International AI and ML Landscape

Private and Confidential
December 2016
INTERNATIONAL AI / MACHINE LEARNING
LANDSCAPE

2
EU / ISRAEL AI LANDSCAPE
KEY FINDINGS
Magister interviewed 40+ Artificial Intelligence companies across the EU / Israel in a range of application areas,
from healthcare to analytics to music production
We see the current ‘boom’ in AI funding creating significant value due to increased computational capabilities,
big data and efficient algorithms
Potential AI ‘winners’ are all over Europe not just in UK and Israel. Key industries of focus are: Healthcare,
Finance, Retail, AdTech/MarTech and Cybersecurity
We expect to see 15-20 EU / Israel AI acquisitions in 2017 at valuations as high as $10m per employee
We expect an increased interest from mid cap tech and non tech companies following acquisitions by Ford, PTC,
24/7, Quixey
In terms of financings, we expect to see a growing number of smaller financing rounds, as AI companies expand
incrementally. We do not see huge financing rounds for AI companies as most will be acquired.
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AI HAS EXPERIENCED SEVERAL FALSE DAWNS
Source: Press releases
1966
1974
AI research has always been subject to massive funding cuts (“winters”) across the world,
following a period of unrealistic expectations on what AI could achieve
Due to lack of progress on machine translation, US govt. ended the support after having spent $20m. It was concluded that
“machine translation was more expensive, less accurate and slower than human translation”
—Automatic Language Processing Advisory Committee
“In no part of the field have discoveries made so far produced the major impact that was then promised. AI techniques may
work within the scope of small problem domains, but techniques would not scale up to solve more realistic problems”
—Lighthill report, published by British Science Research Council, that led to funding cuts for most British universities
Before cutting all the funding. DARPA spent $1B on Strategic Computing Initiative to advance artificial intelligence during
1983-93. “DARPA should focus its funding only on those technologies which showed the most promise”
—Jacob Schwartz, director of the DARPA Information Science and Technology Office
Starting in 1982, Japanese govt. spent $400m to develop “fifth generation computer” and accelerate AI applications. "Ten
years ago we faced criticism of being too reckless, now we see criticism from inside and outside the country because we
have failed to achieve such grand goals.“ —Kazuhiro Fuchi, head of Fifth Generation Project
80s–90s
80s–90s

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 Increase in computational power allows
even small startups to innovate
 Advances in Big Data allow AI companies to
leverage data efficiently
 There is a shift in funding from unstable
government sources to VCs and strategics
who are not averse to long-term technology
risk
 We are seeing more co-operation between
universities and industry
 Critical mass & strategic value is achieved
even at small sizes (sub 20 employees)
 Many of the current AI companies have yet
to firm up their business models
 In a number of categories we already see AI
technologies being commoditized (e.g.
chatbots, visual search)
 Monetary value of commercial
arrangements tend to be lower than other
areas of tech, since the counter-parties are
often very large corporates with significant
bargaining power vs. small AI players
 Overall many AI companies are still
technology driven, as opposed to
commercially driven
IT IS DEFINITELY “DIFFERENT THIS TIME”

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AI TALENT IS “EVERYWHERE”
UK, ISRAEL PARTICULARLY STRONG
Source: PitchBook, LinkedIn, company websites, press releases
AI Companies
with 20+
Employees
Israel
Europe

6
A CLEAR TREND: BUILD IN EUROPE,
MOVE TO THE US TO SCALE
Source: (1) LinkedIn
Press releases and PitchBook
Note: Profiles of select companies in the next pages
Angel.ai was founded in Germany, moved to US and
was acqui-hired by Amazon
 Sep 2016: Amazon reportedly buys angel.ai, hires co-founder and CEO
Navid Hadzaad as Product Lead, New Initiatives to potentially develop a
chatbot
 Jan 2016: Gobutler, the messaging-based personal assistant, “pauses”
operations in Germany to focus on U.S.
Employees: 10+
US: c.7 EU: c.3
Total Amount Raised: $8m
% of US Investors: 60%
% of European Investors: 40%
CEO, Mihkel Jäätma confirmed he will gradually expand to the US
 Sep 2016: Realeyes hires Bilicic for global role where he will be based in
New York
 Mar 2014: Bill Jaris has been named the first President of North America
at Realeyes, focusing on accelerating the company's growth in the market
Employees: 56
US: c.8 UK: c. 18 HU: c.30
CEO and Co-Founder, Husayn Kassai is currently based in SF, US
 Apr 2016: UK’s Onfido raises $25M as it plans to expand its US business
(currently its largest market)
 Feb 2015: Onfido raises $4.5m to take its automated background checks
global, in particular US
 Aug 2012: Founded in 2012 by three Oxford graduates, to develop
technology to automate these checks so companies can speed up their
“onboarding” process
Employees: 124
US: c.12 UK: c. 99 PT: c.3
Feedzai was founded in Portugal, launched in the US and later
expanded to the UK
 Apr 2016: Feedzai brings on Anthony Lanham as Head of Sales for US
 Mar 2016: Feedzai opens in the UK and hires Richard Harris as new head
of international operations, based in London
 Jan 2014: Feedzai credit-card fraud startup launches in US
Employees: 118
US: c.39 EU: c.72

