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Knowledgelevers

Presentation to Investors – December 2011

Unlocking Value in Data“The future belongs to the companies and people that turn data into products”

O’Reilly Radar Report

1. Mission2. Executive Summary3. Knowledgelevers4. Data Exchange 5. The Data Federation and Exchange Space6. Job To Be Done7. Knowledgelevers Tool Sets 8. IP Protection for Knowledge Levers and Derivative Applications I9. IP Protection for Knowledge Levers and Derivative Applications II10. Upside Potential11. Differentiators12. Staging Our Income Pyramid13. Facilitating Data Trading 14. Traders Need Tools15. Tools and Development Progress16. Strengths - Needs - Risks17. Our Founder18. Evolving The Team19. Exit Strategy20. Bottom Line and Summary Appendix

Mission

Disrupt enterprise data products through “just in time” notifications for CRM, Supply Chains, and Business Intelligence.

Copyright 2011 Compages

Data is the “oil” of the 21st century

Copyright © 2011 for Knowledgelevers.com 1

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.

Robust IP with supportive prototyping

Concept and technology validated by currently working installations

3.5 Million invested into software and IP

A multi-billion dollar opportunity


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

Effort to identify which data to buy or sell.

Need for actionable intelligence for risk assessment and

competitive advantage Resistance

Opportunity

Diverse market for buyers of data. Diverse producers of data who want to sell it.


Data Exchange

Data Federators and

Distributors Gallup, Gartner –

Distribute the right data to customers

Data Accumulators and AggregatorsCritical Research Enterprises - Cut losses

from useless research and liabilities from missed indicators.

Data Based Risk MitigatorsStock Fund Managers or Homeland Security

– Notify the right person as the dots get connected.

Data Creators and ProducersAll businesses,

especially retailers and

financial institutions – Sell

fallow data to buyers.

A Market in Search of a Trading Platform

Copyright © 2011 Knowledgelevers.com

Warehousing and Linking for

Specialized Data Exchange

Visualization and Computation

Data Transformation

Cloud Apps, Appliances,

Management and Storage

Business Intelligence

Suites

Consulting Odd Fellows Analytics, Extraction,

Collaboration

The Data Federation and Exchange Space

4

Customers or Potential

Competitors

Joint Venture Partners

Channel Partners Channel Partners or Competitors

OEM Outlets Sales Outlets Joint Sales

Nobody in the space has monetized automated chains of dataor triggered actions.

Node51

5

Job To Be Done

Be the global leader for brokering actionable data in real time.

Problem SolutionData exchange is constricted due to

No effective marketplace for offering or discovering data

No easy way to buy or sell

No easy way to determine a price

Multiple data formats

No standardized data updates

No standardized tools for triggering actions based on data

Software and infrastructure to

Post/offer and discover data to a central location

Establish standardized data exchange PRICING agreements

Provide a mechanisms for supply-side or demand-side pricing

Collect and federate data in real time or bypass federation

Enable updating and event triggering

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

Copyright © 2011 Knowledgelevers.com 6

Risk Reduction and CRM

Connection Tools

Calculation Tools

CombinationTools

CommunicationTools

Knowledgelevers Tool Sets


Big Picture: Patent methods and systems involving pricing and fees associated with data trading. Protect prices and fees for Gateways to Datasets1. Transmission from electronic devices like Smart Phones that offer GPS locations

and point of sale transactions2. Enrollment into data trading venues through data strings like Matrix Codes, RFID

tags, and direct to web services connections3. 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 Datasets4. Iterative additions to a dataset5. Alternate versions of a dataset 6. Immediate utility of the data format (Data Item Pair)

IP Protection for Knowledge Levers and Derivative Applications I


Protect prices and fees for Interaction with Datasets1. Setting up triggers to initiate server actions upon changes in a dataset2. Tracking interaction with a GUI associated with a dataset3. Linking enrollees (contributors) to data protocols and associated datasets4. Linking recipients of reports or server actions to data protocols and

associated datasets Protect prices and fees for assigning Value to a Data Item5. Popularity of the item 6. Reputation of the source for the data7. Importance of the item relative to other data items Protect prices and fees for Financial Transactions Involving Data8. Uploading data to a parent dataset9. Use of validation keys to connect contributors with financial institutions10.Enrollment of a new contributor or recipient into a data supply chain

