See

Why

CredCount

CredCount

You can get feedback like this for free in our app

You can get feedback like this for free in our app

See Why

YC Application Feedback

YC Application Feedback

See Why is an AI tool designed to help you understand why your YC application may have been rejected and what you can do to improve it. By analyzing YC content and top applications, See Why provides actionable feedback, helping you refine your application for better chances of success.

How See Why Can Help You

  • Identify Weaknesses: Understand the specific areas where your application falls short.

  • Actionable Insights: Receive clear, practical advice on how to improve your application.

  • Continuous Improvement: Discover opportunities for ongoing enhancement to stay competitive.

Unsuccessful

YC Application Feedback

YC Application Feedback

If you have a demo, what's the url? Demo can be anything that shows us how the product works. Usually that's a video or screen recording.

http://credcount.com/whitepaper

Describe what your company does in 50 characters or less.

AI based fact checking through social media data

What is your company going to make? Please describe your product and what it does or will do.

An analytics platform, backed by data science and machine learning, used to infer the credibility of events in real-time.

Where do you live now, and where would the company be based after YC?

Atlanta, GA, United States / New York, New York, United States

Founders

Please enter the url of a 1 minute unlisted (not private) YouTube video introducing the founder(s). This video is an important part of the application. (Follow the Video Guidelines.)

Please tell us about an interesting project, preferably outside of class or work, that two or more of you created together. Include urls if possible.

Tanu and Eric have known each other via the PhD program at Georgia Tech. Eric advised Tanu throughout her PhD, and they worked on a number of projects together. A paper from 2014, “Phrases that Predict Success on Kickstarter” (http://comp.social.gatech.edu/papers/cscw14.crowdfunding.mitra.pdf) resulted in many conversations outside of academia, with entrepreneurs wanting this as a product. Christian and Eric worked closely together throughout the summer of 2016 on a project called HackGT@UPC as one of the first international, collaborative hackathons. With Eric’s faculty oversight, Christian built this event over the course of a few months around the subject of “assistive technologies” (https://hack-gtupc.devpost.com/).

How long have the founders known one another and how did you meet? Have any of the founders not met in person?

Tanu and Christian met through Professor Eric Gilbert at Georgia Tech about 2 months ago. Eric is currently Tanu's PhD advisor and Christian worked with Eric as his teaching assistant during the GT study abroad program in Barcelona, Spain last summer. When Tanu came to Eric about turning her PhD research project into a full-blown company, Eric immediately thought of Christian. Tanu and Christian met a couple days later for coffee/discussion and jumped into customer discovery and a bit of market research, eventually leading them here to this application!

Category

Which category best applies to your company?

Artificial Intelligence

Is this application in response to a YC RFS?

Yes

If yes, which one?

News

Progress

How far along are you?

Starting in 2014, Tanu has been working on this model, the science and the technologies surrounding it for about 3 years now. Our first major paper (http://credcount.com/whitepaper) was reviewed and published in late 2014 in a top CS conference. Although the majority of the work such as data collection was finished in approximately 8 or so months, there are certain analyses that have taken place up until today. That said, we are in what we consider the prototyping stage: the service/technology in its current form is not suited to any specific market but rather a proof of concept. With a little bit of modification to the machine learning models, we feel as though we can target new markets in as little as a couple weeks. Thus, Christian has spent the last few months trying to identify where we fit and have the biggest effect as a company/product. So far, we’ve considered news, politics, finance and the public sector. With contacts such as news anchor George Howell from CNN, political advisor and campaign manager Jason Boles and Senior Vice President of investments Mr. Jeff Wolk from Raymond James in the financial sector, we feel as though our best fit at this moment is to ride the “fake news” wave. Currently there is no window dressing to our model. In its current form, it is command-line driven (literally “python model.py [input]”), but we feel as though who our potential customers might be should be identified before we begin to work on any sort of graphical interface and/or API. That’s why we’ve come to YC. Our idea currently is a product/application built around our technology as an easy to use display of current news items and their credibility rankings such that someone might be able to read through an article with a presumed level of bias or accuracy up front. It comes through the narrative of “news literacy” and an attempt to have all sides of every story. Think if every fact or bullet from a news article could have a certain score for credibility such that the least items with the least standing fall to the way side and only the true and solid facts of the situation are left. How long have each of you been working on this? Have you been part-time or full-time? Please explain. Tanu has been working on CredCount since early 2014 as part of her PhD in social computing with her advisor Eric Gilbert at Georgia Tech. The majority of data collection and analysis led into early 2015, and she’s continued to circle back to it in a “part-time” role as a project with the most potential as a product/service outside of academia. Christian joined on early this year, 2017, in a product manager capacity to build this paper/model into a business. Working part-time while taking classes since his joining, he’s found himself devoting more time towards this venture than schoolwork, such that Tanu and Christian both are ready to commit to the project for the summer and however long after that.

Which of the following best describes your progress?

Prototype

How many active users or customers do you have? If you have some particularly valuable customers, who are they? If you're building hardware, how many units have you shipped?

None.

When will you have a prototype or beta?

N/A

Do you have revenue?

No

We're interested in your revenue over the last several months. (Not cumulative and not GMV).

N/A

Anything else you would like us to know regarding your revenue or growth rate?

Nope.

If you are applying with the same idea as a previous batch, did anything change? If you applied with a different idea, why did you pivot and what did you learn from the last idea?

N/A

If you have already participated or committed to participate in an incubator, "accelerator" or "pre-accelerator" program, please tell us about it.

N/A

Idea

Why did you pick this idea to work on? Do you have domain expertise in this area? How do you know people need what you're making?

