This document discusses how AI can help enable connected retail. It describes Peak's AI system which provides a full-stack solution to help retailers better understand customers, optimize operations, and deliver personalized experiences. The system includes infrastructure to ingest and process data at scale securely, AI tools for building and managing models, and pre-built solutions for applications like predictive marketing, demand forecasting, and supply chain optimization. Peak aims to make AI more accessible and help retailers realize business benefits like increased sales, profitability and customer satisfaction.

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Take AI/ML from theory to practice
with Intel technologies on AWS
A I M 2 0 3 - S
Binay Ackalloor
Director
Intel AI Business Development for Cloud Service Providers

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Spotlight on:
Delivering connected retail with AI
A I M 2 0 3 - S
Richard Potter
CEO
Peak
Tom Summerfield
Head of Commerce
Footasylum

4. Agenda
1. Retail landscape
2. Connected retail
3. The AI tech stack for retail
4. AI Infrastructure
5. AI Studio
6. Peak solutions
7. Q&A

6. Its not all doom and gloom
+4%
YoY growth
+3%
YoY growth
US
retail sales
UK
retail sales
*Source: Securities and Exchange Commission (SEC)*Source: ONS

8. We are Footasylum
Creativity connects our bricks,
clicks and kicks.
• Through our 60+ stores, website and own
brands we’re story tellers, intent on pushing
boundaries.
• We became a part of our consumers’
community: urban style leaders who drive
trends and do things our own way.
• Endorsed by this community of
tribes we’re young and agile – never straight
laced.

9. Retailers need to be better connected
Context ControlConvenience
With customers

10. Retailers need to be better connected
Context ControlConvenience
…and internally too.
Marketing Supply chainMerchandising

11. Connected retail isn’t easy
Understand
your consumer
Ensure
efficient delivery
Understand
product interactions
1m+ consumers
60+ stores
25k products
3x commerce channels
Omnichannel Fulfilment

12. AI supports connected retail
Makes sense
of data
Supports the
three Cs and puts
consumer first
Connects data, workflows
and functions.
Optimizes supply and
demand

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14. Built to deliver outcomes

15. AI: From hype to reality
Computational
power
Scalable cloud
infrastructure
AI System and
solutions
The data to
operationalize AI
Connected retail
experience

16. Peak’s AI System gives retailers
everything they need to
succeed with AI, accessed via
customizable web-based apps
and system integrations

17. The Peak AI System
The full-stack AI System, built
to deliver AI-driven outcomes
across an enterprise.
An intelligence layer across the enterprise

19. Store and unify data in
our secure and scalable
infrastructure

20. Configure AI solutions
to your business’ needs,
or build custom ones

21. Activate Peak solutions, either
standalone or interconnected,
and send outputs directly into
business systems using APIs

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23. Peak’s AI Infrastructure allows
the AI System to seamlessly
integrate with a wide-range
of connectors.
Seamless integration

24. Data ingestion and processing at scale
Real time streaming
of enterprise datasets
Apache Spark on
EMR for processing
Stored on S3
and Redshift
Data availability on
time for multiple
workflows

25. MLOps in a box
Cloud-native,
built for AI and ML
Support for open-source
tools and frameworks
Several instance types
to fit various ML use cases

26. Enterprise-grade security and control
Enterprise-grade security
and compliance
Built-in controls for identity,
data and network access
for assets
Governance with audit
trail and resource
management

27. AI Lifecycle
Discovery Scope &
proposal
Build &
Configure
Deploy
& Run
Optimization
& Management
• Exploration
• Prioritization
• Feasibility
• Business KPIs
• Data exploration
• Data preparation
• Build & train
• Evaluate
• Deploy
• Integrate
• Monitor
• Evaluate
• Validate KPIs
• Support
• Re-training

28. Significant resource needed
Discovery Scope &
proposal
Build &
Configure
Deploy
& Run
Optimization
& Management
• Exploration
• Prioritization
• Feasibility
• Business KPIs
• Data exploration
• Data preparation
• Build & train
• Evaluate
• Deploy
• Integrate
• Monitor & Evaluate
• Validate KPIs
• Support
• Re-training
Resource
requirements:
Data Sc.
Data Eng.
SW Eng.
AI Ops.
Biz Ops.
Data Sc.
Data Eng.
SW Eng.
AI Ops.
Biz Ops.
AI Ops.
Data Sc.
Biz Ops.