IP Protection for Knowledge Levers and Derivative Applications II


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

Upside Potential


Differentiators

Competitors

Databases – “Big” data

Data Federation and Aggregation

Data Transformation and Analysis

Data Mining

Data Storage and Warehousing

Software Sales and Consulting Income

IT Departments - Centralized Management

Siloed by Organization or Function

Scheduled

Value Proposition is “Organized Data”

Knowledgelevers

Data Items – “Small” data

Data Chains, Streams, Combinations

Data Assessment for Actionable Value

Data Triggering and Notifications

Forward and Backward Redistribution

Transactional Income

Local End Users - Distributed (Individual Users)

Socially Networked

Real-time

Value Proposition is “Actionable Information”

We understand and can match our competitors capability and technology, but we are the first “transactional and actionable ” data firm – hence our name – Knowledgelevers.


SaaS Data Market

SOFTWARE -Direct to Researchers & Enterprise Risk

Managers

OEM LICENSES - for Data Distribution Businesses

SHAREWARE - Self-service Consumers - to set up exchanges, wrangle data, trigger actions and notifications

CURRENT CUSTOMERS - Expanded sales of upgraded Employee Performance and Risk Management Software to the public sector and

hospitals.

Staging Our Income Pyramid

Stage 4VC Capital

Stage 2Skip ifVC capital

Stage 3Skip if VCcapital

Stage 1Beta testingand validation

Stage 5

100,000 Buyers $80,000 per sale 15% Maintenance Continuous Income

10 Million Users for 200 Billion Data Points $.01 per field/3% transaction – Continuous income

500,000 Buyers $99 each

3,000 Licensees $50,000 per license

125,000 Buyers $25,000 per sale 15% Maintenance Continuous Income

Year 1 $268,000

Year 2 $4,000,000

Year 3 $32,000,000

Year 4 $246,000,000

Year 5 = Exit at $500,000,000 to

$800,000,000


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

Facilitating Data Trading


Traders Need Tools

Implement a data marketplace to automate uploading and downloading, pricing, payment, and action upon data in real time.

.

Enable fees and charges for exchange and payment process Device uploads and downloads

Payment and transaction toolsMembership fees, activation fees, convenience fees, subscription fees, volume discounts

Easily input pricing variables to enable fair compensation or reciprocity for data

Price per question and answer pair Price per field

Contributor reputation rating Popularity rating

Specific utility (rarity/recency/compatibility)

Automated actions

User friendly and secure applications to monetize data

Universally post and exchange data Security and authentication for data transport

Data is most valuable as and when it changes.


2006 Patent Application

● Research & Analysis● Business Model Created● Cost out Development Agenda

2009 Architect Prototype

● Recruit Developers● Fold in Legacy Software● Confirm Customer Need ● Establish Coding and Design

2010 Expand Patent Protection

● Monetize Weighting● Monetize Handshakes● Monetize Popularity and Recency● Monetize GUI● Embed Systems, Tools, and Methods into IP

2012 Prepare for Growth

● Complete Prototypes● Fold Legacy Applications together with Prototypes● Up-sell current customers● Secure Venture Capital/Partners● Expand Management Team● Further Design and Protect Methods for Data Pricing and Exchange

Tools and Development Progress


Strengths – Needs - Risks

Strengths

Ownership of IP - defensible competitive position

Design and implement flexible/modular software architecture

Unique database design with supporting code

Data administration capability and experience

Loyal customer base for current software - receptive to upgradingPassion for data and its potential to improve and change lives and reduce risks

Needs

Expand senior management team to drive growth

Sales and marketing skill and capacity

Financial backing to fund development

Cultivate strategic partnerships

Recruit and organize development team

Experience scaling

Risks Mitigation

Ownership of data not attributable – unclear data rights

Retain focus on High Risk Researchers and Risk Managers – grow through OEM rather than SaaS