Tanu was attracted to the project by the prevalence of fake news spreading online following natural disasters such as Superstorm Sandy in 2012. As she saw people sharing a lot of incorrect or misleading information about the events, Tanu decided to track both big stories and smaller rumors with the goal of creating an app that could help ordinary people sort fact from fiction to make decisions that could be crucial. Tanu has an undergraduate degree in computer engineering and is currently finishing her PhD in computer science with a specialization in social computing. Christian is working on his undergraduate degree, but is focusing into the domain of people and society. He’s currently finishing up a Georgia Tech sponsored paper on the website http://voat.co with qualitative research through interviews with users. Based on Christian’s market research, the financial, political and news markets seem to be the most motivated to move forward with our work. In my discussions with Jason Boles, he would like to use our model as a sort of real-time polling for statements made from congressional candidate Kurt Wilson. While discussing the project with Jeff Wolk from Raymond James, Wolk proposed to use the service for adding credibility to events through social media for commodity trading decisions. Also, we have discussed the possibility of parsing analyst papers and summarizing the ideas and decisions they propose with scores as well. Lastly, in talks with George Howell, CredCount would be a great addition to the new media tool-set used in collaboration with pre-existing service like https://www.dataminer.co/. With this, instead of focusing on volume and noise alone, news outlets could intelligently use the world through social media as credibility to a story or event.

What's new about what you're making? What substitutes do people resort to because it doesn't exist yet (or they don't know about it)?

Basic event analysis has existed in its current form, based on volume and noise alone. The ability to take collective reactions through social media to help determine reliability of a specific event is what we bring to the table. Think of it as a haystack of social media posts and events that finding a needle in is close to impossible. We help shrink this haystack to make fact checking far easier.

Who are your competitors, and who might become competitors? Who do you fear most?

Based on data science and specific to the social computing field, we fear FactMata (https://medium.com/factmata/introducing-factmata-artificial-intelligence-for-political-fact-checking-db8acdbf4cf1) the most. As far as we know, to this day, they have no model or product and haven’t released anything close to the technology described within our whitepaper. That said, other fact checking websites will be our main competitors such as Snopes (http://www.snopes.com/) and PolitiFact. Otherwise, as Google and Facebook begin to roll out their own solutions these will be internal solutions to problems faced by the all media services on the internet. We do not fear these big guys as much because we stand to help all the rest using a paid for plug-in service if we decide to go that route.

What do you understand about your business that other companies in it just don't get?

The data science and linguistics portion of these potential markets. What sort of language implies the most credible financial analysis of one specific commodity? Or, how well a certain political statement goes over with a constituency? Or, what sort of language might you look out for when flagging something as potentially fake? Currently with some sort of human touch these sorts of questions can be answered but it might take a bit of time while doing so. We’re attempting to help narrow into the credible pieces.

How do or will you make money? How much could you make?

A specific case we’ve looked into is a contract or subscription service with big media companies. Similarly, DataMiner has made millions helping the news guys observe events as they happen going from labels of “this might be newsworthy” to “lots of people are talking about this; you should check this out”. We hope that our service will be an even bigger asset to news media companies bringing more than volume statistics through analytics. With early research, these contracts have figures in the millions.

How will you get users? If your idea is the type that faces a chicken-and-egg problem in the sense that it won't be attractive to users till it has a lot of users (e.g. a marketplace, a dating site, an ad network), how will you overcome that?

For a product of our own we’d need to attract potential users by having a narrative for the social benefit of “news literacy”. Minds are changed through emotion rather than factual concrete evidence so this application would be more of a training to see both sides idea instead of showing someone raw evidence against their position. In this manner we’d be very chicken-and-egg. As a service, we’ll get users by sales and marketing showing how much value we could add to markets like high frequency trading or politics. We’d have to lineup demos and grow through grassroots from the beginning.

Equity

Have you incorporated, or formed any legal entity (like an LLC) yet?

No

What kind of entity and in what state or country was the entity formed?

N/A

Please describe the breakdown of the equity ownership in percentages among the founders, employees and any other stockholders. If there are multiple founders, be sure to give the equity ownership of each founder.

N/A

List any investments your company has received. Include the name of the investor, the amount invested, the premoney valuation / valuation cap, and the type of security sold (convertible notes, safes or stock).

N/A

How much money do you spend per month?

N/A

How much money does your company have in the bank now?

N/A

How long is your runway?

infinite, (no money in or out yet) but we think we can get to market in around 3 months

Please provide any other relevant information about the structure or formation of the company.

Christian Battaglia will be in charge of most business-oriented tasks and product management of the service/platform. Tanushree will be focusing on data science, machine learning and the specifics of the model tailored to whatever market we decide on. Eric will be in an scientific advising role, based on his expertise through many years of social computing.

Legal

Are any of the founders covered by noncompetes or intellectual property agreements that overlap with your project? If so, please explain.

No.

Who writes code, or does other technical work on your product? Was any of it done by a non-founder? Please explain.

Tanu wrote the code, setup the Turkers and has done all technical work on the project so far. Christian is finishing his undergrad at Georgia Tech and has about 3 total years of work experience in internship, co-operative and other part-time capacities. He’s comfortable reviewing all code for the project in its current form as it relates to AI and machine learning. Christian was also previously head of web development at the European startup Glovo (http://glovoapp.com). That said, all technology was developed by founding members.

Is there anything else we should know about your company?

Funded originally by a DARPA grant and under the umbrella of Georgia Tech, you might think that we would need to license this idea or give some credit to the university, but that is not the case. The primary paper has been out for over a year, meaning it cannot be patented. The nature of the technology--highly specific to social computing, machine learning and linguistics--form a steep barrier.

Others

If you had any other ideas you considered applying with, please list them. One may be something we've been waiting for. Often when we fund people it's to do something they list here and not in the main application.

Christian’s ideas include:

- A social experiment much like Facebook where every login or session has you assume the role and identity of a fictitious person. All data created by you while in the role of this person persists upon session expiration or sign out. Each time you log back in you must put yourself in the shoes of the previously created identity and post as such breaking the echo chamber that is social media today while also having fun.