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KEY AREAS OF FUNCTIONALITIES
MAPPING INTO…
Source: PitchBook
NATURAL LANGUAGE PROCESSING
IMAGE PROCESSINGCORE AI
PREDICTION AND ANALYTICS
 Image Processing companies, albeit small in number, represent some of
the highest value exits — Magic Pony acquired by Twitter for $150m
 Select companies interviewed by Magister:
 Real Eyes (UK): Emotion analysis for brands and media agencies
 MedyMatch (IL): Computer vision for emergency room decision
making
 Other representative companies in EU/Israel
 Prediction and Analytics is the largest category with applications in variety
of industries including Retail, Finance, AdTech, Healthcare, Cybersecurity
 Featurespace (UK): Fraud detection for financial services, insurance
and gaming industries
 Tinyclues (FR): Audience targeting platform for brands and retail
clients
 Application areas are primarily scheduling agents, virtual assistants, search
engines, translators
 Twiggle (IL): AI based search engine for e-commerce customers
 Snips (FR): Voice and text based AI assistant
 Gluru (UK): Smart assistant to organize events, meetings
 Companies in this category develop core AI, ML capabilities with little
focus on specific applications
 There are very few core AI companies in EU and are usually affiliated to
universities. For e.g. , Deepmind and Satalia were born at UCL
 Satalia (UK): AI based optimization engine
 Seldon (UK): Open Source ML platform for enterprises

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… BROAD RANGE OF INDUSTRIES
Source: PitchBook
 Traditionally, Healthcare companies have been slow in leveraging
computing technologies leaving an opportunity for AI startups
 Prime areas of innovation include image diagnosis, virtual doctors, lifestyle
management, emergency room management and medical research
 Israel is leading the way in diagnosis using computer vision (Medymatch,
Zebra Computer Vision) while UK in virtual doctors (Your.MD, Babylon)
and lifestyle management (Biobeats)
HEALTHCARE
FINANCE
 This category is the most well funded among all the industries and is
concentrated in the UK due to its strong FinTech presence
 Key startups serve other FinTech and on-demand companies
 Primary areas of innovation include:
– Fraud Prevention: Ravelin, Featurespace, Feedzai
– AML & Compliance: Onfido, ComplyAdvantage, Behavox
 Compared to other industries, there have been investments from strategic
players including Alibaba (Twiggle), Naspers (Twiggle) and Otto Group
(Blue Yonder)
 Main areas of innovation are search engine, recommendations engine,
replenishment optimization and price optimization
 Retail AI companies are scattered across EU and Israel with very few based
in UK
 AdTech has been one of the few industries that adopted advanced
technologies including AI, ML, NLP since early years
 MarTech, on the other hand, has had very little innovation but we see that
changing with adoption of AI
 Geographically, most of the companies are UK based with very few in
other parts of EU and Israel
E-COMMERCE / RETAIL
ADTECH / MARTECH
 Unsurprisingly, most of the companies in this space are concentrated in
Israel with very few in Europe
 However, Darktrace, spun out of Cambridge University, is far ahead of the
pack, with $100m+ in funding and boasts ex MI5, NSA and GCHQ veterans
as advisors/management
CYBERSECURITY

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MARKET ACTIVITY
TRENDS AND DRIVERS

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AI INVESTMENTS, ACQUISITIONS
KEY FINDINGS
Source: PitchBook
Acquisitions and Financings have grown exponentially since 2011; EU / Israel acquisitions growing faster than US
Acquisitions are primarily driven by team capabilities with companies paying as much as $10m per employee.
Revenue and heavy commercial engagement add little value, hurtful in some cases due to possible leakages in IP
Cross Functional AI companies tend to raise $100m+ then build out independently. We see these companies
concentrated in US
On the other hand, industry specific AI companies can maximize value by building teams of sub 50 employees or
less, demonstrating product value and getting acquired before focusing heavily on commercial engagement
In addition to maximising value, founders and researchers are incentivized by the huge amount of data available
to companies such as Google, Facebook that can improve their machine learning algorithms
It takes very little funding to build out an AI company and we see more “mainstream” VCs taking advantage of
the high Return on Invested Capital (ROIC)
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INT’L M&A GROWING FASTEST
Source: PitchBook
3 2
8
19
25
15
2 3
2
3
4
10
1
2
1
2011 2012 2013 2014 2015 2016
North America Europe / Israel Other
Median Deal Value $80m $73m $29m $87m $30m $150m
Key Acquisitions  Nuance/SVOX—$150m
 eBay/Hunch—$80m
 Nuance/Loquendo—$73m
 Google/CleverSense—n.a.
 Oracle/InQuira—n.a.
 Nuance/Vlingo—$196m
 Facebook/face.com—$100m
 Google/Viewdle—$45m
 Saab/MEDAV—$37m
 Connotate/Fetch—n.a.
 PTC/ThingWorx—$112m
 Avigilon/VideoIQ—$32m
 Intel/Indisys—$26m
 Marketo/Insightera—$20m
 Google/DNNresearch—n.a.
 Yahoo/IQ Engines—n.a.
 Yahoo/lookflow—n.a.
 Microsoft/Netbreeze—n.a.
 NICE/Causata—n.a.
 Google/Deepmind—$650m
 Salesforce/RelateIQ—$390m
 Microsoft/Equivio—$200m
 Tivo/Digitalsmiths—$135m
 Spotify/Echo Nest—$125m
 LinkedIn/Bright—$120m
 AOL/Convertro—$91m
 AOL/Gravity—$83m
 TiVo/Veveo—$62m
 Twitter/TellApart—$553m
 Advance.net/1010data—
$500m
 Vector Capital/Saba Software
—$400m
 Blue Coat Systems/Elastica—
$280m
 Splunk/Caspida—$190m
 PTC/ColdLight—$105m
 Intel/Nervana—$408m
 Intel/Movidius—$400m
 ARM/Apical—$350m
 Microsoft/SwiftKey—$250m
 Apple/Turi—$200m
 Twitter/Magic Pony—$150m
 NICE/Nexidia—$135m
 Oracle/Crosswire—$50m
 eBay/SalesPredict—$40m
We’ve seen 10 EU AI acquisitions in 2016 up from 2 in 2013; we expect the number to increase in the coming years
Annualised
5

