Innodata Inc. · NASDAQ:INOD
Innodata — 24th Annual Needham Growth Conference 2022
43 slides from Innodata's conference presentation, January 2022
Slide 1: 24th Annual Needham Growth Conference, January 2022
We’re a technology company delivering the promise of AI to many of the world’s most prestigious companies. January 2022 24th Annual Needham Growth Conference (Nasdaq: INOD)
Slide 2: Disclaimers
Disclaimers Forward-looking Statements This presentation contains certain forward-looking statements within the meaning of Our actual results could differ materially from the results referred to in forward-looking Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the
Slide 3: Investment Highlights / Who We Are
INVESTMENT HIGHLIGHTS • Unrivaled expertise in managing data has enabled transition to new AI markets and SaaS offerings WHO WE ARE • Massive TAM for AI solutions and services Innodata is a • Multiple organic levers technology company • Six straight quarters of growth and recent key customer wins an
Slide 4: AI is destined to be in everything we use and do
THE INNODATA STORY AI is destined to be in everything we use and everything we do. AI can do things that normally require human intelligence. AI applications have the ability to learn, and then use that knowledge to make decisions in new situations. These AI systems allow humans to expand capabiliti
Slide 5: AI applications are built with data
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. Traditional applications are built with code Example 1 Next >>
Slide 6: AI applications are built with data
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Traditional applications are built with code Example 1 Next >>
Slide 7: AI applications are built with data: original image
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Data: Original Image Traditional applications are built with code Example 1 Next >>
Slide 8: AI applications are built with data: labels
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Data: Image OriginalLabels Image Traditional applications are built with code Example 1 Next >>
Slide 9: AI applications are built with data: image example output
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data FINAL OUTPUT: JSON File “blue_van.8837293.jpg” STATUS: Data: Image OriginalLabels Image Application Learning “
Slide 10: AI applications are built with data: example 2
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Traditional applications are built with code Example 2 Next >>
Slide 11: AI applications are built with data: article text
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Data: Article, Text Traditional applications are built with code Example 2 Next >>
Slide 12: AI applications are built with data: labels for training
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Labels For Training Data: Article, Text Traditional applications are built with code Example 2 Next >>
Slide 13: AI applications are built with data: labeled text
THE INNODATA STORY AI (deep learning) applications are built with data. They succeed when the data is good (and plentiful) and fail when the data is bad. AI applications are built with data Labels For Training Data: Article, Text DATA INPUT: Labeled Text Traditional applications are built with code
Slide 14: Growing provider of AI Solutions & Platforms
THE INNODATA STORY We’ve become a growing provider of AI Solutions & Platforms, leveraging our 20+ year legacy of data ops for the information industry. Legacy Low-Growth Markets Fast-Growing Enterprise AI Markets (<$300M SAM) (~$115B TAM by 2027) Information AI Solutions Forecasted Industry & Platf
Slide 15: Products reflect start-to-finish AI development experience
THE INNODATA STORY Our products and solutions reflect our start-to-finish AI development experience. AI Development, Data AI Model Application Collection, Selection, Layer, AI-Enabled end-to-end Synthesis, and Training, and Workflow and Application Annotation Deployment User Interface
Slide 16: Total addressable market of >$115B
THE INNODATA STORY We have our sights on a total addressable market of >$115B, with >$100B becoming addressable in 2022. AI Development, Data AI Model Application Collection, Selection, Layer, AI-Enabled end-to-end Synthesis, and Training, and Workflow and Application Annotation Deployment User Inte
Slide 17: Total addressable market of >$115B, by segment
THE INNODATA STORY We have our sights on a total addressable market of >$115B, with >$100B becoming addressable in 2022. AI Development, Data AI Model Application Collection, Selection, Layer, AI-Enabled end-to-end Synthesis, and Training, and Workflow and Application Annotation Deployment User Inte
Slide 18: We compete on quality of data and AI efficacy
THE INNODATA STORY We compete on quality of data and resulting AI efficacy, breadth of capabilities, trust, and the value of experience. AI Development, Data AI Model Application Collection, Selection, Layer, AI-Enabled end-to-end Synthesis, and Training, and Workflow and Application Annotation Depl
Slide 19: We support a growing range of AI use cases
OUR STORY We support a growing range of AI use cases
Slide 20: Year-over-year and sequential growth
OUR RESULTS Innodata is achieving year-over-year and sequential growth “Anticipate growth to accelerate in Q4 as well as next year.” - Q4 and 2022 Guidance (3Q-2021 Earnings Call) (Percentages have been rounded)
Slide 21: Higher recurring revenue, less customer concentration
OUR RESULTS Our growth has resulted in higher Q3-2020 Q3-2021 recurring revenues (both SaaS and Top 1 customer 13% 10% managed services) Top 3 customers 30% 13% and less customer Recurring 89% 91% concentration.