- Event-going today has turned into a very spontaneous unscheduled sort of thing. Often times I find myself wishing to get in touch with the people I met within the context of some random evening for pictures, videos and just networking in general. Think of this as a digital collection of your concert/sports tickets where each one expands into your experience prior and after the specific event!

- Having spent 3 summers abroad in Barcelona working closely with nightlife promoters, one thing they always talk about for general improvement of their day to day is a way to more easily get in touch with their network to predict figures for guest lists and to not spam as much. Think of this as a 1 degree removed connection of the promoter and I want to hand over my network list of those specific few that are headed to Barcelona for vacation. Hopefully this would be that he’d have my network only for the time that my friends are travelling to cut through on the spam.

Please tell us something surprising or amusing that one of you has discovered.

Christian: I over analyze social settings a bit too much and over the years I’ve noticed just how much weight we give first impressions… With that, I always try and give strangers and new contacts the time of day/benefit of the doubt while attempting to be my best self in return.

Curious

What convinced you to apply to Y Combinator? Did someone encourage you to apply?

When the triple request for startups based around news, jobs and democracy dropped, Tanushree and Christian immediately decided that it was time to apply. After years mulling around with the idea that this model could potentially be a business, we decided to jump all in.

How did you hear about Y Combinator?

Online and through friends


If you have a demo, what's the url? Demo can be anything that shows us how the product works. Usually that's a video or screen recording.

http://credcount.com/whitepaper

Describe what your company does in 50 characters or less.

AI based fact checking through social media data

What is your company going to make? Please describe your product and what it does or will do.

An analytics platform, backed by data science and machine learning, used to infer the credibility of events in real-time.

Where do you live now, and where would the company be based after YC?

Atlanta, GA, United States / New York, New York, United States

Founders

Please enter the url of a 1 minute unlisted (not private) YouTube video introducing the founder(s). This video is an important part of the application. (Follow the Video Guidelines.)

Please tell us about an interesting project, preferably outside of class or work, that two or more of you created together. Include urls if possible.

Tanu and Eric have known each other via the PhD program at Georgia Tech. Eric advised Tanu throughout her PhD, and they worked on a number of projects together. A paper from 2014, “Phrases that Predict Success on Kickstarter” (http://comp.social.gatech.edu/papers/cscw14.crowdfunding.mitra.pdf) resulted in many conversations outside of academia, with entrepreneurs wanting this as a product. Christian and Eric worked closely together throughout the summer of 2016 on a project called HackGT@UPC as one of the first international, collaborative hackathons. With Eric’s faculty oversight, Christian built this event over the course of a few months around the subject of “assistive technologies” (https://hack-gtupc.devpost.com/).

How long have the founders known one another and how did you meet? Have any of the founders not met in person?

Tanu and Christian met through Professor Eric Gilbert at Georgia Tech about 2 months ago. Eric is currently Tanu's PhD advisor and Christian worked with Eric as his teaching assistant during the GT study abroad program in Barcelona, Spain last summer. When Tanu came to Eric about turning her PhD research project into a full-blown company, Eric immediately thought of Christian. Tanu and Christian met a couple days later for coffee/discussion and jumped into customer discovery and a bit of market research, eventually leading them here to this application!

Category

Which category best applies to your company?

Artificial Intelligence

Is this application in response to a YC RFS?

Yes

If yes, which one?

News

Progress

How far along are you?

Starting in 2014, Tanu has been working on this model, the science and the technologies surrounding it for about 3 years now. Our first major paper (http://credcount.com/whitepaper) was reviewed and published in late 2014 in a top CS conference. Although the majority of the work such as data collection was finished in approximately 8 or so months, there are certain analyses that have taken place up until today. That said, we are in what we consider the prototyping stage: the service/technology in its current form is not suited to any specific market but rather a proof of concept. With a little bit of modification to the machine learning models, we feel as though we can target new markets in as little as a couple weeks. Thus, Christian has spent the last few months trying to identify where we fit and have the biggest effect as a company/product. So far, we’ve considered news, politics, finance and the public sector. With contacts such as news anchor George Howell from CNN, political advisor and campaign manager Jason Boles and Senior Vice President of investments Mr. Jeff Wolk from Raymond James in the financial sector, we feel as though our best fit at this moment is to ride the “fake news” wave. Currently there is no window dressing to our model. In its current form, it is command-line driven (literally “python model.py [input]”), but we feel as though who our potential customers might be should be identified before we begin to work on any sort of graphical interface and/or API. That’s why we’ve come to YC. Our idea currently is a product/application built around our technology as an easy to use display of current news items and their credibility rankings such that someone might be able to read through an article with a presumed level of bias or accuracy up front. It comes through the narrative of “news literacy” and an attempt to have all sides of every story. Think if every fact or bullet from a news article could have a certain score for credibility such that the least items with the least standing fall to the way side and only the true and solid facts of the situation are left. How long have each of you been working on this? Have you been part-time or full-time? Please explain. Tanu has been working on CredCount since early 2014 as part of her PhD in social computing with her advisor Eric Gilbert at Georgia Tech. The majority of data collection and analysis led into early 2015, and she’s continued to circle back to it in a “part-time” role as a project with the most potential as a product/service outside of academia. Christian joined on early this year, 2017, in a product manager capacity to build this paper/model into a business. Working part-time while taking classes since his joining, he’s found himself devoting more time towards this venture than schoolwork, such that Tanu and Christian both are ready to commit to the project for the summer and however long after that.

Which of the following best describes your progress?

Prototype

How many active users or customers do you have? If you have some particularly valuable customers, who are they? If you're building hardware, how many units have you shipped?

None.

When will you have a prototype or beta?

N/A

Do you have revenue?

No

We're interested in your revenue over the last several months. (Not cumulative and not GMV).

N/A

Anything else you would like us to know regarding your revenue or growth rate?

Nope.

If you are applying with the same idea as a previous batch, did anything change? If you applied with a different idea, why did you pivot and what did you learn from the last idea?

N/A

If you have already participated or committed to participate in an incubator, "accelerator" or "pre-accelerator" program, please tell us about it.