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WHAT IS DRIVING M&A ACTIVITY
BY FUNCTIONALITY
Source: PitchBook
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1
2
4 4 4
2011 2012 2013 2014 2015 2016
Median Deal Value: $73m | Medial EV/FTE: $1.3m
 Buyers in this category are looking to improve voice and natural language capabilities to
include contextual awareness, better semantic understanding
 Industry wise, buyers come from social media (Facebook), mobile OS developers (Apple,
Google), market research firms (Gartner)
NATURAL LANGUAGE PROCESSING
IMAGE PROCESSINGCORE AI
PREDICTION AND ANALYTICS
0
2
3
5 5
7
2011 2012 2013 2014 2015 2016
2 2
5
12
18
10
2011 2012 2013 2014 2015 2016
0 0 0
2
3
5
2011 2012 2013 2014 2015 2016
 The targets in this category are very high value compared to others and are largely driven
by team capabilities
 For e.g. Google acquired Deepmind for expertise of Demis Hassabis, former neuroscience
research fellow at UCL and Apple’s acquired Turi for Carlos Guestrin, Prof. at University of
Washington
 Image processing has seen the highest growth among all the categories with buyers coming
from all industries – Social media, semiconductor, retail etc.
 Key deals include Intel’s acquisition of Movidius, ARM’s acquisition of Apical, Twitter’s
acquisition of Magic Pony
 Acquisitions in this category peaked in 2015, we’ve seen a decline in 2016 as AI powered
analytics become commoditized
 Buyers are looking to improve search, recommendation, targeting capabilities
Key Acquisitions Key Acquisitions
Key Acquisitions Key Acquisitions

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LARGE COMPANIES SCRAMBLE FOR
SCARCE TALENT DRIVING UP DEAL VALUES
Source: PitchBook, press releases, company websites
Sep-2016 Aug-2016 Dec-2013May-2014
“While schools like MIT and Stanford do a wonderful job teaching AI, that’s reserved for a small number of students who spend years
getting a Ph.D. What we’re trying to accomplish is getting the latest and best from industry experts so individuals can be ready for AI jobs
quickly. Our partners (IBM, Amazon, Didi) made commitments to take our students very seriously in the hiring process”
– CEO of Udacity after launch of online AI and ML degrees

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BUYER UNIVERSE EXPANDING TO
MID CAP AND LOW TECH COMPANIES
Source: PitchBook
Jul-2016 Jan-2016 May-2015
Buyers interested in acquiring AI companies have expanded from Apple, Google, Facebook etc. to
more traditional and mid cap tech companies such as NICE Systems and PTC

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M&A DONE AT $2.5M PER EMPLOYEE
WITH LITTLE TO NO REVENUE
Source: PitchBook
Median deal value per employee for AI companies with sub 50 employees increased to $8.5m
in 2016 (YTD) up from $1.1m in 2013
Date Acquirer Target Country Description
Deal Value
($m)
FTE
Deal Value /
FTE ($m)
Aug-16 Intel Nervana Systems US Next generation platform for machine learning 408 49 $8.3m
Aug-16 Apple Turi US ML analytics engine for graph datasets 200 23 $8.7m
Jun-16 Twitter Magic Pony UK ML based visual processing for video enhancement 150 11 $13.6m
Apr-16 Oracle Crosswise IL Provider of ML software for cross-device advertising 50 20 $2.5m
Sep-15 Apple MapSense US Mapping technology to leverage geospatial data 30 12 $2.5m
Jul-15 Splunk Caspida US ML based cyber-security and threat detection 190 35 $5.4m
Apr-15 Insitu 2d3 US Motion imagery software 25 40 $0.6m
Oct-14 Microsoft Equivio US Developer of text analysis software for e-discovery 200 24 $8.3m
Jan-14 Tivo Digitalsmiths US Computer vision based video indexing & digital publishing 135 49 $2.8m
Jan-14 AOL Gravity US AI based content recommendations engine 83 40 $2.1m
Dec-13 Avigilon VideoIQ CA Computer vision based video surveillance systems 32 30 $1.1m
Dec-13 Marketo Insightera US Real-time B2B personalization platform 20 20 $1.0m
Sep-13 Intel Indisys ES Natural language and intelligent conversation system 26 20 $1.3m
Oct-12 Google Viewdle UA Mobile focused visual analysis platform 45 36 $1.3m
Median $2.5m
Mean $4.2m

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EU and Israel based AI companies
Global AI companies
MEDIAN ROIC HIGHER FOR EU / ISRAEL AI
COMPANIES COMPARED TO US
Source: PitchBook. Accessed 20-Sep-2016
Note: ROIC calculated by dividing exit value by total funding raised
EU / Israel AI companies reach critical mass (i.e. ready to be acquired) with lower funding
compared to US, explaining higher ROICs

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$0.3B $0.4B
$0.8B
$2.2B
$2.6B
$1.7B
$1.7B
67
133
203
311
396
2011 2012 2013 2014 2015 2016
Deal Value ($m) Total Deals
INVESTING ACTIVITY
DOMINATED BY SMALLER DEALS
Source: PitchBook
Note: (1) Annualised
Annualised
510
There has been steady increase in global AI funding since 2010, a 10x increase from
$0.3B in 2012 to $3.4B in 2016
(1)