Slide 22: Investing FCF back into the business
OUR RESULTS Our strategy is to invest our FCF back into the business for sales expansion and product expansion. Q1 Q2 Q3 Growth Investment $1,974 $3,063 $4,180 Sales 38% 34% 34% Expansion Capex 0% 15% 23% Product Development 23% 17% 12% Marketing 16% 15% 15% Product Management 16% 11% 10% R&D 5% 6%
Slide 23: Cash generation versus consumption
OUR RESULTS In 2019/2020, we aggressively reduced structural costs in order to enable growth investment through operating cash flow. Gross margin DDS from revenue $4.6M increase $7.0M Infra- Investing structure Activities $2.8M $10.7M AGILITY $5.5M Support Cost CONSUMPTION $1.8M Optimization GENERAT
Slide 24: Revenue increase contributing to gross margin
OUR RESULTS Margins from revenue growth also help fund growth initiatives. We expect >50% incremental revenue to contribute to gross margin. Q1 Q2 Q3 2021 Revenue $16.0 $17.0 $17.5 2020 Revenue $14.5 $13.9 $14.6 ! Revenue $1.5 $3.1 $2.9 2021 Gross Margin $5.9 $6.6 $6.7 2020 Gross Margin $4.8 $4.2 $4
Slide 25: Four new SaaS platforms launched in 2022
2022 GROWTH STRATEGY We anticipate that our product management and product engineering investment will translate to four new SaaS platforms launched in 2022. 2021 Q1 '22 Q2 '22 Q3 '22 Q4 '22 Agility PR 2,106 Customers Data Annotation 4 charter customers (GA Q3-2021) Applied AI - Document Intelligenc
Slide 26: 400% increase in sales headcount
2022 GROWTH STRATEGY We anticipate that our sales investment will include a 400% y/y increase in sales headcount by EOY 2021 that will become productive during 2022. EOY 2020 (A) EOY 2021 (A) AI Data Solutions 9 16 2021’s new sales hires are expected to Synodex 0 3 become productive in 2022 due to t
Slide 27: Expanding relationships with large customers
2022 GROWTH STRATEGY “We were all incredibly impressed… I’m excited to say we’d like to partner with In addition to winning new Innodata…” customers, we plan to expand - Leading Social Media company, on first contract further our relationships with expansion in month 10 (March 2021) $1,200,000 large
Slide 28: Multiple interrelated tailwinds
Growing Markets Increased TAM AI anticipated to be the Aligned to AI markets next major technology shift reaching >$115B in next 2-3 years Tuck-in Acquisitions Will consider if 2022 GROWTH STRATEGY accelerates New Products strategy or opens 4 new SaaS platforms We anticipate benefitting from new mar
Slide 29: Delivering the promise of AI
Title Sub Title Delivering the promise of AI Innodata is a technology company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data annotation, AI digital transformation, and industry-specific busines
Slide 30: FAQs
FAQs 1. What are some of your recent 2H-2021 wins? 2. What is the current composition of your revenue stream? 3. What is the AI-enabled Agility SaaS PR platform? 4. What is the AI-enabled Synodex medical record data extraction platform? 5. What is the AI-enabled SaaS data annotation platform? 6. Wha
Slide 31: Recent 2H-2021 wins
FAQs 1. What are some of your recent 2H-2021 wins?