N/A

Idea

Why did you pick this idea to work on? Do you have domain expertise in this area? How do you know people need what you're making?

Tanu was attracted to the project by the prevalence of fake news spreading online following natural disasters such as Superstorm Sandy in 2012. As she saw people sharing a lot of incorrect or misleading information about the events, Tanu decided to track both big stories and smaller rumors with the goal of creating an app that could help ordinary people sort fact from fiction to make decisions that could be crucial. Tanu has an undergraduate degree in computer engineering and is currently finishing her PhD in computer science with a specialization in social computing. Christian is working on his undergraduate degree, but is focusing into the domain of people and society. He’s currently finishing up a Georgia Tech sponsored paper on the website http://voat.co with qualitative research through interviews with users. Based on Christian’s market research, the financial, political and news markets seem to be the most motivated to move forward with our work. In my discussions with Jason Boles, he would like to use our model as a sort of real-time polling for statements made from congressional candidate Kurt Wilson. While discussing the project with Jeff Wolk from Raymond James, Wolk proposed to use the service for adding credibility to events through social media for commodity trading decisions. Also, we have discussed the possibility of parsing analyst papers and summarizing the ideas and decisions they propose with scores as well. Lastly, in talks with George Howell, CredCount would be a great addition to the new media tool-set used in collaboration with pre-existing service like https://www.dataminer.co/. With this, instead of focusing on volume and noise alone, news outlets could intelligently use the world through social media as credibility to a story or event.

What's new about what you're making? What substitutes do people resort to because it doesn't exist yet (or they don't know about it)?

Basic event analysis has existed in its current form, based on volume and noise alone. The ability to take collective reactions through social media to help determine reliability of a specific event is what we bring to the table. Think of it as a haystack of social media posts and events that finding a needle in is close to impossible. We help shrink this haystack to make fact checking far easier.

Who are your competitors, and who might become competitors? Who do you fear most?

Based on data science and specific to the social computing field, we fear FactMata (https://medium.com/factmata/introducing-factmata-artificial-intelligence-for-political-fact-checking-db8acdbf4cf1) the most. As far as we know, to this day, they have no model or product and haven’t released anything close to the technology described within our whitepaper. That said, other fact checking websites will be our main competitors such as Snopes (http://www.snopes.com/) and PolitiFact. Otherwise, as Google and Facebook begin to roll out their own solutions these will be internal solutions to problems faced by the all media services on the internet. We do not fear these big guys as much because we stand to help all the rest using a paid for plug-in service if we decide to go that route.

What do you understand about your business that other companies in it just don't get?

The data science and linguistics portion of these potential markets. What sort of language implies the most credible financial analysis of one specific commodity? Or, how well a certain political statement goes over with a constituency? Or, what sort of language might you look out for when flagging something as potentially fake? Currently with some sort of human touch these sorts of questions can be answered but it might take a bit of time while doing so. We’re attempting to help narrow into the credible pieces.

How do or will you make money? How much could you make?

A specific case we’ve looked into is a contract or subscription service with big media companies. Similarly, DataMiner has made millions helping the news guys observe events as they happen going from labels of “this might be newsworthy” to “lots of people are talking about this; you should check this out”. We hope that our service will be an even bigger asset to news media companies bringing more than volume statistics through analytics. With early research, these contracts have figures in the millions.

How will you get users? If your idea is the type that faces a chicken-and-egg problem in the sense that it won't be attractive to users till it has a lot of users (e.g. a marketplace, a dating site, an ad network), how will you overcome that?

For a product of our own we’d need to attract potential users by having a narrative for the social benefit of “news literacy”. Minds are changed through emotion rather than factual concrete evidence so this application would be more of a training to see both sides idea instead of showing someone raw evidence against their position. In this manner we’d be very chicken-and-egg. As a service, we’ll get users by sales and marketing showing how much value we could add to markets like high frequency trading or politics. We’d have to lineup demos and grow through grassroots from the beginning.

Equity

Have you incorporated, or formed any legal entity (like an LLC) yet?

No

What kind of entity and in what state or country was the entity formed?

N/A

Please describe the breakdown of the equity ownership in percentages among the founders, employees and any other stockholders. If there are multiple founders, be sure to give the equity ownership of each founder.

N/A

List any investments your company has received. Include the name of the investor, the amount invested, the premoney valuation / valuation cap, and the type of security sold (convertible notes, safes or stock).

N/A

How much money do you spend per month?

N/A

How much money does your company have in the bank now?

N/A

How long is your runway?

infinite, (no money in or out yet) but we think we can get to market in around 3 months

Please provide any other relevant information about the structure or formation of the company.

Christian Battaglia will be in charge of most business-oriented tasks and product management of the service/platform. Tanushree will be focusing on data science, machine learning and the specifics of the model tailored to whatever market we decide on. Eric will be in an scientific advising role, based on his expertise through many years of social computing.

Legal

Are any of the founders covered by noncompetes or intellectual property agreements that overlap with your project? If so, please explain.

No.

Who writes code, or does other technical work on your product? Was any of it done by a non-founder? Please explain.

Tanu wrote the code, setup the Turkers and has done all technical work on the project so far. Christian is finishing his undergrad at Georgia Tech and has about 3 total years of work experience in internship, co-operative and other part-time capacities. He’s comfortable reviewing all code for the project in its current form as it relates to AI and machine learning. Christian was also previously head of web development at the European startup Glovo (http://glovoapp.com). That said, all technology was developed by founding members.

Is there anything else we should know about your company?

Funded originally by a DARPA grant and under the umbrella of Georgia Tech, you might think that we would need to license this idea or give some credit to the university, but that is not the case. The primary paper has been out for over a year, meaning it cannot be patented. The nature of the technology--highly specific to social computing, machine learning and linguistics--form a steep barrier.

Others

If you had any other ideas you considered applying with, please list them. One may be something we've been waiting for. Often when we fund people it's to do something they list here and not in the main application.