Patents not enforceable or not issued First mover advantage


» software company automating survey research » survey research instrumentation

» Human Patterns - a psychometric tool which now has a network of over 200 Certified Administrators » applied in hundreds of businesses, universities, and organizations

» Ensera (acquired by ADS)» Applied Biosystems (developed the code to drive the equipment for the Human Genome Project)» Propellerhead Software (acquired through a chain of acquisitions by Symantec)» Alliance One (initiated and spun off alert® Food Safety Alert System)» Workplace Options (implemented “Network Advantage” support systems for EAP’s)

» real time data supply chain software company» 7 current installations doing performance evaluation and risk management

Multi-year consulting

engagements with startups involved in

data supply and research automation

Developed many psychometric and

survey instruments

Converted The Human Factor into

Human Patterns 1998

Converted Compages into the

Human Factor 1983

Founded Compages Limited 1980

» data driven systems » organization intervention consultation business

Our FounderStan Smith

Copyright © 2011 for Knowledgelevers.com

The Evolving Team

17

Person Role ExperienceTo Be Identified CEO

Adam Chasen Architecture Product Development

rPath Systems Automation

To Be Identified VP Sales and Marketing

Reed Altman COO, Implementation andTraining, Customer Relations, and Software Maintenance

Involved in first iteration of our data design and approach. Long term customer relationships on strengths of our technology and maintenance.

To Be Identified Exhibition Sales and Marketing

Joseph Tate Python DeveloperSaaS Developer

Developed patent for data form conversions


Exit Strategy

We can generate a valuation of >$500 million in 5 years

Sale to major enterprise software vendors

Multi-billion $ behemoths with

capacity and cash to buy

All improve position by offering a platform

to trade the world’s most ubiquitous

commodity! DATA


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."

First mover advantage with proof of concept implemented

Fundamentally changing the exchange and sale of data

Business model is highly efficient and scalable

Large market; recurring revenue stream

Defensible IP and team with deep domain expertise

Seeking $3-$5 M funding for management and development expansion


Thank you!

Contact:[email protected] land, 1-919-740-5010 mobile

mailto:[email protected]

Copyright © 2011 Knowledgelevers.com

APPENDIX“90 % of all data has been generated in the last 2 years”

IBM

1. The Size of Market2. IP to Revolutionize Data Trading3. Secret Sauce – New Technology4. Code and Architecture for Data Production and Consumption 5. Sales Divisions and Markets6. Budget Projection for First Year 7. The Easiest Customer - The Distributor8. Our Highest Margin Customer9. Everybody Pays to Play in Our Cloud10. Many Products – One Source Code


The Size of the Market

Non-CRO Researchers

Data Integrators

Risk Managers

Clinical Research

Consumers

0 20 40 60 80 100 120 14025

16

84

20

123

20

12

50

16

50

5

6

25

8

30

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 BillionOur Worst Case Estimate of our share of the total market = $25 Billion

Graph shows numbers assuming larger market.


Process Patent Number or Application Number

Defensive Value Offensive Value

Discovering Data 7,860,76012/930/280

High High

Building a User and Contributor Hierarchy 7,860,76012/932/798

Low High

Formulating an Exchange Agreement 7,860,76012/930/28013/134,596

Med Med

Assigning Data Access Rights and Roles 7,860,76012/932,79812/932,797

Low Med

Federating Data 7.860,76013/134,596

High High

Uploading Data from Devices, Message Services, RFID Tags and Transmitters

13/134,596New application not assigned a number

High High

Pricing Parsimonious Data 13/135,420 High Mod

Folding Data into Triggers 7,860,760 High High

Assigning Value 7,860,76012/932,79812/932,797

High High

Setting Chains or Loops for Server Actions 7,860,760 Low High

IP to Revolutionize Data Trading

Copyright © 2011 Compages Limited for Knowledgelevers.com

3

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 datasetAct 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


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 sourcesReal time forward and backward chaining of computer driven server events based upon calculated thresholds or valuesEncryption 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

