Evolving a data supply chain and disrupting the Google model of ignoring data ownership and the Facebook model of co-opting data ownership. The data supply chain model assumes the person or the owner of the device that creates data is the owner of that data and should have the right to trade in in an open marketplace.

1. Presentation to Investors – December 2011

2. Unlocking Value in Data
“The future belongs to the companies and people that turn data into products”
O’Reilly Radar Report
1. Mission
2. Executive Summary
3. Knowledgelevers
4. Data Exchange
5. The Data Federation and Exchange Space
6. Job To Be Done
7. Knowledgelevers Tool Sets
8. IP Protection for Knowledge Levers and Derivative Applications I
9. IP Protection for Knowledge Levers and Derivative Applications II
10. Upside Potential
11. Differentiators
12. Staging Our Income Pyramid
13. Facilitating Data Trading
14. Traders Need Tools
15. Tools and Development Progress
16. Strengths - Needs - Risks
17. Our Founder
18. Evolving The Team
19. Exit Strategy
20. Bottom Line and Summary
Appendix

3. Mission
Disrupt enterprise data products through “just in time”
notifications for CRM, Supply Chains, and Business Intelligence.
Data is the “oil” of the 21st century
Copyright 2011 Compages

4. Executive Summary
Unlocking value in data through enabling a new market — a hybrid
between what did for used goods, did for retailers
and for the music industry.
We will implement and protect methods and systems to collect fees
for enabling data to be traded and operated upon in real time.
A multi-billion dollar opportunity
3.5 Million invested into software and IP
Concept and technology validated by currently working installations
Robust IP with supportive prototyping
Copyright © 2011 for Knowledgelevers.com 1

5. Experienced in data management
Deep understanding of problems faced by researchers and risk managers
Projected valuation takes us to $500 million in 2016
Multiple sales and growth channels – Broad market
Diverse market for buyers of data. Diverse producers of data
who want to sell it.
Effort to identify which
data to buy or sell.
Need for actionable intelligence
for risk assessment and
competitive advantage
Resistance
Opportunity
Copyright © 2011 for Knowledgelevers.com 2

6. Data Exchange
Data Federators and Distributors Data Accumulators and Aggregators
Critical Research Enterprises - Cut losses
Gallup, Gartner – Distribute the right data
from useless research and liabilities from
to customers missed indicators.
A Market in Search
of a Trading
Platform
Data Based Risk Mitigators Data Creators and Producers
Stock Fund Managers or Homeland Security All businesses, especially retailers and
– Notify the right person as the dots get financial institutions – Sell fallow data to
connected. buyers.
Copyright © 2011 for Knowledgelevers.com 3

7. The Data Federation and Exchange Space
Node51
Warehousing Visualization Data Cloud Apps, Business Consulting Odd Fellows
and Linking for and Transformation Appliances, Intelligence Analytics,
Specialized Computation Management Suites Extraction,
Data Exchange and Storage Collaboration
Customers or Joint Venture Channel Channel OEM Outlets Sales Outlets Joint Sales
Potential Partners Partners Partners or
Competitors Competitors
Nobody in the space has monetized
automated chains of data
or triggered actions.
Copyright © 2011 Knowledgelevers.com 4

8. Job To Be Done
Be the global leader for brokering actionable data in real time.
Problem Solution
Data exchange is constricted due to Software and infrastructure to
No effective marketplace for offering or discovering data Post/offer and discover data to a central location
No easy way to buy or sell Establish standardized data exchange PRICING agreements
No easy way to determine a price Provide a mechanisms for supply-side or demand-side pricing
Multiple data formats Collect and federate data in real time or bypass federation
No standardized data updates Enable updating and event triggering
No standardized tools for triggering actions based on data Provide a self-service interface for simple data sharing
Every Internet User - a Data Trader
Every Business - a Data Vendor or Consumer
Every Employee or Researcher – a Data Creator
5

9. Knowledgelevers Tool Sets
Connection
Tools
Risk
Communication Calculation
Reduction Tools
Tools
and CRM
Combination
Tools
Copyright © 2011 Knowledgelevers.com 6

