CORPORATE OVERVIEW JANUARY 2018

Proprietary & Confidential

OPERATIONAL QUALITY FOR A MOBILE WORLD

AppliedEA increases the availability rates and reduces the high operational costs of Mobile Machines by providing a real time solution that continuously

monitors, analyzes and benchmarks the performance of these systems

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MOBILE MACHINES IS A FAST GROWING MARKET

The global Mobile Machines sector is large and growing exponentially

Mobile Machines include manned and autonomous aircraft, ground vehicles, robots, space craft, heavy equipment, industrial and mining

apparatus, trains and light rail, and maritime vessels

>$500BN 2016 market size

>15% CAGR

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MAINTENANCE IS THE MOST SIGNIFICANT COST CENTER

Maintenance is the largest and most important budget item associated with Mobile Machine ownership and operation

~50% of overall operational budget

200% of original Mobile Machine acquisition cost

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Despite such heavy investments, failure rates of Mobile Machines run at 10-60%

THE OPPORTUNITY

AppliedEA monitors and analyzes critical performance benchmarks of Mobile Machines in order to

▸ Enhance platform availability

▸ Reduce failure rates

▸ Minimize operational costs

▸ Increase maintenance effectiveness

▸ Facilitate efficient parts replacement

▸ Maximize performance

▸ Meet quality control and regulatory standards

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ABOUT US

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Gen. Mike Hayden Dr. Bill Schneider Matthew Freedman Dr. Benjamin Mann

Director of the CIA. First Principal Deputy Director of National Intelligence. Director of the NSA. Four star general at USAF. Commander of the Air Intelligence Agency. Director of the Joint Command and Control Warfare Center. Director of Motorola Solutions. Distinguished Visiting Professor at Oxford University.

U.S. Under Secretary of State. Chairman of the Defense Science Board and the Defense Business Board. Director of General Atomics, BAE Systems USA, EADS North America, ABB Susa, MBDA USA, and Selex ES USA. Advisor to the U.S. Departments of Defense, Energy, and State. Advisor to Kurion-Veolia and DSI.

Advisor to U.S. Pacific Fleet, Defense Intelligence Agency, U.S. Special Operations Command, Department of State, Department of Defense, Department of the Navy, National Security Council, and Office of Management and Budget. White House Transition Director reporting to Secretary of State Colin Powell.

Inventor of Topological Data Analysis. VP at Ayasdi. Program Manager, Senior Scientist and Acting Deputy Office Director at DARPA. Program Officer at National Science Foundation. Faculty member at Harvard University, Clarkson University, and the University of New Mexico.

Dr. Paul Kaminski Dr. Tony Tether Dr. Rick Lawrence

U.S. Under Secretary of Defense. Chairman of RAND Corporation, the Defense Science Board, and Seagate Government Solutions. Director of General Dynamics, The Mitre Corporation, Bay Microsystems, CoVant Technologies, and Johns Hopkins Applied Physics Lab. Advisor to the MIT Lincoln Laboratory.

Director of DARPA. Director of the National Intelligence Office. Vice President of Science Applications International Corporation’s (SAIC) Advanced Technology Group. Director of Aurora Flight Sciences (acquired by Boeing) and Strobe (acquired by GM). Member of the Army, Navy and Defense Science Boards.

Head of Machine Learning and Decision Analytics at IBM Watson. Distinguished Research Staff Member at IBM. Head of the Neutronics Methods Group at the Argonne National Laboratory. Recipient of the 2014 INFORMS Innovative Applications in Analytics Award.

Josh SegalCEO & Founder

First employee and VP at Varonis Systems (Nasdaq: VRNS). Venture capital experience at Exigen Capital, Applied Materials Ventures, WR Hambrecht, and Global Catalyst Partners. Investor in P-Cube (acquired), M-Stream (acquired), Grandis (acquired), and Infinera (IPO). Combat service at Israel Defense Forces.

Gafar LawalCOO

Managing Director, CTO & Chief Architect, Morgan Stanley. Partner Architect, Microsoft. Chief Technology Architect, Merrill Lynch. Awarded two patents.

Senior Management Board of Advisors

Patrick LongDirector of Innovation

SVP Aviation Programs at 4M Research. Lean Six Sigma Project Officer at US Army. Maintenance Information Technology Officer at US Army. Special Operations maintenance test pilot at US Army. Grey Eagle Unmanned Aerial System Officer at US Army.

THE MAINTENANCE CHALLENGE

OPERATIONALREADINESS

LOGISTICS STRATEGICPLANNING

WHAT CAN STAKEHOLDERS DO TO IMPROVE OVERALL FLEET

AVAILABILITY AND COST CONTAINMENT NEXT YEAR?

ENTERPRISE, OEM, SERVICE PROVIDER

MAINTENANCE &SAFETY

SERVICE PROVIDER, INSURANCE UNDERWRITER,

PILOT/OPERATOR/DRIVER

IS MY MOBILE MACHINE READY NOW? IS IT SAFE TO OPERATE?

ARE PROCESSES IMPACTFUL AND RAPID?

HOW MANY MOBILE MACHINES ARE PREPARED FOR OPERATIONS

TODAY? THIS WEEK?