Code and Architecture for Data Production and Consumption


BUSINESS DIVISION

MARKET AVERAGE SALE AVERAGE IMPLEMENTATION OR SERVICE COST

SALES METHOD

COST PER SALE

RECURRING INCOME

Employee Performance and Risk Management

Public sector (Law Enforcement) and hospitals

$25,000 $6,000 Conferences and Exhibitions

$3,000 15%

Shareware Sales – if VC funding not obtained

Web Users $99 $2 SEO and Shareware Outlets

$3.50

Joint Ventures with Niche Data Federators

Patent enforcement FUD and cooperative alliances

Unknown Unknown Patent Infringement Attorney

$0 Potentially

OEM Licenses Data Vendors and Buyers

$50,000 $6000 Direct Sales $3000 Variable

Software Hooking into Enterprise Software

Risk Managers $80,000 $3000 Direct Sales $3000 Variable

SaaS Anyone Variable $1 Subscription $1 Variable

Sales Divisions and Markets


Business Unit Employee Allocation Employee Cost Contractors for Rapid Ramp Up to Stage 5

Expenses Sales Income

Administration – Architecture-Investor Relationships

.7 Founder

.4 CEO

.1 Sales and Marketing Manager

.3 Software Architect

$126,000$72,000$12,000

$60,000

Infrastructure $16,000Office and Phone$8,000

Employee Performance and Risk Management

.3 Sales and Marketing Manager

ExhibitorDemonstration /Closer.5 Implementation Staff

$40,000

$65,000$85,000

5 .NET Developers $300,000

Travel $15,000Conferences $40,000

$100,000

Shareware Sales .5 Web Developer/Master.1 Implementation Staff

$45,000

$8,000

6 Python Developers $360,000

Expenses $4,500 $88,000

Joint Ventures with Niche Data Federators

.2 CEO

.3 Sales and Marketing Manager.2 Founder.4 Developer

$36,000$40,000

$36,000$40,000

Travel $15,000 $50,00

OEM Licenses .2 CEO.2 Founder.4 Developer

$36,000$36,000$40,000

Travel $15,000 $80,000

Software Hooking into Enterprise Software

.2 CEO

.3 Sales and Marketing Manager.1 Founder.4 Developer

$36,000$40,000

$18,000$40,000

5 Enterprise Developers $300,000

Expenses $15000

SaaS .5 Web Developer/Master.4 Implementation Staff

$45,000

$32,000

9 SaaS Developers $540,000

Expenses $4000

TOTALS $1,028,000 $1,500,000 $147,500 $268,000

Budget Projection for First Year


The Easiest Customer to Capture – The Distributor

Consultation and Integration Into the OEM’s

Database

Data Federation Utility

Data Contribu

torUtility

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.

Data Download

Utility

Income from OEM Licenses –Include our basic software with their offering


User Hierarchies (LDAP) Utility

Consultation and Integration Into the Risk

Mitigation Database

Data Federation Utility

Internal Contributor Utility

Our Highest Margin Customer – The Risk Mitigator (Medical and Pharma Research – Homeland Security)

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.”

Data Download

Utility

Income from straight software sale of our second stage software


Everybody Pays to Play in Our Cloud

Number of server actions triggered

Handshake between data

creators and data federators

Number of search transactions

Relative weight of the sources

of the data

Relative value of the data

field

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.

Data Contribu

tor Utility

Banking Utility

VPN Utility

Data Entry Utility

Search Utility

Software as a Service Income – 3% of the price from the seller of the data.


When a trigger gets tripped the following should occur in real time:1. A Facebook Page post onto a “Food Safety” page should be

generated2. A Twitter from @FoodSafety should be sent3. The National Food Safety Website should be updated with an alert4. An email blast should go to all members of the food product’s

supply chain5. An SMS message should go to all members of the food product’s

supply chain6. 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 .

Many Products – One Source Code Example - Food Security


End of appendix

Thank You

Contact:[email protected] land, 1-919-740-5010 mobile

mailto:[email protected]

Download - Knowledgelevers expanded

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