10. IP Protection for
Knowledge Levers and Derivative Applications I
Big Picture: Patent methods and systems involving pricing and fees
associated with data trading.
Protect prices and fees for Gateways to Datasets
1. Transmission from electronic devices like Smart Phones that offer GPS locations
and point of sale transactions
2. Enrollment into data trading venues through data strings like Matrix Codes, RFID
tags, and direct to web services connections
3. Transmission to or from social networking sites like Facebook and Twitter in the
event the Supreme Court determines ownership to be by the producer of the
data or the owner of the device originating the data
Protect prices and fees for Improvement of Datasets
1. Iterative additions to a dataset
2. Alternate versions of a dataset
3. Immediate utility of the data format (Data Item Pair)
Copyright © 2011 Knowledgelevers.com 7

11. IP Protection for
Knowledge Levers and Derivative Applications II
Protect prices and fees for Interaction with Datasets
1. Setting up triggers to initiate server actions upon changes in a dataset
2. Tracking interaction with a GUI associated with a dataset
3. Linking enrollees (contributors) to data protocols and associated datasets
4. Linking recipients of reports or server actions to data protocols and
associated datasets
Protect prices and fees for assigning Value to a Data Item
1. Popularity of the item
2. Reputation of the source for the data
3. Importance of the item relative to other data items
Protect prices and fees for Financial Transactions Involving Data
1. Uploading data to a parent dataset
2. Use of validation keys to connect contributors with financial institutions
3. Enrollment of a new contributor or recipient into a data supply chain
Copyright © 2011 Knowledgelevers.com 8

12. Upside Potential
As data strings or matrix codes are used for rapid
enrollment into social media sites
As data strings or matrix codes are expanded into
enrollment of consumers for feedback and risk
management
If ownership of data generated upon or within an
electronic device resides with the owner of the device
If user expectations shift from analytics or statistics to
actionable intelligence
Copyright © 2011 Knowledgelevers.com 9

13. Differentiators
We understand and can match our competitors capability and technology, but we are the
first “transactional and actionable ” data firm – hence our name – Knowledgelevers.
Competitors Knowledgelevers
Databases – “Big” data Data Items – “Small” data
Data Federation and Aggregation Data Chains, Streams, Combinations
Data Transformation and Analysis Data Assessment for Actionable Value
Data Mining Data Triggering and Notifications
Data Storage and Warehousing Forward and Backward Redistribution
Software Sales and Consulting Income Transactional Income
IT Departments - Centralized Management Local End Users - Distributed (Individual Users)
Siloed by Organization or Function Socially Networked
Scheduled Real-time
Value Proposition is “Organized Data” Value Proposition is “Actionable Information”
Copyright © 2011 Knowledgelevers.com 10

14. Staging Our Income Pyramid
10 Million Users for 200 Billion Data Points
Stage 5 SaaS
$.01 per field/3% transaction – Continuous income
Data Market
100,000 Buyers
Stage 4 SOFTWARE -Direct to
Researchers & $80,000 per sale
VC Capital Enterprise Risk 15% Maintenance
Managers Continuous Income
Stage 3 3,000 Licensees
Skip if VC OEM LICENSES - for Data Distribution
Businesses $50,000 per license
capital
Stage 2 500,000 Buyers
SHAREWARE - Self-service Consumers - to set up
Skip if exchanges, wrangle data, trigger actions and notifications
$99 each
VC capital
125,000 Buyers
Stage 1 CURRENT CUSTOMERS - Expanded sales of upgraded Employee $25,000 per sale
Beta testing Performance and Risk Management Software to the public sector and
and validation hospitals. 15% Maintenance
Continuous Income
Year 5 = Exit at
Year 1 Year 2 Year 3 Year 4
$500,000,000 to
$268,000 $4,000,000 $32,000,000 $246,000,000 $800,000,000
Copyright © 2011 Knowledgelevers.com 11

15. Facilitating Data Trading
Access to multiple data types and owners:
Tables, spreadsheets, and distributed databases
Ability to drill down or roll up for federation or subsets:
Aggregating by the data item, the data item pair, the data stream, or the dataset.
Ease collecting from multiple devices, messaging services, observers, and consumers:
Track changes, create and audit data
Flexibility in MONETIZING AND SETTING VALUE:
Rarity, reputability, integrity, usability, compatibility, popularity, recency, format friendly
Streaming:
Ongoing real-time or scheduled data updates
Setting THRESHOLDS AND TRIGGERS FOR ACTIONS:
Notification and/or other automated actions based on schedule and/or new or changed data
Copyright © 2011 for Knowledgelevers.com 12