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LEADING EU VC INVESTORS PICKING UP
AFTER A PERIOD OF ONLY NICHE INVESTORS
Source: PitchBook
Mar-2016 Oct-2016
Total Funding: $9.5m
Investors: Atomico, Playfair Capital,
Sequoia Capital, Wellington Partners
Total Funding: $30m
Investors: Salesforce Ventures, Talis
Capital, Brightbridge Ventures, CrunchFund,
IDInvest Partners, Wellington Partners
Total Funding: $8.2m
Investors: Google DeepMind, Hoxton
Ventures, Kinnevik, JamJar Investments
Apr-2016

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LARGEST FUNDED COMPANIES FOCUSED
ON CORE SECTOR AGNOSTIC AI
Source: PitchBook
$144m
$108m
$88m
$74m
$57m $55m
$38m $34m $34m
$104m
$55m
$12m
$40m
$33m
$55m
$20m $20m
$14m
Total Funding Latest Funding Round
Latest Round Nov 2014 Mar 2015 Aug 2015 May 2016 Feb 2016 Oct 2014 Mar 2014 Nov 2015 May 2016
Funding Round Series C Series C Series C Series D Series B Development Cap. Series C Series B Series D
Investors  Access Industries
 Eric Di Benedetto
 Horizons Ventures
 Tata Communications
 Bullpen Capital
 Centerview Capital
 Citi Ventures
 Draper Nexus
 Floodgate Fund
 GE Ventures
 IVP
 Khosla Ventures
 KPCB
 A-Grade Investments
 ABB Tech. Ventures
 AME Cloud Ventures
 Bezos Expeditions
 Data Collective
 Felicis Ventures
 Founders Fund
 Khosla Ventures
 Mark Zuckerberg
 Peter Thiel
 Samsung Ventures
 Wipro Ventures
 Lemhi Ventures
 NASDAQ OMX Group
 HCA Holdings
 Credit Suisse
 Goldman Sachs
 In-Q-Tel
 Nashville Capital
 Silver Lake
 Accomplice VC
 Atlas Venture
 IA Ventures
 Intel Capital
 New Enterprise
Associates
 New York Life
 Recruit Strategic
Partners
 Techstars
 KKR  Horizons Ventures
 Lanta Capital
 Mail.Ru Group
 Technion Institute of
Israel
 Ynon Kreiz
 Capital One Growth
Ventures
 Nexus Venture
Partners
 Paxion Capital
 Riverwood Capital
 Transamerica
Ventures
 Bandai Namco
 CAC Holdings
 Fenox VC
 Horizons Ventures
 Kantar Media
 Koa Labs
 KPCB
 Myrian Capital
 Sega Sammy Holdings
 WPP Ventures
US AI Companies are developing core AI technologies that are sector agnostic. In contrast, EU Companies are
focused on AI applications in specific sector(s)
EU Based

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PROFILES OF SELECT COMPANIES

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CELATON
‘THE VIRTUAL SECRETARY’
Products & TechnologyCompany Overview
 Description: Provider of an AI SaaS that streamlines and
automates labour intensive clerical tasks and decision making
 Provides automation for customer service, claims handling,
accounts payable and mailroom processing
 Founded: 2004
 Headquarters: Milton Keynes, UK
 Employees: c. 35 / Total Funding: £2.5m
 Investors: Business Growth Fund
 Key Clients: ASOS.com, Davies Group, Carphone Warehouse,
Kuoni, Virgin Trains, TalkTalk Group
 Key Partnerships: Agilisys, Capgemini, CGI, Genfour, Symphony
Key Management
 Andrew Anderson – Founder & CEO
25 years experience in IT industry in various leadership roles
 Richard Hill – CTO
Previous Head of Software Dev. at Netstore
 Gary Grant – COO
Previous MD at DG Tech - image/document processing firm
• inSTREAM: Processes unstructured and semi-
structured content from customers, suppliers and
employees received by email, social media, fax
• Unstructured Problems: Uses deep machine
learning capabilities to understand unstructured
content, to then make suggestions and resolve
sensitive customer issues
• Virgin Trains Case Study:
 Understand the what/where/when/who of
the complaint and extract the data
 Identify similar problem that has been solved
before
 Use that solution to personalize the response
‘Our offering inSTREAM will be “the best knowledge worker you’ve ever
hired”. A knowledge worker refers to people/systems that are required to
deal with unstructured, unpredictable and descriptive content. It’s a labour
intensive job that requires time, effort and is open to errors and omissions.
Our cognitive automation software can not only understand meaning and
intent but recognises the key data buried within the content that is required
for the process’ - Andrew Anderson, Founder & CEO at Celaton
Source: Company website, PitchBook, press releases

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DESKTOP GENETICS
‘AI-POWERED CRISPR GENOME EDITING’
Key Management
‘Our core tech. is a platform of ML based DNA design algorithms. Trained
on tens of thousands of different CRISPR guides in a variety of cell types,
scientists from fundamental research to cell & gene therapy development
can design patient-specific CRISPR vectors. To date, the DESKGEN™
platform has enabled over 5,000 scientists to design and access the best
reagents for their research. These include high throughput screening for
target discovery, cell & animal model generation and cell therapy for
immuno-oncology applications.’ - Victor Dillard, Founder & COO
 Description: Provider and developer of AI powered gene editing
software for designing patient-specific CRISPR genome editing
vectors (Clustered regularly interspaced short palindromic
repeats), for research and clinical applications.
 Founded: 2012 / Headquarters: London, UK
 Employees: 18 / Total Funding: $2.5m
 Key Investors: Illumina, Boundary Capital, Cambridge University
Entrepreneurs, IQ Capital Partners and Sandbox Industries
 Clients: Editas Medicine, Horizon Discovery, enEvolv, two to-10
pharma companies.
 Jun 2016: Partnered with Twist Bioscience to provide integrated
experiment design & DNA synthesis tools for pooled CRISPR
screens
• DeskGen Cloud: Allows designing of efficient
guides for knockout (removal of gene) and
knockin (one-for-one substitution of gene) using
10 guide design parameters
• CRISPR Library Services: Leverages machine
learning to design custom libraries with the most
appropriate guides for target discovery, target
validation or other genomic screen
• CRISPR Genotyping: Used to determine the most
appropriate guides for model, analyze
experimental results and validate the most
relevant gene editing events
 Riley Doyle – CEO & Technical Lead
Former Associate Engineer at Genentech
 Victor Dillard – Founder & COO
Previous experience at P&G, GSK and Flagship Ventures
 Edward Perello – Founder & CBO
2015 SynBio LEAP fellow