29. Significant technology needed
Discovery Scope &
proposal
Build &
Configure
Deploy
& Run
Optimization
& Management
• Exploration
• Prioritization
• Feasibility
• Business KPIs
• Data exploration
• Data preparation
• Build & train
• Evaluate
• Deploy
• Integrate
• Monitor & Evaluate
• Validate KPIs
• Support
• Re-training
Platforms: • ETL/ELT
• Data Streaming tool
• Data Warehouse
• In-memory database
• Data Wrangler
• ML Workbench
• Continuous Integration
• ML pipeline
• In-memory database
• Container-orchestration
• ML Workbench
• API Monitoring tool
• In-memory database
• Container-orchestration
system

30. …not with Peak!
Discovery Scope &
proposal
Build &
Configure
Deploy
& Run
Optimization
& Management
• Exploration
• Prioritization
• Feasibility
• Business KPIs
• Data exploration
• Data preparation
• Build & train
• Evaluate
• Deploy
• Integrate
• Monitor & Evaluate
• Validate KPIs
• Support
• Re-training
Full stack
AI system:

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32. End-to-end AI workflow

33. Customizable UI and integration

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35. Linking ML tech with retail needs
Context Convenience
Hyper-personalized
marketing comms
Recommendations
and targeted promos
AI powers
buying decisions
Warehouse &
labor optimization
Customer
expectations
Control

36. Customer AI
Get more from your marketing
CONTEXT
Message
personalization
Recommendation
engine
AI driven Social
media targeting
HYPER-PERSONALIZATION = SALES
Higher return
on ad spend
Increased conversion
and higher AOV
Reduced churn, increased
campaign revenues
PREDICTION
Customer
data
Predictive Customer View
+

37. CONTEXT
28%Uplift in email revenue
10xReturn on Ad spend (ROAS)
Rapid time to value

38. Demand AI
CONVENIENCE
AI-driven demand planning and optimization
IMPROVE PROFITABILITY
Increased return
on capital
Improved efficiency
and CX
Higher margins,
faster sell through
PREDICTION
Customer
data
Predictive Demand View
+ =
Markdowns
and promotions
Inventory allocation
Buying
optimization
OPTIMIZATION
Assortment

39. Demand AI in practice
CONVENIENCE
Demand modelling
suggests optimum prices
for products
This maximizes margin,
balancing rate of sale with
markdown size
Most pricing decisions made
automatically, increasing time
for creativity in merchandising

40. Supply AI
Fulfill demand at reduced costs
CONTROL
Logistics
optimization
Workforce
planning
Warehouse
optimization
OPTIMIZATION = IMPROVE PROFITABILITY
Reduced stock
movement costs
Minimized
labor costs
Increased fulfilment
efficiency
PREDICTION
Customer
data
Predictive Supply View
+

41. Supply AI in practice
CONTROL
AI System determines
optimum stock levels
Supply AI connects to
Demand and Customer AI
Stock allocated
intelligently across pick-
face and bulk storage

42. Delivering connected retail
Amazing customer
experience
Optimizing
for profit

43. Delivering connected retail
Amazing customer
experience
Optimizing
for profit
The future of retail.
Driven by AI

45. THE only AI stack
Scalable + Secure + Outcome-focused
Focused on what’s important
for future-facing retailers.
Peak.ai/reinvent
Visit us on the Intel stand
and find out what your AI
opportunity could be

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47. Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Richard Potter
Richard.Potter@peak.ai
Tom Summerfield
Tom.Summerfield@footasylum.com