Christian’s ideas include:

- A social experiment much like Facebook where every login or session has you assume the role and identity of a fictitious person. All data created by you while in the role of this person persists upon session expiration or sign out. Each time you log back in you must put yourself in the shoes of the previously created identity and post as such breaking the echo chamber that is social media today while also having fun.

- Event-going today has turned into a very spontaneous unscheduled sort of thing. Often times I find myself wishing to get in touch with the people I met within the context of some random evening for pictures, videos and just networking in general. Think of this as a digital collection of your concert/sports tickets where each one expands into your experience prior and after the specific event!

- Having spent 3 summers abroad in Barcelona working closely with nightlife promoters, one thing they always talk about for general improvement of their day to day is a way to more easily get in touch with their network to predict figures for guest lists and to not spam as much. Think of this as a 1 degree removed connection of the promoter and I want to hand over my network list of those specific few that are headed to Barcelona for vacation. Hopefully this would be that he’d have my network only for the time that my friends are travelling to cut through on the spam.

Please tell us something surprising or amusing that one of you has discovered.

Christian: I over analyze social settings a bit too much and over the years I’ve noticed just how much weight we give first impressions… With that, I always try and give strangers and new contacts the time of day/benefit of the doubt while attempting to be my best self in return.

Curious

What convinced you to apply to Y Combinator? Did someone encourage you to apply?

When the triple request for startups based around news, jobs and democracy dropped, Tanushree and Christian immediately decided that it was time to apply. After years mulling around with the idea that this model could potentially be a business, we decided to jump all in.

How did you hear about Y Combinator?

Online and through friends


If you have a demo, what's the url? Demo can be anything that shows us how the product works. Usually that's a video or screen recording.

http://credcount.com/whitepaper

Describe what your company does in 50 characters or less.

AI based fact checking through social media data

What is your company going to make? Please describe your product and what it does or will do.

An analytics platform, backed by data science and machine learning, used to infer the credibility of events in real-time.

Where do you live now, and where would the company be based after YC?

Atlanta, GA, United States / New York, New York, United States

Founders

Please enter the url of a 1 minute unlisted (not private) YouTube video introducing the founder(s). This video is an important part of the application. (Follow the Video Guidelines.)

Please tell us about an interesting project, preferably outside of class or work, that two or more of you created together. Include urls if possible.

Tanu and Eric have known each other via the PhD program at Georgia Tech. Eric advised Tanu throughout her PhD, and they worked on a number of projects together. A paper from 2014, “Phrases that Predict Success on Kickstarter” (http://comp.social.gatech.edu/papers/cscw14.crowdfunding.mitra.pdf) resulted in many conversations outside of academia, with entrepreneurs wanting this as a product. Christian and Eric worked closely together throughout the summer of 2016 on a project called HackGT@UPC as one of the first international, collaborative hackathons. With Eric’s faculty oversight, Christian built this event over the course of a few months around the subject of “assistive technologies” (https://hack-gtupc.devpost.com/).

How long have the founders known one another and how did you meet? Have any of the founders not met in person?

Tanu and Christian met through Professor Eric Gilbert at Georgia Tech about 2 months ago. Eric is currently Tanu's PhD advisor and Christian worked with Eric as his teaching assistant during the GT study abroad program in Barcelona, Spain last summer. When Tanu came to Eric about turning her PhD research project into a full-blown company, Eric immediately thought of Christian. Tanu and Christian met a couple days later for coffee/discussion and jumped into customer discovery and a bit of market research, eventually leading them here to this application!

Category

Which category best applies to your company?

Artificial Intelligence

Is this application in response to a YC RFS?

Yes

If yes, which one?

News

Progress

How far along are you?

Starting in 2014, Tanu has been working on this model, the science and the technologies surrounding it for about 3 years now. Our first major paper (http://credcount.com/whitepaper) was reviewed and published in late 2014 in a top CS conference. Although the majority of the work such as data collection was finished in approximately 8 or so months, there are certain analyses that have taken place up until today. That said, we are in what we consider the prototyping stage: the service/technology in its current form is not suited to any specific market but rather a proof of concept. With a little bit of modification to the machine learning models, we feel as though we can target new markets in as little as a couple weeks. Thus, Christian has spent the last few months trying to identify where we fit and have the biggest effect as a company/product. So far, we’ve considered news, politics, finance and the public sector. With contacts such as news anchor George Howell from CNN, political advisor and campaign manager Jason Boles and Senior Vice President of investments Mr. Jeff Wolk from Raymond James in the financial sector, we feel as though our best fit at this moment is to ride the “fake news” wave. Currently there is no window dressing to our model. In its current form, it is command-line driven (literally “python model.py [input]”), but we feel as though who our potential customers might be should be identified before we begin to work on any sort of graphical interface and/or API. That’s why we’ve come to YC. Our idea currently is a product/application built around our technology as an easy to use display of current news items and their credibility rankings such that someone might be able to read through an article with a presumed level of bias or accuracy up front. It comes through the narrative of “news literacy” and an attempt to have all sides of every story. Think if every fact or bullet from a news article could have a certain score for credibility such that the least items with the least standing fall to the way side and only the true and solid facts of the situation are left. How long have each of you been working on this? Have you been part-time or full-time? Please explain. Tanu has been working on CredCount since early 2014 as part of her PhD in social computing with her advisor Eric Gilbert at Georgia Tech. The majority of data collection and analysis led into early 2015, and she’s continued to circle back to it in a “part-time” role as a project with the most potential as a product/service outside of academia. Christian joined on early this year, 2017, in a product manager capacity to build this paper/model into a business. Working part-time while taking classes since his joining, he’s found himself devoting more time towards this venture than schoolwork, such that Tanu and Christian both are ready to commit to the project for the summer and however long after that.

Which of the following best describes your progress?

Prototype

How many active users or customers do you have? If you have some particularly valuable customers, who are they? If you're building hardware, how many units have you shipped?