16. Traders Need Tools
Implement a data marketplace to automate uploading and downloading,
pricing, payment, and action upon data in real time.
User friendly and secure applications to monetize data
Universally post and exchange data Security and authentication for data transport
Easily input pricing variables to enable fair compensation or reciprocity for data
Price per question Contributor Specific utility
and answer pair Price per field Popularity rating (rarity/recency/ Automated actions
reputation rating compatibility)
Enable fees and charges for exchange and payment process
Device uploads and downloads
Payment and transaction tools
Membership fees, activation fees, convenience fees, subscription fees, volume discounts
.
Data is most valuable as and when it changes.
Copyright © 2011 for Knowledgelevers.com 13

17. Tools and Development Progress
2012
2010 Prepare for Growth
Expand Patent
Protection ● Complete Prototypes
2009 ● Fold Legacy Applications
● Monetize Weighting together with Prototypes
Architect Prototype
● Monetize Handshakes ● Up-sell current customers
● Recruit Developers ● Monetize Popularity and ● Secure Venture Capital/Partners
● Fold in Legacy Software Recency
● Expand Management Team
● Confirm Customer Need ● Monetize GUI
2006 ● Further Design and Protect
● Establish Coding and Design ● Embed Systems, Tools, and
Patent Application Methods into IP
Methods for Data Pricing and
Exchange
● Research & Analysis
● Business Model Created
● Cost out Development
Agenda
Copyright © 2011 Knowledgelevers.com 14

18. Strengths – Needs - Risks
Strengths Needs
Ownership of IP - defensible competitive position Expand senior management team to drive growth
Design and implement flexible/modular software Sales and marketing skill and capacity
architecture
Unique database design with supporting code Financial backing to fund development
Data administration capability and experience Cultivate strategic partnerships
Loyal customer base for current software - Recruit and organize development team
receptive to upgrading
Passion for data and its potential to improve and Experience scaling
change lives and reduce risks
Risks Mitigation
Ownership of data not attributable – unclear data Retain focus on High Risk Researchers and Risk
rights Managers – grow through OEM rather than SaaS
Patents not enforceable or not issued First mover advantage
Copyright © 2011 Knowledgelevers.com 15

19. Our Founder
Stan Smith
Founded Compages » data driven systems
Limited 1980 » organization intervention consultation business
Converted » software company automating survey research
Compages into the
Human Factor 1983 » survey research instrumentation
Converted The
Human Factor into » real time data supply chain software company
Human Patterns » 7 current installations doing performance evaluation and risk management
1998
Developed many
psychometric and » Human Patterns - a psychometric tool which now has a network of over 200 Certified Administrators
survey instruments » applied in hundreds of businesses, universities, and organizations
Multi-year » Ensera (acquired by ADS)
consulting » Applied Biosystems (developed the code to drive the equipment for the Human Genome Project)
engagements with » Propellerhead Software (acquired through a chain of acquisitions by Symantec)
startups involved in
data supply and » Alliance One (initiated and spun off alert® Food Safety Alert System)
research automation » Workplace Options (implemented “Network Advantage” support systems for EAP’s)
Copyright © 2011 Knowledgelevers.com 16

20. The Evolving Team
Person Role Experience
To Be Identified CEO
Adam Chasen Architecture rPath Systems Automation
Product Development
To Be Identified VP Sales and Marketing
Reed Altman COO, Implementation and Involved in first iteration of our data design and
Training, Customer Relations, approach. Long term customer relationships on
and Software Maintenance strengths of our technology and maintenance.
To Be Identified Exhibition Sales and Marketing
Joseph Tate Python Developer Developed patent for data form conversions
SaaS Developer
Copyright © 2011 for Knowledgelevers.com
17

21. Exit Strategy
We can generate a valuation of >$500 million in 5 years
Sale to major All improve position
Multi-billion $
enterprise behemoths with
by offering a platform
to trade the world’s
software capacity and cash to
most ubiquitous
buy
commodity! DATA
vendors
Copyright © 2011 for Knowledgelevers.com 18

22. Bottom Line and Summary
KnowledgeLevers is a global data exchange company enabling data
producers and consumers to price and trade actionable data instead
of leaving it dormant in enterprise databases or siloed on local
systems.
"Everything should
be made as simple
as possible, but not
simpler."
Copyright © 2011 for Knowledgelevers.com 19

23. Thank you!
Contact:
Stan@humanpatterns.com
1-803-792-0103 land, 1-919-740-5010 mobile
Copyright © 2011 Knowledgelevers.com 20

