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. 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. 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. 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. The Future of News 5
1850 1960 20102000
Local Newspapers Television Social Media Machine Learning
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. Market Size 7
$30 Billion
$10 Billion
Total Spending on Policy Analysis
Total Spending on Newspaper Subscriptions
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. 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. 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
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. 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)
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)