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FEATURESPACE
‘ADAPTIVE BEHAVIORAL ANALYTICS USING ML’
Key Management
‘Our technology is of one of those beautiful British inventions. It is referred
to a ‘fundamental’ and revolutionary invention. Traditional fraud
prevention technologies tend to use rule based static system. Our platform
follows everything in the dataset and self learns using machine learning and
Bayesian analysis. We are then able to spot anomalies and block new
frauds while reducing false positives by 70%.’
- Martina King, CEO of Featurespace
 Description: Provider of ML powered adaptive behavioral
analytics platform for finance, insurance and gaming industries
 Featurespace’s ARIC™ platform creates statistical profiles of your
customers in real-time to detect anomalies in customer behavior
 Key Clients: Betfair, TSYS, OpenBet, William Hill, Zapp, KPMG,
Camelot, Vocalink, ComeOn
 Employees: 60+
 Total Funding: c. $16m
 Key Investors: Imperial Innovations, TTV Capital
 Nov 2016: Ranked in the Deloitte 2016 UK Technology Fast 50
• ARIC™ Fraud Hub: Monitors customer data in
real-time across multiple channels to spot
anomalies and block fraud attacks. Also allows
real time alerting and reporting of frauds
• ARIC™ Responsible Gambling: Enables gambling
companies to maintain a healthy user base by
intervening ‘at risk’ players via multiple channels
including in-game, email, SMS notifications
• ARIC™ Accept: Selects the optimal payment
acceptance route across multiple acquirers
resulting in fewer declined transactions
 Martina King – CEO
Former MD at Aurasma, MD Europe at Yahoo
 Matt Mills – Commercial Director
Former Global Head of Sales at Aurasma
 David Excell – Co-Founder and CTO
Developed ARIC platform at University of Cambridge

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GLURU
‘THE SMART TO-DO LIST’
Key Management
‘Gluru will do for your own personal and business data what Google Now is
attempting for web data – instantly assess and continuously learn about the
information you need; to predict and deliver it when you need it…often
before you realize it yourself’
- Tim Porter, Founder & CEO
 Description: Provider of AI-based task manager that helps users to
generate to-do list which includes answering calls, e-mails and
preparing for meetings/events. Gluru also links to other
productivity platforms, making them smarter
 The “Daily View” shows the important events for the day along
with any files/information needed for the events
 Currently available on web and Android, and in beta on iOS
 Employees: c. 15 / Total Funding: $3.5m
 Key Investors: Playfair Capital, Funding London, Force Over Mass
Capital, GECAD Group
 Clients: O2, Dropbox, Google, LinkedIn
 Tim Porter – Founder & CEO
Former Product and Marketing executive at Google, Apple
and Shazam
 Michele Sama – VP of Engineering
Former Data Strategy Lead and Data Strategy Lead at
Swiftkey
• Gluru Task Assistant: Acts as users’ personal
assistant to generate an intelligent "stream" of
suggested tasks. The process will be learning as it
goes to automatically prioritize the users’ day
• Gluru Integrations: Allows integration of Gluru
technology on other productivity platforms
including Salesforce, HipChat and others, thereby
making them smarter and more useful
• Reliable backup system: High-level of encryption
powered by 365 daily cloud backups, 256-bit
encryption and 99.9% uptime

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JUKEDECK
‘MUSIC PRODUCTION FOR THE MASSES’
 Description: Founded in Cambridge, Jukedeck is developing an AI
system that can compose its own original music
 Jukedeck composes music chord by chord and note by note, giving
video creators and other users simple way of sourcing unique,
original music
 Founded: 2012
 Headquarters: London, UK
 Employees: c.16 / Total Funding: $4m
 Key Investors: Backed, Playfair Capital, Cambridge Innovation
Capital, Parkwalk Advisors, Cambridge Enterprise
 Winner of TechCrunch Disrupt London 2015
Key Management
‘At Jukedeck, our aim is to create amazing, new listening experiences. This
process can be accelerated If we democratise music production, since it
allows non-musical people to take far greater control over their music. We
believe creativity can be enhanced with the use of AI and we have proven
that by building up a core group of recurring users. Soon, it would be like
everyone having a personal composer following them around’
- Ed Rex, Founder & CEO at Jukedeck
 Ed Rex – Founder & CEO
Composer; Published by Boosey & Hawkes
 Patrick Stobbs – Co-founder & COO
Former Strategic Partner Development Manager at Google
 Jonathan Cooper; PhD – Technical Lead
Former Software Engineer at GE
• AI-composed music: Leverages AI to produce
music based on user’s input on elements such as
genre, beats per minute, length etc.
• Produced in realtime: Able to respond instantly,
changing tone and intensity to match a varied
pace and mood. Well suited for video games and
YouTube videos
• Royalty-free: All music tracks created using the
platform are royalty-free (as long as the user
credits Jukedeck); Also offers an option for user
to buy the copyright for the music track