24. APPENDIX
“90 % of all data has been generated in the last 2 years”
IBM
1. The Size of Market
2. IP to Revolutionize Data Trading
3. Secret Sauce – New Technology
4. Code and Architecture for Data Production and Consumption
5. Sales Divisions and Markets
6. Budget Projection for First Year
7. The Easiest Customer - The Distributor
8. Our Highest Margin Customer
9. Everybody Pays to Play in Our Cloud
10. Many Products – One Source Code
Copyright © 2011 Knowledgelevers.com

25. The Size of the Market
30
Consumers 50
123
8
Clinical Research 16
20
25
Risk Managers 50
84
6
Data Integrators 12
16
5
Non-CRO Researchers 20
25
0 20 40 60 80 100 120 140
Worst Case Best Case Total Market Billions
Total Market Size is between 100-268 Billion
Our Best Case Estimate of our share of the total market = $148 Billion
Our Worst Case Estimate of our share of the total market = $25 Billion
Graph shows numbers assuming larger market.
Copyright © 2011 Knowledgelevers.com 1

26. IP to Revolutionize Data Trading
Process Patent Number or Defensive Value Offensive Value
Application Number
Discovering Data 7,860,760 High High
12/930/280
Building a User and Contributor Hierarchy 7,860,760 Low High
12/932/798
Formulating an Exchange Agreement 7,860,760 Med Med
12/930/280
13/134,596
Assigning Data Access Rights and Roles 7,860,760 Low Med
12/932,798
12/932,797
Federating Data 7.860,760 High High
13/134,596
Uploading Data from Devices, Message Services, RFID 13/134,596 High High
Tags and Transmitters New application not
assigned a number
Pricing Parsimonious Data 13/135,420 High Mod
Folding Data into Triggers 7,860,760 High High
Assigning Value 7,860,760 High High
12/932,798
12/932,797
Setting Chains or Loops for Server Actions 7,860,760 Low High
Copyright © 2011 for Knowledgelevers.com 2

27. Secret Sauce – New Technology
Exchanging data across any electronic device or tag (RFID) or messaging
system (Twitter - IM)
Bypass need to federate datasets – link and post by the item, stream, or
dataset
Act upon data in real time with forward and backward chaining
Easy GUI for building triggers for actions upon data
Variable pricing of data items, data streams, and datasets
Automated payment implementation per transaction
Optional implementation of Data Item Pairs (question with answers) for
researchers
Copyright © 2011 Compages Limited for
3
Knowledgelevers.com

28. Code and Architecture for Data Production and Consumption
A simple calculator-like GUI for building triggers for server actions
A simple GUI to import entire enterprise-wide participant hierarchies
A rigorous build and versioning method for research protocols
A simple GUI to configure and implement authentication and rights schemas for levels
of users across a network of data owners and contributors
Real time routing of specific data points with specific context
Real time distribution of notifications, updates, views, dashboard postings and
updating of data sources
Real time forward and backward chaining of computer driven server events based
upon calculated thresholds or values
Encryption and parsimonious storage at the bit level of observations entered into
research protocols
“Handshake” initiation based on search term results
Background calculation of the pricing formula
Linkage to Search engines, VPNs, and financial institutions
Copyright © 2011 for Knowledgelevers.com 4

29. Sales Divisions and Markets
BUSINESS MARKET AVERAGE SALE AVERAGE SALES COST PER RECURRING
DIVISION IMPLEMENTATION METHOD SALE INCOME
OR SERVICE COST
Employee Public sector $25,000 $6,000 Conferences $3,000 15%
Performance (Law and
and Risk Enforcement) Exhibitions
Management and hospitals
Shareware Web Users $99 $2 SEO and $3.50
Sales – if VC Shareware
funding not Outlets
obtained
Joint Ventures Patent Unknown Unknown Patent $0 Potentially
with Niche enforcement Infringement
Data FUD and Attorney
Federators cooperative
alliances
OEM Licenses Data Vendors $50,000 $6000 Direct Sales $3000 Variable
and Buyers
Software Risk Managers $80,000 $3000 Direct Sales $3000 Variable
Hooking into
Enterprise
Software
SaaS Anyone Variable $1 Subscription $1 Variable
Copyright © 2011 Knowledgelevers.com 5