Slide 32: Composition of revenue stream
FAQs 2. What is the current composition of your revenue stream? Q3 Revenue (rounded, in millions) Customers Managed Service Subscription and Software Subtotal % >> Synodex Platform (medical data $1.0 20 $1.0 6% Industry AI Platforms extraction) We encapsulate our AI technologies >> Agility Platform
Slide 33: Agility SaaS PR Platform
FAQs 3. What is the AI-enabled Agility SaaS PR Platform? Agility is our SaaS media intelligence platform used by over 2,500 PR and communications professionals for targeting, distributing, monitoring, and analyzing social and traditional media coverage. Agility has been ranked ahead of the largest t
Slide 34: Synodex medical record data extraction platform
FAQs 4. What is the AI-enabled Synodex medical record data extraction platform? Synodex is our medical data extraction platform used by 12 of the leading life insurers to turn traditional and EHR medical records into computer-addressable digital data for automation and analytics.
Slide 35: SaaS data annotation platform
FAQs 5. What is the AI-enabled SaaS data annotation platform? Our data annotation platform is a web-based, self-service annotation tool for creating training data for AI models. It has auto- tagging intelligence for both classical and generative AI tasks. • On-premise deployment options • Supports l
Slide 36: Intelligent Automation Workflow Platform
FAQs 6. What is the AI-enabled SaaS Intelligent Automation Workflow Platform? • Cloud-based, self-service workflow platform • Robust workflow management: spans asset acquisition, transformation, management & distribution, with collaboration layer • AI/ML-driven expert augmentation • Easy to use user
Slide 37: Innodata AI Suite (Goldengate)
FAQs 7. What is the Innodata AI (Goldengate) and why is it important? It is our proprietary high-performing no-code, horizontal AI/ML platform, trained from many terabytes of data over the past four years. It is at the core of most everything we do. Layout and zones Industry AI Platforms Table/form
Slide 38: Shareholder base and inside ownership
FAQs 8. What is the composition of your shareholder base? Inside ownership? Top Institutional Holders Insider ownership (beneficial) 17.40% Holder Shares Date Reported % Out Value Insider ownership (shares) 9.52% Vanguard Group, Inc. (The) 1,145,026 29-Sep-21 4.26% 10,912,097 Renaissance Technologie
Slide 39: Leadership team
FAQs 9. Who is your leadership team? ` Jack Abuhoff Amy Agress Ed Barrios Subhash Bisht Rommel Camantigue Janet Chua Steve Davis Pragya Dubey Debbie Eala Marissa Espinelli Chief Executive Officer General Counsel VP Performance Chief Information VP Delivery VP Delivery VP Engineering VP Agility VP Fi
Slide 40: Public and private valuation comps
FAQs 10. What are some of your public and private company valuation comps? INOD trading at 2.2x EV/TTMRev (as of market close January 7, 2022) Select comps: o Sprout Social: $172M TTM revenue valued at $4B (23x) (Public) o Scale AI: forecasted to near $100M in 2021, valued at $7B (70x) (Private) o T
Slide 41: Endnotes
Endnotes 1 Data Annotation Tools Market to 2027 - Global Analysis and Forecasts by Type ; Annotation Type ; End-user (ReportLinker, March 2020) 2 Data Engineering, Preparation, and Labeling for AI 2020 (Cognilytica Research, 2020) 3 Document Analytics Market by Product Type (Solution and Services),
Slide 42: Reconciliation of non-GAAP metrics
Appendix Reconciliation of non-GAAP metrics Reconciliation of net income (loss) to EBITDA ($ in millions) 2021 2021 Q1 Q2 Q3 YTD Consolidated Net Income (Loss) $ 0.4 $ (0.1) $ (0.8) $ (0.5) Add: Depreciation and Amortization 0.7 0.7 0.7 2.1 Tax Provision (0.1) 0.3 0.3 0.5 EBITDA $ 1.0 $ 0.9 $ 0.2 $
Slide 43: Delivering the promise of AI
Title Sub Title Delivering the promise of AI Innodata is a technology company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data annotation, AI digital transformation, and industry-specific busines
Innodata — 24th Annual Needham Growth Conference 2022 on 6ix