OPERATIONS MANAGER

HOW MANY PARTS DO WE NEED NEXT WEEK? NEXT MONTH?

MAINTENANCE MANAGER, INVENTORY MANAGER

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Affordability An acquisition issue

Mission Achievement An operations and logistics issue

Liability Exposure A financial issue

Customer Retention A business issue

THE REALITY – DEFICIENT PERFORMANCE

Maintenance concerns fundamentally undermine

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CASE STUDY – UAV

▸ Since 2008, US Customs and Border Protection has acquired 11 UAVs

▸ Total Cost of Ownership

• Purchase cost – $150 million

• 40% of the acquisition price to operate the aircraft ($62 million per year/$460 million over 5 years)

▸ 2 UAVs have crashed and need to be replaced

• Significant downtime

• Scope of operations was dramatically reduced (30% of initial objective)

• Unrecognized operating costs, planning/supply chain failures, no performance benchmarking

▸ Due to overheads and operational failures, recommended to shut the program

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A NEW APPROACH

AppliedEA represents the first opportunity for enterprises employing Mobile Machines to

innovate maintenance efforts beyond pen & paper, visualization tools, or human-generated

analysis

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CURRENT MAINTENANCE METHODOLOGIES

▸ Scheduled and Preventive

• Programmed on a time or usage trigger

▸ Predictive

• Model-based, looks at MTBF per component, defines a causality-driven model, and deduces what are the subsystem and system MTBF impacts

▸ Vibration Analysis

• Model-based, looks at single factor/component and identifies when it breaches operational parameters

▸ Unscheduled

• Activated by operator reports or other diagnostics

These approaches are expensive – but still ineffective in meeting maintenance benchmarks

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APPLIEDEA’S INNOVATIVE CONDITION BASED MAINTENANCE

A Mobile Machine executing the same maneuver under identical conditions should record exactly a similar performance profile each operation

AppliedEA analytically profiles a Mobile Machine’s normal performance and then compares its previous and current operations to identify sudden or progressive change

By contextualizing performance with external, operational and repair data, impending failures are accurately identified and alerted

As more data is logged, AppliedEA’s diagnostic, predictive and prescriptive results improve in accuracy

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OUR SOLUTION

The AppliedEA Condition Based Maintenance solution dictates that corrective actions should only be performed when certain indicators show signs of decreasing performance, continuous operation outside the normal operational

parameters, or upcoming failure.

AppliedEA increases Mobile Machine availability rates by providing a real-time solution that continuously monitors and analyzes these systems.

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END-TO-END VALUE PROPOSITION

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Enterprise Functions

▸ Engineering

▸ Program Management

▸ Aftermarket

▸ Supply Chain

▸ Maintenance

▸ Inventory

▸ Security & Cyber Hardening

▸ Fleet & Asset Management

▸ Fleet Operations

▸ Risk Management

BusinessIntelligence

Data mining and visualization software that reveals trends and

useful information.

DrivingEfficiency

Gains

CloudInfrastructure

Virtualized, on-demand resources with infinitely extensible

processing, bandwidth and storage.

AnalyticsAutomated analytics integrated into workflow that unlock data value and improve profitability around failure prediction and

optimized maintenance.

Data Pooling and Query Platforms

Connect data & create structure by merging, conditioning streams

and archived data.

OUR DIFFERENTIATORS

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Rapid and Easy Deployment

Can work on any type of Mobile MachineSuitable for both new and legacy systems

No hardware, no need to modify the Mobile Machine, no certifications requiredCloud or on-premise implementation

No need to reskill or hire laborSupplements existing processes

Secure environment

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Definitive Outcomes

Ongoing and immediate feedback as to the reliability status of the Mobile MachineIntegrated descriptive, predictive and prescriptive analysis

Based on advanced Artificial Intelligence and Machine Learning algorithmsFind actionable information cost-effectively regardless of the quantity of data

Utilize existing empiric dataFully-automated – no need for manual analysis or human intervention

User-friendly dashboard interface and drilldown tools

KEY BENEFICIARY STAKEHOLDERS

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Service Operators

Regulators

OS/Applications Development

Primes & Integrators

Insurance & Risk Management

Product & Mission Assurance

End CustomersOEMs & Manufacturers

Lease Providers Warranty Providers

MaintainersCloud/Analytics Platform Vendors

CUSTOMER IMPACT

Decreased Maintenance Costs

Improved Reliability

Enhanced Safety

Increased Availability

Incidents, Available Mobile Machines, Downtime

Reduced Liability Costs

Higher User Satisfaction

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Lower Overall Program Costs

Replacement Cost of Inoperative Mobile Machines

Higher ROI

Lower Cost Per Operational HourLower Operational RiskIncreased Available Operational Hours

Per Same Budget

OPERATIONAL

FINANCIAL

Competitive Differentiator

Better Use of Available Data

New Opportunities in Manned and Unmanned Systems Markets

Highlights Improved Past Performance

Cyber HardeningBetter Risk Management Streamlined Material Flows

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More Detailed Troubleshooting Techniques

Reduced Training Burdens

CONTACT

AppliedEA, Inc.

Josh Segal

CEO

+1 (212) 920 6822

josh@applied-ea.com

www.applied-ea.com

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