30. Budget Projection for First Year
Business Unit Employee Allocation Employee Cost Contractors for Rapid Expenses Sales Income
Ramp Up to Stage 5
Administration – .7 Founder $126,000 Infrastructure $16,000
Architecture-Investor .4 CEO $72,000 Office and Phone
Relationships .1 Sales and Marketing $12,000 $8,000
Manager
.3 Software Architect $60,000
Employee Performance .3 Sales and Marketing $40,000 5 .NET Developers Travel $15,000 $100,000
and Risk Management Manager $300,000 Conferences $40,000
Exhibitor
Demonstration /Closer $65,000
.5 Implementation Staff $85,000
Shareware Sales .5 Web $45,000 6 Python Developers Expenses $4,500 $88,000
Developer/Master $360,000
.1 Implementation Staff $8,000
Joint Ventures with .2 CEO $36,000 Travel $15,000 $50,00
Niche Data Federators .3 Sales and Marketing $40,000
Manager
.2 Founder $36,000
.4 Developer $40,000
OEM Licenses .2 CEO $36,000 Travel $15,000 $80,000
.2 Founder $36,000
.4 Developer $40,000
Software Hooking into .2 CEO $36,000 5 Enterprise Developers Expenses $15000
Enterprise Software .3 Sales and Marketing $40,000 $300,000
Manager
.1 Founder $18,000
.4 Developer $40,000
SaaS .5 Web $45,000 9 SaaS Developers Expenses $4000
Developer/Master $540,000
.4 Implementation Staff $32,000
TOTALS $1,028,000 $1,500,000 $147,500 $268,000
Copyright © 2011 Knowledgelevers.com 6

31. The Easiest Customer to Capture – The
Income from OEM
Distributor
Licenses –Include
Consultation and Integration
our basic software Into the OEM’s Database
with their offering
User Hierarchies (LDAP)
Utility
Trigger Building Utility
Data Wrangling Utility
Data Federation
Utility
Data Data
Contributor Download
Utility Utility
The premise of OEM and Data Distributor pricing is that OEMs and Distributors fold our
“Utilities” into their offerings to enable consumers to pull triggered real time notifications
from the database and/or for data contributors to push data to federated databases.
Copyright © 2011 for Knowledgelevers.com 7

32. Our Highest Margin Customer – The Risk Mitigator
(Medical and Pharma Research – Homeland Security)
Income from Consultation and Integration
Into the Risk Mitigation
Database
straight software
sale of our second Notification Hierarchies
User Hierarchies (LDAP)
(LDAP) Utility
stage software Utility
Trigger Building Utility
Data Wrangling Utility
Data Federation
Utility
Internal Data
Blind Contributor Download
Contributor Utility
Utility Utility
The premise of Risk Mitigation Pricing is that the price includes “Utilities” to enable the Risk Mitigator to
configure and push secure triggered notifications in real time to users who may not be contributors and
for contributors to push data to the federated database “blind.”
Copyright © 2011 for Knowledgelevers.com 8

33. Everybody Pays to Play in Our Cloud
Software as a Service Income – 3% of the price from the seller of the data.
Number of search
Number of server actions Search transactions
triggered Utility
Number of VPN
Number of users with transactions
rights to server actions
VPN
Number of fields Utility Number of banking
included in triggered transactions
server actions
Number of data Number of users
sources involved in receiving
Banking notifications
exchange Utility
Relative weight
of the sources of
Handshake the data
between data Data
creators and data Contributor Relative
federators Utility value of
the data
field
Data Entry
Utility
Basic Pricing Incremental Value Pricing
Knowledgelevers operates as the data distributor for the Cloud using the tools of the OEM and the
Risk Mitigator. The premise of the “Cloud” is that data creators and data federators pay only for
actual use of the resource and that fees are configurable, incremental, and transparent.
Copyright © 2011 Knowledgelevers.com 9

34. Many Products – One Source Code
Example - Food Security
When a trigger gets tripped the following should occur in real time:
1. A Facebook Page post onto a “Food Safety” page should be
generated
2. A Twitter from @FoodSafety should be sent
3. The National Food Safety Website should be updated with an alert
4. An email blast should go to all members of the food product’s
supply chain
5. An SMS message should go to all members of the food product’s
supply chain
6. SMS and Email alerts should also be sent to all Public Health
agencies and EMS units
As the FDA or CDC becomes aware of a risk, the management of the risk is
automated .
Copyright © 2011 Knowledgelevers.com 34

35. End of appendix
Thank You
Contact:
Stan@humanpatterns.com
1-803-792-0103 land, 1-919-740-5010 mobile
Copyright © 2011 Knowledgelevers.com 35