## Inspiration

You don't have Internet connection, but you want to use Bing anyway cuz you're a hopeless Internet addict.
Or you want to find cheap hotel deals. HotlineBing can help.

## What it does

Allows you to use Bing via text messaging, search for hotel deals using natural language (that's right no prompts) using HP-HavenOnDemand Extract Entity API

## How I built it

Nodejs, Express, HP-HavenOnDemand, BrainTree API, Bing API, PriceLine API

## Challenges I ran into

Twilio Voice API requires an upgraded account so this is not really a hotline (\*sighs Drake), general asynchronous JS flow conflicts with our expectation of how our app should flow, integrating Braintree API is also hard given the nature of our hack

## Accomplishments that I'm proud of

Get some functionality working

## What I learned

JS is a pain to work with

## What's next for HotlineBing

More polished version: improve natural language processing, allow for more dynamic workflow, send Drake gifs via MMS??
## Inspiration

We recognize that small businesses lose revenue simply because they aren't able to answer all incoming calls. Yet, for many, hiring a receptionist or outsourcing an answering service is too expensive. We believe there is a better way.

## What it does

R2D-Talk automates your small business's reception, allowing you to answer incoming calls you would have missed. Trained on your business data — including company PDFs and Word Docs (e.g. Restaurant menus), Databases and Google Apps (e.g. Restaurant reservations system), and even business website content — we provide a conversational phone agent that does all the work of a receptionist.

## How we built it

1. Data → Vector DB: Upload business data and we’ll embed it.
2. LangChain: We use retrieval-augmented generation with LLMs to augment responses with live business database data and unstructured documents/PDFs
3. Text ↔ Speech: With Twilio, we convert text to speech and vice-versa to create a conversational phone agent that can deploy the LangChain LLM.

Our data is collected with BeautifulSoup and PostgreSQL with PyPDF and PythonDocx. Our backend is based in a microservice architecture with Flask. Our Vector store utilizes LangChain and FAISS. Our text-to-speech from Vosk integrates into Twilio and connects to Google Calendar API.

## Accomplishments that we're proud of

* We have a fully working live demo and commercializable use case with integrations
* We were able to create a high-impact product that could be relevant for multiple industries
* Lasya is a first-time hacker and learned a lot
## 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.