Real-Time Government Analysis
Timothy Hwang | Jonathan Chen | Gerald Yao
July 2013
Hello Government!
Co-Founders 1
Jonathan Chen, CTO
University of Maryland,
Computer Science
Gerald Yao, CFO
Emory University,
Financial Engi...
Dev Shah,
Lead Back End
Dan Maslagag,
Lead Front End
JiajunNiu, PhD
Machine Learning
Kareem Hashem,
Senior Back End
Team a...
Problem 3
Information has fragmented
Government Information moves markets and affect
business decisions.
Newspapers and La...
Solution 4
A web platform that aggregates, analyzes, and predicts
government information using machine learning and NLP.
R...
The Future of News 5
1850 1960 20102000
Local Newspapers Television Social Media Machine Learning
PoliticalGenome Project 6
Bills
Improves prediction
algorithms and allows for
comparison across
states
Legislators
Allows ...
Market Size 7
$30 Billion
$10 Billion
Total Spending on Policy Analysis
Total Spending on Newspaper Subscriptions
Product 8
Track Legislation and News  Get Relevant Portions  Predict
Market Adoption 9
Conventions
West Coast Government
Technology Conference
National Conference of
State Legislators
Build P...
Traction
April 2013
Ideation
July 2013
MVP I
August 2013
Private Beta
10
Est. Oct. 2013
MVP II w/ Mobile
Est. Dec. 2013
Co...
Business Model
We charge a monthly subscription for each organization.
25
5,000+
Users
AVG MO. FEE
$2,500/mo
$250,000
Q4 R...
Competition
Real-time
Slower
Low RelevanceHigh Relevance
12
Competitive Advantages 13
First Mover
Proprietary ScraperProprietary Algorithms
We are currently the only
company to apply...
Our Vision 14
1. Expand Government
Verticals
3. Open Data Platform2. Global Expansion
Regulations, court
cases, speeches, ...
Ask
$740,000
Convertible Debt
1. More Developers for NLP
2. Business Development to close 100
3. Move Office to DC
4. Test...
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FN

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  • FiscalNote is a the quick, customized way for you to receive government information online.
  • Jiajun – top recommender systems from GrouponAndrew – wrote much of the initial NLP models (Brown Corpus) and 30+ years of NLP experience
  • Talk about experience heading up the NYA in Washington, DC = Information is currencyGovernment Information (Unstructured data: Legislation, Regulations, even Speeches) move markets and affect business decisions.Newspapers and Lawyers are realizing that their methods are too costly and slow. Information has fragmented. And no easy way exists to get relevant state and local government information quickly and reliably. * Patton Boggs (largest government regulations lobbying firm just laid of 17 partners) * State and Local Reporting is down 80% in the last 2 decades with massive layoffs in the Journalist industry (while National news is up)* According to the National Federation of Independent Businesses, the #1 concern for small and medium size businesses is keeping up with new laws and regulations (high uncertainty)
  • Make it easier to understand government impact
  • We’re not just replacing these institutions, we’re on the cutting edge of the future of newsESPN and Bloomberg already following this model with their instantaneous articles based on sports scores or stock news. Do for politics what was done for finance and sports.
  • Followthemoney.orgState of the Media (Pew)
  • Scraper that takes information on pending legislation across all 50 states every 200 microsecondsNews and tweet analyzer that tracks current micro trendsMapReduce function to get relevant bills for an industry and portions to applySentiment analysis and relational algorithm (moneyball for politics) to predict legislation
  • This new product is marketed to those most impacted by government actions, including influencers (NGOs, associations, etc.), and business leaders (C-suite executives, federal contractors, internal government affairs teams, etc.). The product brings the an unparalleled level of data-based and fact-based, objective reporting and analysis to policymaking.
