agents based off of client history. Although there may be some data concerns - providing clients with the ability to talk to the same agents that they preferred in the past would be very beneficial.

Finally, we would love to take traint to the next level and see it incorporated in some real life businesses. We truly believe in the use case and hope it solves some key pain points for people.
## Inspiration

Data analytics can be **extremely** time-consuming. We strove to create a tool utilizing modern AI technology to generate analysis such as trend recognition on user-uploaded datasets.The inspiration behind our product stemmed from the growing complexity and volume of data in today's digital age. As businesses and organizations grapple with increasingly massive datasets, the need for efficient, accurate, and rapid data analysis became evident. We even saw this within one of our sponsor's work, CapitalOne, in which they have volumes of financial transaction data, which is very difficult to manually, or even programmatically parse.

We recognized the frustration many professionals faced when dealing with cumbersome manual data analysis processes. By combining **advanced machine learning algorithms** with **user-friendly design**, we aimed to empower users from various domains to effortlessly extract valuable insights from their data.

## What it does

On our website, a user can upload their data, generally in the form of a .csv file, which will then be sent to our backend processes. These backend processes utilize Docker and MLBot to train a LLM which performs the proper data analyses.

## How we built it

Front-end was very simple. We created the platform using Next.js and React.js and hosted on Vercel.

The back-end was created using Python, in which we employed use of technologies such as Docker and MLBot to perform data analyses as well as return charts, which were then processed on the front-end using ApexCharts.js.

## Challenges we ran into

* It was some of our first times working in live time with multiple people on the same project. This advanced our understand of how Git's features worked.
* There was difficulty getting the Docker server to be publicly available to our front-end, since we had our server locally hosted on the back-end.
* Even once it was publicly available, it was difficult to figure out how to actually connect it to the front-end.

## Accomplishments that we're proud of

* We were able to create a full-fledged, functional product within the allotted time we were given.
* We utilized our knowledge of how APIs worked to incorporate multiple of them into our project.
* We worked positively as a team even though we had not met each other before.

## What we learned

* Learning how to incorporate multiple APIs into one product with Next.
* Learned a new tech-stack
* Learned how to work simultaneously on the same product with multiple people.

## What's next for DataDaddy

### Short Term

* Add a more diverse applicability to different types of datasets and statistical analyses.
* Add more compatibility with SQL/NoSQL commands from Natural Language.
* Attend more hackathons :)
### Long Term
* Minimize the amount of work workers need to do for their data analyses, almost creating a pipeline from data to results.
* Have the product be able to interpret what type of data it has (e.g. financial, physical, etc.) to perform the most appropriate analyses.
## Inspiration

To any financial institution, the most valuable asset to increase revenue, remain competitive and drive innovation, is aggregated **market** and **client** **data**. However, a lot of data and information is left behind due to lack of *structure*.

So we asked ourselves, *what is a source of unstructured data in the financial industry that would provide novel client insight and color to market research*?. We chose to focus on phone call audio between a salesperson and client on an investment banking level. This source of unstructured data is more often then not, completely gone after a call is ended, leaving valuable information completely underutilized.

## What it does

**Structerall** is a web application that translates phone call recordings to structured data for client querying, portfolio switching/management and novel client insight. **Structerall** displays text dialogue transcription from a phone call and sentiment analysis specific to each trade idea proposed in the call.

Instead of loosing valuable client information, **Structerall** will aggregate this data, allowing the institution to leverage this underutilized data.

## How we built it

We worked with RevSpeech to transcribe call audio to text dialogue. From here, we connected to Microsoft Azure to conduct sentiment analysis on the trade ideas discussed, and displayed this analysis on our web app, deployed on Azure.

## Challenges we ran into

We had some trouble deploying our application on Azure. This was definitely a slow point for getting a minimum viable product on the table. Another challenge we faced was learning the domain to fit our product to, and what format/structure of data may be useful to our proposed end users.

## Accomplishments that we're proud of

We created a proof of concept solution to an issue that occurs across a multitude of domains; structuring call audio for data aggregation.

## What we learned

We learnt a lot about deploying web apps, server configurations, natural language processing and how to effectively delegate tasks among a team with diverse skill sets.

## What's next for Structurall

We also developed some machine learning algorithms/predictive analytics to model credit ratings of financial instruments. We built out a neural network to predict credit ratings of financial instruments and clustering techniques to map credit ratings independent of s\_and\_p and moodys. We unfortunately were not able to showcase this model but look forward to investigating this idea in the future.