26
MEDYMATCH
‘ACCURATE CLINICAL DECISION MAKING’
 Description: Provider of AI powered, cloud, medical diagnostic
support system
 The company utilizes advanced cognitive analytics and artificial
intelligence to deliver real time decision support tools to improve
clinical outcomes in acute medical scenarios
 Founded: 2012
 Headquarters: Tel Aviv, Israel
 Employees: c. 17 / Total Funding: $2m
 Key Investors: Exigent Capital and Genesis Capital
 Jun 2016: Partnered with Capital Heath Hospitals to improve
critical diagnostics for stroke patients
Key Management
‘With the help of AI, machine learning, and algorithmic experts and our
medical advisory boards, we are making faster and more accurate clinical
decision support. We at MedyMatch acknowledge that speed is essential in
many instances. In addition, having access to an archive of anonymized
medical imaging data, and understanding the patient outcomes, means our
AI is able to continually self-learn and gets incrementally intelligent.’
- Michael Rosenberg , CFO at MedyMatch
 Gene Saragnese – Chairman of the Board and CEO
Former CEO at Philips Healthcare Imaging Systems
 Michael Rosenberg – CFO
Former CFO at Pursway
 Jacob Cohen – Co-Founder & CTO
21+ years exp. in developing computer vision and ML algorithms
• Anonymized medical imaging data access:
Access to data from multiple medical imaging
modalities, including CT, X-ray, MRI, ultrasound
and PET. By incorporating on these records,
MedyMatch’s AI algorithms will self-learn, auto
label and tag hundreds of diseases
• AI, machine learning-based deep vision:
MedyMatch’s imaging AI engine leverages deep
vision and cognitive analytics to compare billions
of data points for immediate identification of the
most rare, long-tail anomalies invisible to the
human eye

27
PREDICSIS
‘LEADING PREDICTIVE DISRUPTION’
 Description: Provider of ML based predictive platform for
marketing and process optimisation such as churn, upsell, cross-
sell, lifetime value
 Also offers other predictive services such as credit scoring, fraud
detection for financial services clients
 Founded: 2013
 Headquarters: Lannion, France
 Employees: c.16 / Total Funding: £2.0m
 Key Investors: Innovacom
 Key Sector Focus: Insurance, Finance, Telco, E-commerce
 Clients: AssurOne, Credit Agricole, EDF, Natixis, Orange, Renault,
Key Management
“We observed that firms using predictive analytics had better customer
loyalty and had 30% better sales performance metrics. However, these
predictive analytics solutions were only available to large high tech
companies tapping into a fraction of the different sources available. We
democratized predictive analytics using AI and Big Data. As a result, one
billion predictive scores are achieved by our product and is activated by
marketing teams in a dozen countries” - Jean-Louis Fuccellaro, CEO
 Jean-Louis Fuccellaro – Co-Founder & CEO
Previous CEO Orange Labs UK
 Bertrand Grezes-Besset – Founder & Chairman
Previous Deputy CEO of Sofrecom
 Bastien Murzeau – CTO
Previous CTO at Azendoo and Founder at Paloma Networks
• Predicsis AI API & SDK : Provides automatic data
preparation, modelling and scoring services
which can be integrated directly into client’s
applications i.e. IT or existing analytics, BI.
• Predicsis AI : Provides end to end ML experience
by allowing non expert users to generate
profiling and scoring directly from multi sources
raw data (CRM, logs, chat, helpdesk
interactions…) in minutes.

28
RAVELIN
‘FRAUD DETECTION FOR REAL-TIME WORLD’
 Description: Developer of a real-time based fraud detection
platform for on demand businesses
 The platform combines machine learning, graph database
technology with merchant's own risk profiling to reduce
chargebacks and manual reviews compared to rule based models
 Founded: 2014
 Employees: 20+ / Total Funding: £5m
 Key Investors: Amadeus Capital, Playfair Capital, Techstars and
Passion Capital
 Clients: Deliveroo, Easy Taxi, Gift Off, Quipup, UrbanMassage
Key Management
‘We are confident that we can deliver a product that gives payments
confidence seen only amongst the giants of the e-commerce world for the
general on-demand market. With a fraud detection technology built from
scratch, Ravelin aims to address the ongoing issue of payment fraud. In
addition, our AI and machine learning approach resulted in our real-time
and accurate solutions, giving our customers confidence’
- Nick Lally, Co-Founder and COO at Ravelin
 Martin Sweeney – Co-Founder & CEO
Former Founding Engineer at Hailo
 Leonard Austin – Co-Founder & CTO
Former Technical Lead at Hailo
 Mairtin O’Riada – Co-Founder & CIO
Former Head of Fraud & Revenue Protection at Hailo
• Machine Learning: The platform self-learns and
develops over time, getting increasingly accurate
as more transactions go through. The accuracy
does not compromise speed and scale, making
Ravelin a perfect platform for on demand
businesses
• Proprietary Graph Database Technology:
Generates a full graph of connections for a
specific customer in microseconds to uncover
fraud networks and block suspect, bogus
accounts before any fraudulent action occurs

