by Eben Esterhuizen
0.0 stars - 0 reviews
range from 0 to 5
1. Opening Page Quote
WSJ quote from Relevant
Talk about previous wave being about content
filtering, next wave will be about turning all this
data into predictive algorithms...
2. What we do
From Relevant: We aggregate and analyze millions of bits of data in real time from thousands of sources, and then
parse it through our analytics platform to deliver answers that will enable our users to make more informed
decisions. This process will revolutionize the way they do business.
Simply put, we will build significant value for our investors by commercializing the predictability power of
information and discussions on both the internet and from all other sourced material. Our algorithms determine
what information is most relevant to our users for any given application, and thus we are able to provide product
offerings that address targeted audiences who can profit from the information.
We essentially provide actionable insights based on real-time analysis of all available relevant information from a
targeted internet universe.
Contextuall was founded on the belief that people’s online activities are a leading indicator of what they plan to do in the future.
- Before you buy a car, you search for the best deals online
- If you just lost your job, you tweet about it
- If you’re visiting your local shopping mall, you “check in” with your mobile phone
- When you’re reading financial media, you might be influenced by the bullish/bearish tone of the content
- And so on…
Contextuall uses cutting-edge machine learning and artificial intelligence technologies to transform these unstructured data points into useful
insights for portfolio managers and investors…
Put simply, it is a prediction engine.
3. The opportunity
Companies want to make predictions about the world around them…are people more / less likely to buy?
Potential clients: Ad agencies, investors, political consultants, fantasy sports, government agencies
6. Beyond first product
Utlimate goal is total customization…where
users can select / upload their own data, and
create a prediction dashboard…
Democratize machine learning
Expanding our team, adding to our brain trust
(advisors, board members etc,)
Rapid prototyping: At the point that you know your product's future isn't going to be
remarkable, it's time to change route immediately. Why? Because unless people are going to
be fascinated by it, they're not going to talk about it, so investing in this "dying product" isn't
4. Competitive Landscape
Why we are better / different
we mass produce a wide variety of algorithms, instead of relying too
heavily on a single indicator…this gives us the opportunity to
attached confidence levels to our predictions…other firms never
really diversify their efforts…
Accuracy rates, how we avoid statistical overfitting
Differentiate itself based upon the usability and simplicity of its interface. The company believes
that users will favor clarity and suggested action over the more traditional approach of data pass
Sheer scope and number of algorithms we can produce will blow away competitors...
The kind of data sets we're crunching--its not the algorithms that give us an edge, its the way we use data
5. Use of Proceeds
What we've spent so far, and what it got us
- Automated web scraping tool, used to collect data from a wide
variety of sources, including Twitter, FourSquare,
- Automated content
- Automated machine learning and artificial intelligence algorithms
Marketing and Revenue Plan
Content automation technologies to increase the amount of syndicated content / search indexing etc.