None.

When will you have a prototype or beta?

N/A

Do you have revenue?

No

We're interested in your revenue over the last several months. (Not cumulative and not GMV).

N/A

Anything else you would like us to know regarding your revenue or growth rate?

Nope.

If you are applying with the same idea as a previous batch, did anything change? If you applied with a different idea, why did you pivot and what did you learn from the last idea?

N/A

If you have already participated or committed to participate in an incubator, "accelerator" or "pre-accelerator" program, please tell us about it.

N/A

Idea

Why did you pick this idea to work on? Do you have domain expertise in this area? How do you know people need what you're making?

Tanu was attracted to the project by the prevalence of fake news spreading online following natural disasters such as Superstorm Sandy in 2012. As she saw people sharing a lot of incorrect or misleading information about the events, Tanu decided to track both big stories and smaller rumors with the goal of creating an app that could help ordinary people sort fact from fiction to make decisions that could be crucial. Tanu has an undergraduate degree in computer engineering and is currently finishing her PhD in computer science with a specialization in social computing. Christian is working on his undergraduate degree, but is focusing into the domain of people and society. He’s currently finishing up a Georgia Tech sponsored paper on the website http://voat.co with qualitative research through interviews with users. Based on Christian’s market research, the financial, political and news markets seem to be the most motivated to move forward with our work. In my discussions with Jason Boles, he would like to use our model as a sort of real-time polling for statements made from congressional candidate Kurt Wilson. While discussing the project with Jeff Wolk from Raymond James, Wolk proposed to use the service for adding credibility to events through social media for commodity trading decisions. Also, we have discussed the possibility of parsing analyst papers and summarizing the ideas and decisions they propose with scores as well. Lastly, in talks with George Howell, CredCount would be a great addition to the new media tool-set used in collaboration with pre-existing service like https://www.dataminer.co/. With this, instead of focusing on volume and noise alone, news outlets could intelligently use the world through social media as credibility to a story or event.

What's new about what you're making? What substitutes do people resort to because it doesn't exist yet (or they don't know about it)?

Basic event analysis has existed in its current form, based on volume and noise alone. The ability to take collective reactions through social media to help determine reliability of a specific event is what we bring to the table. Think of it as a haystack of social media posts and events that finding a needle in is close to impossible. We help shrink this haystack to make fact checking far easier.

Who are your competitors, and who might become competitors? Who do you fear most?

Based on data science and specific to the social computing field, we fear FactMata (https://medium.com/factmata/introducing-factmata-artificial-intelligence-for-political-fact-checking-db8acdbf4cf1) the most. As far as we know, to this day, they have no model or product and haven’t released anything close to the technology described within our whitepaper. That said, other fact checking websites will be our main competitors such as Snopes (http://www.snopes.com/) and PolitiFact. Otherwise, as Google and Facebook begin to roll out their own solutions these will be internal solutions to problems faced by the all media services on the internet. We do not fear these big guys as much because we stand to help all the rest using a paid for plug-in service if we decide to go that route.

What do you understand about your business that other companies in it just don't get?

The data science and linguistics portion of these potential markets. What sort of language implies the most credible financial analysis of one specific commodity? Or, how well a certain political statement goes over with a constituency? Or, what sort of language might you look out for when flagging something as potentially fake? Currently with some sort of human touch these sorts of questions can be answered but it might take a bit of time while doing so. We’re attempting to help narrow into the credible pieces.

How do or will you make money? How much could you make?

A specific case we’ve looked into is a contract or subscription service with big media companies. Similarly, DataMiner has made millions helping the news guys observe events as they happen going from labels of “this might be newsworthy” to “lots of people are talking about this; you should check this out”. We hope that our service will be an even bigger asset to news media companies bringing more than volume statistics through analytics. With early research, these contracts have figures in the millions.

How will you get users? If your idea is the type that faces a chicken-and-egg problem in the sense that it won't be attractive to users till it has a lot of users (e.g. a marketplace, a dating site, an ad network), how will you overcome that?

For a product of our own we’d need to attract potential users by having a narrative for the social benefit of “news literacy”. Minds are changed through emotion rather than factual concrete evidence so this application would be more of a training to see both sides idea instead of showing someone raw evidence against their position. In this manner we’d be very chicken-and-egg. As a service, we’ll get users by sales and marketing showing how much value we could add to markets like high frequency trading or politics. We’d have to lineup demos and grow through grassroots from the beginning.

Equity

Have you incorporated, or formed any legal entity (like an LLC) yet?

No

What kind of entity and in what state or country was the entity formed?

N/A

Please describe the breakdown of the equity ownership in percentages among the founders, employees and any other stockholders. If there are multiple founders, be sure to give the equity ownership of each founder.

N/A

List any investments your company has received. Include the name of the investor, the amount invested, the premoney valuation / valuation cap, and the type of security sold (convertible notes, safes or stock).

N/A

How much money do you spend per month?

N/A

How much money does your company have in the bank now?

N/A

How long is your runway?

infinite, (no money in or out yet) but we think we can get to market in around 3 months

Please provide any other relevant information about the structure or formation of the company.

Christian Battaglia will be in charge of most business-oriented tasks and product management of the service/platform. Tanushree will be focusing on data science, machine learning and the specifics of the model tailored to whatever market we decide on. Eric will be in an scientific advising role, based on his expertise through many years of social computing.

Legal

Are any of the founders covered by noncompetes or intellectual property agreements that overlap with your project? If so, please explain.

No.

Who writes code, or does other technical work on your product? Was any of it done by a non-founder? Please explain.

Tanu wrote the code, setup the Turkers and has done all technical work on the project so far. Christian is finishing his undergrad at Georgia Tech and has about 3 total years of work experience in internship, co-operative and other part-time capacities. He’s comfortable reviewing all code for the project in its current form as it relates to AI and machine learning. Christian was also previously head of web development at the European startup Glovo (http://glovoapp.com). That said, all technology was developed by founding members.