29
REALEYES
‘EMOTIONALLY INTELLIGENT MARKETING’
 Description: Provider of a computer vision platform to quantify
human emotions in response to video advertisements
 The platform is used by brands, agencies and media firms to
optimise the effectiveness of video ad campaigns
 Employees: c. 50 / Total Funding: $19m
 Key Investors: Draper Esprit, Entrepreneurs Fund, SmartCap
 Clients: AOL, Disney, HSBC, Ipsos, Lenovo, Mars Inc., Marketcast,
Mediacom, Philips, P&G, Volkswagen, Scripps Networks, Turner
 Pricing: Subscription plans range from $10-100k per month (60%
of business); pay-per use from $3-5k per video (40% of business)
Key Management
At Realeyes, we have trained computers to be able to read people's faces
accurately. By building our core propositions around creative testing and
media planning, we've managed to scale our operations and revenue
rapidly. As we develop real understanding of ways to assist brands,
agencies and media companies improve their performance, we can further
establish Realeyes as the standard in consumer research.’
- Mihkel Jäätma, CEO at Realeyes
 Mihkel Jäätma, MBA – Founder / CEO
Built and ran 4 companies with 200 employees at age of 22
 Elnar Hajiyev, PhD – Founder / CTO
Former co-founder at Semmle and Software Engineer at Siemens
 Martin Salo – Founder / CPO
Built and ran 50 employee web agency at age of 20
• Automated SaaS Platform: Utilises computer
vision and machine learning to process, analyse
facial expressions in the cloud and show results
in near real-time via interactive dashboard
• Ekman’s Universal Emotions + Other Behaviour:
Quantifying human engagement via six basic
emotions - happiness, surprise, sadness, disgust,
fear and confusion – plus attention metrics
• Creative and Media decisions: The videos are
tested on relevant audience segments based on
geography, age etc. to quantify business KPI
potential of videos and build tailored media plans

30
SATALIA
‘ITUNES FOR ALGORITHMS’
 Description: Provider of AI driven optimisation algorithms that
optimise various business decisions ranging from workplace
scheduling, vehicle routing logistics and infrastructure planning
 Also developed an algorithm marketplace that provides
companies with new academic algorithms
 Founded: 2008
 Employees: c. 35
 Total Funding: n.a.
 Financials: Revenue £2.5m in 2016; £3.5m in 2017E
 Awarded Gartner’s Cool Vendors in Data Science 2016
Key Management
‘Insights from the data is only 10% of the journey, decisions need to be
made using these insights. And decision making, to me, is optimisation. In
fact, most problems in various industries are related to objectives-
maximising resource allocation, which in itself is an optimisation problem.
Satalia aims to solve these problems using the right set of algorithms,
coupled with AI self-learning capabilities’
- Daniel Hulme, Founder & CEO at Satalia
 Daniel Hulme Ph.D AI – Founder & CEO
Co-Founder at ASI and Kaufman Global Scholar
 Alastair Moore Ph.D – Co-Founder & Advisor
Head of Innovation and Entrepreneurship at UCL
• iTunes for algorithms: An optimisation algorithm
marketplace that analyses problems and suggests
suitable algorithm for the particular problem
• Workforce schedule organisation: Currently
building a scheduling software for a major
accountancy firm’s workforce of 250,000
• Logistics and infrastructure locations design:
Constructed a last mile delivery solution for
retailers and optimally positioned mobile
network infrastructure to maximise coverage
 Paul Hart – Product Owner - Delivery
Former Product Owner – Transport at Tesco

31
SELDON
‘MACHINE LEARNING FOR ENTERPRISE’
 Description: Developer of an open source and platform-agnostic
machine learning infrastructure focused primarily on deployment
and real-time scoring of recommendation and prediction models.
 The platform can be used by a range of industries including E-
Commerce, Finance, Insurance, Media, CRM
 Founded: 2014
 Employees: c. 7 / Total Funding: £1.6m
 Key Investors: Management, Barclays Accelerator
 Clients: Barclays, Hewlett Packard, Trinity Mirror, Il Sole 24 Ore,
RatedPeople.com, Lastminute.com
Key Management
‘We are seeing an increasing commoditisation of machine learning and AI
technology; Open source goes hand in hand with commoditisation. It saves
people the time they would have otherwise spent building technology in-
house. This allows the whole industry moves faster as people build their
own technologies on top. We, at Seldon, are building a flexible open source
ecosystem of the best machine learning tech and making it easier for
enterprises to deploy “ - Alex Housley, Co-Founder & CEO at Seldon
 Alex Housley – Founder & CEO
Former COO of Rummble Labs - Recommendations SaaS
 Clive Cox – CTO
Ph.D. in Computational Linguistics from University of Sussex
 Lee Baker – Commercial Director
Former Commercial Lead EMEA at Microsoft
• Recommendations Engine: Generates
recommendations for variety of use cases
including basket analysis and next best action in
e-commerce, audience segmentation in AdTech
• Open Source Platform: built using open source
projects – Kubernetes, Spark, Kafka, InfluxDB –
allowing developers the flexibility of deploying
Seldon on-premise or in the cloud
• Predictions Engine: Leverages supervised ML
algorithms and neural networks to predict churn
and customer lifetime value in e-commerce, risk
in finance, sales leads in CRM etc.

32
SNIPS
‘PUTTING AI IN EVERY CONNECTED DEVICE’
 Description: Provider of an on-device software development kit
(SDK) to banking/insurance, automotive, or connected home
clients in order to power their services with AI capabilities
 Snips analyses end users’ context and learns from their habits to
help them access relevant information through natural interfaces
 Aims to embed Artificial Intelligence (AI) in every connected
device while making privacy the priority
 Founded: 2013
 Headquarters: Paris, France
 Key Investors: 500 Startups, Bpifrance, Eniac Ventures, The Hive
Key Management
‘At Snips, we realized that the way we interact with technology is not
intuitive enough. Our mission is to provide tools to delegate tasks for
intelligent assistants to streamline a user’s life. In the future, these AI
assistant will anticipate the user’s intentions, making it even more simple to
use. In ten years, our vision is clearly to make technology "disappear" and
eliminate all the complexities behind the service. ’
- Yann Lechelle , COO at Snips.ai
 Dr Rand Hindi – Founder & CEO
2015 Forbes 30 under 30
 Dr Mael Primet – Co-Founder & CTO
Former Co-Founder at 8pen
 Yann Lechelle – COO
Former Co-Founder at APPSFIRE (Acquired by Mobile Network Grp.)
• Smart Personal Assistant: By accessing users’
device data, Snips creates a highly contextualised
timeline and suggestions, streamlining users’ life
while keeping them hyper-connected
• Personal Knowledge Graph: Snips focuses on the
digital memory, it connects data from multiple
sources to form a personalised knowledge graph.
This is done on-device to ensure users’ privacy
• Intent and Entity Recognition: Leverages Natural
Language Processing and Deep Neural Networks
to understand the context behind the natural
language queries, and maximise disambiguation