  • 1 Million in ARR 2013 4.56 Million in ARR 2014 10,000 for hedge funds and financial firms like Goldman, 8,000 for companies like Pfizer (foreign companies, fortune 500, federal and state agencies, etc.) and <300 for local government agencies/non-profits/charities. Current monthly burn: 12,000/mo ramping up to 185k Expected net income by end of year (740k)Contract Size: $2,500/moProj. Lifetime Value: $150,000Engagement: 10-15 EmployeesCost Savings: $162,000/yr+CAC: $5,200 Exclusive licensing if client is willing to pay for 20% of Market
  • Machine learning algorithms become better with more data
  • Our vision world domination
  • Series A next Spring (10 month runway)Very hands on (demos)
  • Transcript of "FN"

    1. 1. Real-Time Government Analysis Timothy Hwang | Jonathan Chen | Gerald Yao July 2013 Hello Government!
    2. 2. Co-Founders 1 Jonathan Chen, CTO University of Maryland, Computer Science Gerald Yao, CFO Emory University, Financial Engineering Tim Hwang, CEO Princeton University, Public Policy/Computer Science
    3. 3. Dev Shah, Lead Back End Dan Maslagag, Lead Front End JiajunNiu, PhD Machine Learning Kareem Hashem, Senior Back End Team and Advisors YS Chi, Advisor * Chairman, Elsevier (Global FN 500) * President, International Publishers Association Chris Lu, Advisor * Senior Advisor to President Obama * White House Cabinet Secretary * Executive Director, Obama Transition Team 2 Andrew Mackie, PhD Chief Scientist
    4. 4. Problem 3 Information has fragmented Government Information moves markets and affect business decisions. Newspapers and Lawyers are realizing that their methods are too costly and slow. Information has fragmented. And no easy way exists to get relevant the information quickly and reliably.
    5. 5. Solution 4 A web platform that aggregates, analyzes, and predicts government information using machine learning and NLP. REDUCE UNCERTAINTY REAL-TIME DECISION MAKING CUSTOM ANALYSIS
    6. 6. The Future of News 5 1850 1960 20102000 Local Newspapers Television Social Media Machine Learning
    7. 7. PoliticalGenome Project 6 Bills Improves prediction algorithms and allows for comparison across states Legislators Allows for comparison of new legislators Districts Brings in the actual electoral element of politics Building a political graph over the entire world.
    8. 8. Market Size 7 $30 Billion $10 Billion Total Spending on Policy Analysis Total Spending on Newspaper Subscriptions
    9. 9. Product 8 Track Legislation and News  Get Relevant Portions  Predict
    10. 10. Market Adoption 9 Conventions West Coast Government Technology Conference National Conference of State Legislators Build PR Direct Sales Industries most affected by local/state law (real estate, healthcare) Financial firms (hedge funds, investment banks) Consulting groups around compliance Referrals National advocacy groups refer to local chapters Contractors refer to local and state agencies Early Adopters Early Majority Late Majority
    11. 11. Traction April 2013 Ideation July 2013 MVP I August 2013 Private Beta 10 Est. Oct. 2013 MVP II w/ Mobile Est. Dec. 2013 Court Cases We move fast. +15 +35 Est. Jan. 2014 Global Expansion
    12. 12. Business Model We charge a monthly subscription for each organization. 25 5,000+ Users AVG MO. FEE $2,500/mo $250,000 Q4 REVENUE 8/13-12/13 $150M REVENUE 2014-2017 11
    13. 13. Competition Real-time Slower Low RelevanceHigh Relevance 12
    14. 14. Competitive Advantages 13 First Mover Proprietary ScraperProprietary Algorithms We are currently the only company to apply machine learning to political unstructured data. Prediction and optimized search/categorization of legislation. Current algorithm scrapes legislation across all 50 states in less than 200 ms (building top 10 cities in US) Proprietary NLP Models Competitive Industry Intuitive Design and Use First and only company to create legislative and government taxonomy of linguistic NLP models. Nothing technical or “mathy” – very easy to use for public policy makers. “Keeping Up with the Jones’” Phenomenon (Deloitte)
    15. 15. Our Vision 14 1. Expand Government Verticals 3. Open Data Platform2. Global Expansion Regulations, court cases, speeches, and procurement China, European Union, South America (all confirmed with Letters of Intent) Third party algorithms (IBM, Accenture, etc) and data visualization partnerships (Socrata, Palantir)
    16. 16. Ask $740,000 Convertible Debt 1. More Developers for NLP 2. Business Development to close 100 3. Move Office to DC 4. Test Assumptions
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