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1. Deep Link Analytics Empowered by
AI + Graph + Verticals
Dongyan Wang
VP AI Transformation, Landing AI

2. ANDREW NG
CEO and Founder
• Co-Founder of Coursera
• Founding lead of Google Brain
• Former Baidu Chief Scientist
• Stanford Computer Science
Adjunct Professor

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Retail
Travel
Transport & Logistics
Automotive & Assembly
Basic Materials
Advanced Electronics/Semiconductors
Healthcare Systems and Services
High Tech
Telecom
Oil & Gas
Agriculture
$0.8T
$480B
$475B
$405B
$300B
$291B
$267B
$267B
$174B
$173B
$164B
Source: https://www.mckinsey.com/featured-insights/artificial-intelligence/visualizing-the-
uses-and-potential-impact-of-ai-and-other-analytics
Impact by Economic Sector
AI
13 TUSD
AI value creation
by 2030
$13TRILLION

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Landing AI Focus
Vision Platform
• Manufacturing Visual
Inspection
• eCommerce
• Healthcare…
Deep Link Analytics
• Customer 360
• Recommenders
• Telecom, E-Commerce,
retail…
• Platform (Upcoming)
AI Transformation Partnership
Projects, Strategy, Hiring, Training, Communication

5. 75% of AI projects do
not go live
PoC
/Experiment
Works on researcher’s laptop.
May be publishable.
Deployment
Running “live” in production
and creating value for at
least 1 user.
Scale up
Creating significant
business value.
HARD HARD HARD

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ML Code
Customers Need More Than ML Code
Defect Definition &
Ambiguity Resolution
Detect Image Acquisition
Issues Early On
Efficient, Batch Data
Labeling
ML Code
Data
Verification
Defect, Data
Analytics
Data, Model
Version Control
Process Management Tools
Detect Model
Performance
Degradation
Environment
Change
Detection
Create data for
Rare Defects
Only 5% - 10% of AI projects are ML code
Landing AI is good at building a customized stack to serve different verticals

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Landing AI Vision Platform: Visual Inspection, Healthcare…
Enable customers to create, deploy, scale AI solutions with a close-loop platform

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Manufacturing is the Perfect Playground for AI Applications

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• Faster: unify data across silos & generate business value
• Deeper: understand complex data relationship
• Bigger: navigate huge amount, ever growing data
• Optimized for vertical business: analytics for my business problem!
Customers Needs – Deep Link Analytics
even after years of work in BI, EDW, Analytics…

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• How can I sell more to existing customers?
• How can I identify new customers?
• What do my customers need me for today and future?
• Who are my customers' suppliers or what brands do they like?
• How I can retain my customers and prevent churn?
• How do I price my products?
• What products or services will my customers buy?
• How efficiently can I deliver products to my customers?
• How efficiently can I forecast what products are needed and when?
• others ( i.e., Unified ID, customer 360 can expand to healthcare, transportation
and many)
Generate Business Value Fast & Profound!

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The Power of AI + Graph + Vertical Data/Analytics
Graph Database Architecture
Specialized
Vertical AI
Algorithms
Customer, Sales,
Product 360
Deep Data Connection
Data Injection Actionable AI Analytics
Optimized Vertical AI AnalyticsData Injection
Landing AI
Deep AI Analytics
Specialized
Vertical AI
Algorithms
ID Unification, fraud
Detection
Specialized
Vertical AI
Algorithms
e-Comm / Retail /
Telecom
Recommendations
High performance querying
Pre-optimized schema for vertical & AI analytics
Quickly connect siloed data
Product
Specialization
Knowledge Networking

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• Integrate high volume data at high speed and analyze in real-time
• Organize data to reveal deep relationship for decisions
• Pre-build schemas using decades of data & business expertise
• Application ML - algorithms for targeted business solutions
• Ongoing Model Management – versioning, monitoring, CI/CD
• Domain expertise, architecture, customizations
What is Deep Link Analytics?

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Landing Deep Link Analytics: AI + Graph + Vertical Engine
Client Data Graph
Vertex per record ingest high volume at
high speed from enterprise systems
Landing Analytics SubGraph
client-agnostic application features
(derived queries) for AI Modeling
Landing Data-Model Subgraph
algorithms to develop deep data fabric
using pre-defined Landing’s schema
Unify data silos + real-time +
high performance
ID Unification Process
Customer & Product 360
X & Up-Sell Recommender
Demand Sense, Prediction
MLDeploy+CICDAutomation
Vertical App Centric ML models

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Deep Link Analytics: AI + Graph + Verticals
Quickly unify siloed data with
deep data relationships, integrated
across multiple data sources
Organize data using predefined
vertical specific schemas for
feature engineering & analytics
Integrated Multi Model framework using vertical
specific ML/DL models & graph algorithms
Customizable APIs to integrate AI
analytics with customer apps
Automation framework with
CI/CD capabilities Model
Deployment
Data
Pipelines
Data
Organization
Multi-model
Algorithms
Custom
APIs
AI Powered
Deep
Analytics

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Core Business Value
• Integrate & organize siloed customer data to deep knowledge network
• Knowledge network & sharing, data connections, app intelligence
• in an efficient way to drive critical decision making
• High performance query
• Understand deeper relations for focused verticals, starting from customer 360
• Across consumers, products, brands & demand
• Knowing consumers better - consumers emotions and sentiments
• Predicting demand and deliver services for improved customer satisfaction
• Transform data to actions using scientific algorithms
• Customer 360 / Recommendations for x-sell & up-selling products & services
• Demand Sensing, Forecasting / Supply Chain

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Deep Link Analytics Use Cases
Customer 360
Customer churn prevention
Lifetime value prediction
Recommendation engines
Customer sentiment analysis
Customer Journey
Predictive analytics
Fraud detection
Network management
Route optimization
Price optimization
ID Unification: Use deep link analytics to unite/dedupe different users ID across data silos, and
differentiate same or different users
Customer 360: Understand customers full persona across various engagement and interactions
captured as part of their engagement with a product, service or support for sentiments/interests
X-sell / up-sell: know customer better for their needs to buy the right product at the right time
Route Optimization: Vehicle history for routes, repairs, fuel rate, usage, segments (routes),
schedule activities (scheduled vs. actual). And customer 360 can be integrated to add more value
in terms of optimizing based on deeper understanding of customer travel patterns across time,
place and events of interest and family structure.
Predictive Maintenance: Based on vehicles history in terms of various factors like repairs,
breakdowns, services, total cost of ownership including consumption, usage etc.,
Predictive Analytics: Ability to understand historical failures pattern and learn to predict future
failures based on specific identifiable signals over time
Fraud Detection: Ability to understand and identify potential fraud/ risk associated with
transactions based on deep data relationships and risk pattern identifiable within the data network

17. 10-100s
Products
10-100s
Defects/
Data Patterns
x 100-1000s
Labeled Data
x
Gain Control
of Your
AI
Have to Manage Compounding Complexity of AI
Apps?

18. Offline PoC
Deploying AI Needs Model Performance Management
Gain Control
of Your
AI
Time
Performance
Online PoC AI Rampup Track and Adjust
100%
Track Performance Drops
Detect Data Drifts
Continous Learning
Continous Deployments
HLP
Human in the
Loop
(Virtuous Cycle)
EasyCases
Hard Cases

19. Find the right
use cases
Be ready for
data
Plan for
deployment not
PoC
Gain control
of your AI
AI Around the Corner.

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Thank you.
Dongyan Wang
Dongyan@landing.ai
1-650-695-1878