Is there anything else we should know about your company?

Funded originally by a DARPA grant and under the umbrella of Georgia Tech, you might think that we would need to license this idea or give some credit to the university, but that is not the case. The primary paper has been out for over a year, meaning it cannot be patented. The nature of the technology--highly specific to social computing, machine learning and linguistics--form a steep barrier.

Others

If you had any other ideas you considered applying with, please list them. One may be something we've been waiting for. Often when we fund people it's to do something they list here and not in the main application.

Christian’s ideas include:

- A social experiment much like Facebook where every login or session has you assume the role and identity of a fictitious person. All data created by you while in the role of this person persists upon session expiration or sign out. Each time you log back in you must put yourself in the shoes of the previously created identity and post as such breaking the echo chamber that is social media today while also having fun.

- Event-going today has turned into a very spontaneous unscheduled sort of thing. Often times I find myself wishing to get in touch with the people I met within the context of some random evening for pictures, videos and just networking in general. Think of this as a digital collection of your concert/sports tickets where each one expands into your experience prior and after the specific event!

- Having spent 3 summers abroad in Barcelona working closely with nightlife promoters, one thing they always talk about for general improvement of their day to day is a way to more easily get in touch with their network to predict figures for guest lists and to not spam as much. Think of this as a 1 degree removed connection of the promoter and I want to hand over my network list of those specific few that are headed to Barcelona for vacation. Hopefully this would be that he’d have my network only for the time that my friends are travelling to cut through on the spam.

Please tell us something surprising or amusing that one of you has discovered.

Christian: I over analyze social settings a bit too much and over the years I’ve noticed just how much weight we give first impressions… With that, I always try and give strangers and new contacts the time of day/benefit of the doubt while attempting to be my best self in return.

Curious

What convinced you to apply to Y Combinator? Did someone encourage you to apply?

When the triple request for startups based around news, jobs and democracy dropped, Tanushree and Christian immediately decided that it was time to apply. After years mulling around with the idea that this model could potentially be a business, we decided to jump all in.

How did you hear about Y Combinator?

Online and through friends


See Why

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Executive Summary:

The application for CredCount presents an AI-based fact-checking tool that utilizes social media data to assess the credibility of events and information in real-time. The founders demonstrate a strong academic background in data science and machine learning, as well as relevant industry experience. They propose to serve markets affected by the spread of misinformation, including news, finance, and politics. Currently, CredCount is in the prototyping stage without a proper customer-facing product. The team is looking to Y Combinator for guidance on market fit and to develop their product further for market entry.

Investment Thesis Relevance:

CredCount's focus on tackling misinformation through AI and fact-checking aligns well with YC's interest in startups that use technology to address socio-political challenges, such as the spread of fake news. Given the current global attention on the veracity of online information, CredCount has the potential to not only serve a pressing need but also to tap into a significant market opportunity. However, being in the early stages of product development might be a concern regarding their immediate readiness for investment.

Market Understanding and Strategy:

The founders exhibit a decent understanding of the problem of misinformation and have engaged with potential users from their target industries. However, the application could benefit from a more detailed analysis of the market size, customer acquisition strategies, and competitive landscape. Emphasizing non-competitors (like Google and Facebook) as potential partners could be a strategic angle to explore.

Business Model Evaluation:

The business model centers on contracts or subscriptions with media companies, reminiscent of existing services like DataMiner. While potentially lucrative, the model relies heavily on the sale to major media companies without a clear contingency plan for direct consumer acquisition. The application should expand on other customer segments and diversify revenue streams for better sustainability.

Team Competency and Dynamics:

The team consists of individuals with relevant technical expertise and previous project collaboration experience. However, the recent addition of a member and a limited track record working as a team on this specific project might raise concerns. Stating more explicitly each founder's contribution and reinforcing the complementary skills within the team might enhance their profile.

Operational Efficiency and Milestone Achievement:

While there is evidence of significant academic progress and recent market research efforts, there is still no functional product or active users. Key operational strategies and timelines for product launch and user acquisition are absent. Providing a concrete roadmap with achievable milestones and demonstrating efficiency in reaching them would strengthen the application.

Use of Language:

The language used in the application is straightforward and generally free of jargon. Nonetheless, some explanations are lengthy and could be more concise. Precision in language that succinctly communicates complex machine learning processes will make the application more compelling.

Financial Health and Projections:

The financial aspect is largely speculative, with no current revenue or investments stated. While projections are optimistic, identifying and planning for key financial metrics and runway requirements would demonstrate a better grasp of the financial aspects of running a startup.

Constructive Criticism and Advice:

The founders should focus on the following to improve their application:

  • Clarify the value proposition and how it differs from competitors.

  • Present a more detailed market analysis and a strategy for customer acquisition.

  • Elaborate on the financial model, including projected revenue, costs, and funding requirements.

  • Articulate a clear product development timeline and operational milestones.

  • Highlight the team's strengths and potential for synergy more convincingly.

  • Consider developing a minimal viable product (MVP) that can be tested with early adopters.

Final Thoughts:

CredCount presents as an interesting entrant in the AI fact-checking space with a strong technical foundation. However, to improve chances with YC, the founders must address the critical issues regarding market fit, business strategy, and product development timeliness. Focusing on creating a viable MVP and securing initial user feedback could significantly bolster the application's appeal to Y Combinator.

Executive Summary:

The application for CredCount presents an AI-based fact-checking tool that utilizes social media data to assess the credibility of events and information in real-time. The founders demonstrate a strong academic background in data science and machine learning, as well as relevant industry experience. They propose to serve markets affected by the spread of misinformation, including news, finance, and politics. Currently, CredCount is in the prototyping stage without a proper customer-facing product. The team is looking to Y Combinator for guidance on market fit and to develop their product further for market entry.