33
TINYCLUES
‘TARGETING THE REMAINING 99%’
 Description: Provider of an AI based audience targeting SaaS
platform for brands and retail clients
 Enables B2C marketers to grow the revenue of their CRM
operations, by finding the right audience for any item / service
they want to promote.
 Founded: 2010 / Headquarters: Paris, France
 Key Investors: Alven Capital Partners, Elaia Partners and ISAI
 Clients: Fnac, Club Med, 3Suisses, Lacoste, Center Parcs, Sarenza,
VeryChic, Vestiaire Collective, Vente Privee
 Financials: Revenue doubling for 4 consecutive years
Key Management
‘Over the next few years, AI will redefine how B2C marketers interact with
their customers. Data driven insights will replace preconceptions. Self-
optimizing flows will replace simplistic rules. In that context we enable
marketers to reap the benefits of advanced AI & automation while retaining
strategic control of their CRM. Tinyclues is the leading contender in the
space because we are able to provide value to businesses in an
undisputable way!’ - David Bessis, Founder & CEO at Tinyclues
 David Bessis; PhD Maths – Founder & CEO
10 years as a researcher in pure mathematics (Yale, CNRS, ENS)
 Xavier Haffreingue – COO
Former VP Operations at SAP Infinite Insight
 Olivier Cuzacq – VP Product
Former Product Director at Criteo
• Deep Unsupervised AI algorithms: allow
Tinyclues to metabolise customer data including
user attributes, transaction history, campaign
history to categorise the customers. The
algorithms are self learning and not industry
standard static “rules”.
• Lookalike models: Identifies the most optimal
target segment for a product that closely
resemble customers who bought the particular
product. The models are free from biases in
culture and industry.

34
TWIGGLE
‘E-COMMERCE SEARCH DONE BETTER’
 Description: Provider of AI powered online search solutions to
tier 1 e-commerce companies with $1b+ in revenues
 Business model: Revenue is earned through subscription fees
which is based on usage, search volume and size of product
catalog
 Founded: 2014
 Headquarters: Tel Aviv, Israel
 Employees: 39
 Total Funding: $20m+
 Key Investors: Alibaba, Naspers, Yahoo Japan, State Of Mind
Ventures, Jeremy Yap
Key Management
‘Twiggle is infusing search with an entirely new approach that will allow
digital commerce players to reach their full potential. How engines
understand search queries has and will continue to improve with deeper
natural language understanding. We aim to bridge the search experience to
closer to the positive aspect of the in-store purchasing as we become the
standard form of search for e-Commerce.’
- Amir Konigsberg, Co-Founder & CEO at Twiggle
 Amir Konigsberg PhD – Co-Founder & CEO
Founding Member of Google Operations in Israel
 Adi Avidor – Co-founder & CTO
Former Software Engineer at Google
 Amir Di-Nur – VP of Research and Development
Founded Google's Networking Infrastructure Group in Israel
• Normalisation using AI and NLP: Solves
inconsistency and incoherence of e-commerce
data by normalising the information in product
catalogue by leveraging AI and NLP
• Indexing: The normalised product information
allows Twiggle to better index the product
catalogue and provide much more relevant and
less “embarrassing” search results
• Marketplaces: Twiggle’s search algorithms also
leverage image processing and are effective in
marketplaces where product information
consists of images with little to no textual data

35
WIZDEE
‘GOOGLE FOR BUSINESS INTELLIGENCE’
 Description: Provider of a self serve BI platform that allows users
to analyse and search for business queries including sales,
inventory via a simple natural language text and voice search
 The platform is equipped to integrate data from multiple data
sources including Salesforce CRM, Salesforce Wave, Oracle,
MySQL, PostgreSQL, JIRA, Bitbucket, SAP, Splunk, SQL Server
 Founded: 2012
 Headquarters: Coimbra, Portugal
 Employees: c. 19
 Total Funding: £1.3m
 Key Investors: Portugal Ventures, Novabase
Key Management
‘With Wizdee, organizations have on-the-fly access to information anytime,
anywhere such as during sales and board meetings or client visits. The
decision process becomes more agile. With an easy-to-use interface and an
affordable price, we are opening the door to small companies who don’t
have the money or human resources to invest in complex BI software.’
- Paulo Gomes, CEO of Wizdee
 Paulo Gomes – Co-Founder & CEO
Previous Senior Researcher at the CISUC, Ph.D. in AI
 Bruno Antunes – Co-Founder & CTO
Previous Researcher at the CISUC, Ph.D. in AI
 Laurie Mascott– Head of Sales & Customer Satisfaction
Previous Head of EMEA Sales of Oracle Knowledge
• Natural Language Voice and Text: Wizdee’s powerful
NLP algorithms allows it to respond to natural
language queries through voice and text in real time
• Intuitive Visualisation: Automatically creates
relevant charts and graphs helping the user visualise
the search results. Also allows users to customise the
charts
• Partner Program: Wizdee partners with Resellers and
OEMs to deploy the platform to their customer base
or to integrate Wizdee into their software

International AI and ML Landscape

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