Investment Thesis Relevance:

CredCount's focus on tackling misinformation through AI and fact-checking aligns well with YC's interest in startups that use technology to address socio-political challenges, such as the spread of fake news. Given the current global attention on the veracity of online information, CredCount has the potential to not only serve a pressing need but also to tap into a significant market opportunity. However, being in the early stages of product development might be a concern regarding their immediate readiness for investment.

Market Understanding and Strategy:

The founders exhibit a decent understanding of the problem of misinformation and have engaged with potential users from their target industries. However, the application could benefit from a more detailed analysis of the market size, customer acquisition strategies, and competitive landscape. Emphasizing non-competitors (like Google and Facebook) as potential partners could be a strategic angle to explore.

Business Model Evaluation:

The business model centers on contracts or subscriptions with media companies, reminiscent of existing services like DataMiner. While potentially lucrative, the model relies heavily on the sale to major media companies without a clear contingency plan for direct consumer acquisition. The application should expand on other customer segments and diversify revenue streams for better sustainability.

Team Competency and Dynamics:

The team consists of individuals with relevant technical expertise and previous project collaboration experience. However, the recent addition of a member and a limited track record working as a team on this specific project might raise concerns. Stating more explicitly each founder's contribution and reinforcing the complementary skills within the team might enhance their profile.

Operational Efficiency and Milestone Achievement:

While there is evidence of significant academic progress and recent market research efforts, there is still no functional product or active users. Key operational strategies and timelines for product launch and user acquisition are absent. Providing a concrete roadmap with achievable milestones and demonstrating efficiency in reaching them would strengthen the application.

Use of Language:

The language used in the application is straightforward and generally free of jargon. Nonetheless, some explanations are lengthy and could be more concise. Precision in language that succinctly communicates complex machine learning processes will make the application more compelling.

Financial Health and Projections:

The financial aspect is largely speculative, with no current revenue or investments stated. While projections are optimistic, identifying and planning for key financial metrics and runway requirements would demonstrate a better grasp of the financial aspects of running a startup.

Constructive Criticism and Advice:

The founders should focus on the following to improve their application:

  • Clarify the value proposition and how it differs from competitors.

  • Present a more detailed market analysis and a strategy for customer acquisition.

  • Elaborate on the financial model, including projected revenue, costs, and funding requirements.

  • Articulate a clear product development timeline and operational milestones.

  • Highlight the team's strengths and potential for synergy more convincingly.

  • Consider developing a minimal viable product (MVP) that can be tested with early adopters.

Final Thoughts:

CredCount presents as an interesting entrant in the AI fact-checking space with a strong technical foundation. However, to improve chances with YC, the founders must address the critical issues regarding market fit, business strategy, and product development timeliness. Focusing on creating a viable MVP and securing initial user feedback could significantly bolster the application's appeal to Y Combinator.

Executive Summary:

The application for CredCount presents an AI-based fact-checking tool that utilizes social media data to assess the credibility of events and information in real-time. The founders demonstrate a strong academic background in data science and machine learning, as well as relevant industry experience. They propose to serve markets affected by the spread of misinformation, including news, finance, and politics. Currently, CredCount is in the prototyping stage without a proper customer-facing product. The team is looking to Y Combinator for guidance on market fit and to develop their product further for market entry.

Investment Thesis Relevance:

CredCount's focus on tackling misinformation through AI and fact-checking aligns well with YC's interest in startups that use technology to address socio-political challenges, such as the spread of fake news. Given the current global attention on the veracity of online information, CredCount has the potential to not only serve a pressing need but also to tap into a significant market opportunity. However, being in the early stages of product development might be a concern regarding their immediate readiness for investment.

Market Understanding and Strategy:

The founders exhibit a decent understanding of the problem of misinformation and have engaged with potential users from their target industries. However, the application could benefit from a more detailed analysis of the market size, customer acquisition strategies, and competitive landscape. Emphasizing non-competitors (like Google and Facebook) as potential partners could be a strategic angle to explore.

Business Model Evaluation:

The business model centers on contracts or subscriptions with media companies, reminiscent of existing services like DataMiner. While potentially lucrative, the model relies heavily on the sale to major media companies without a clear contingency plan for direct consumer acquisition. The application should expand on other customer segments and diversify revenue streams for better sustainability.

Team Competency and Dynamics:

The team consists of individuals with relevant technical expertise and previous project collaboration experience. However, the recent addition of a member and a limited track record working as a team on this specific project might raise concerns. Stating more explicitly each founder's contribution and reinforcing the complementary skills within the team might enhance their profile.

Operational Efficiency and Milestone Achievement:

While there is evidence of significant academic progress and recent market research efforts, there is still no functional product or active users. Key operational strategies and timelines for product launch and user acquisition are absent. Providing a concrete roadmap with achievable milestones and demonstrating efficiency in reaching them would strengthen the application.

Use of Language:

The language used in the application is straightforward and generally free of jargon. Nonetheless, some explanations are lengthy and could be more concise. Precision in language that succinctly communicates complex machine learning processes will make the application more compelling.

Financial Health and Projections:

The financial aspect is largely speculative, with no current revenue or investments stated. While projections are optimistic, identifying and planning for key financial metrics and runway requirements would demonstrate a better grasp of the financial aspects of running a startup.

Constructive Criticism and Advice:

The founders should focus on the following to improve their application:

  • Clarify the value proposition and how it differs from competitors.

  • Present a more detailed market analysis and a strategy for customer acquisition.

  • Elaborate on the financial model, including projected revenue, costs, and funding requirements.

  • Articulate a clear product development timeline and operational milestones.

  • Highlight the team's strengths and potential for synergy more convincingly.

  • Consider developing a minimal viable product (MVP) that can be tested with early adopters.

Final Thoughts:

CredCount presents as an interesting entrant in the AI fact-checking space with a strong technical foundation. However, to improve chances with YC, the founders must address the critical issues regarding market fit, business strategy, and product development timeliness. Focusing on creating a viable MVP and securing initial user feedback could significantly bolster the application's appeal to Y Combinator.