Innodata Inc. · NASDAQ:INOD
Innodata — Q3 2023 Earnings Presentation
37 slides from Innodata's earnings presentation, Nov 7, 2023
Slide 1: Poised for Transformational Growth Driven by AI Wins
Q3-'23 Poised for Transformational Growth Driven by AI Wins with Five of the Largest Global Tech Companies and Leading Enterprises NASDAQ: INOD v2_11072023
Slide 2: Disclaimers
DISCLAIMERS Forward-looking Statements and Exchange Commission. This presentation contains certain forward-looking statements within the meaning of Section Our actual results could differ materially from the results referred to in forward-looking 21E of the Securities Exchange Act of 1934, as amende
Slide 3: We're Excited for Today's Discussion
01. INVESTOR HIGHLIGHTS 05. DIFFERENTIATORS We’re Excited for 02. GENERATIVE AI OPPORTUNITY 06. COMMERCIAL STRATEGY Today’s Discussion 03. HOW WE HELP 07. FINANCIAL OVERVIEW 04. CASE STUDIES 08. MANAGEMENT AND BOARDS 3
Slide 4: About Innodata
01. INVESTOR HIGHLIGHTS About Innodata. We’re a global technology and data engineering company recently laying down a track record of mega-wins in generative AI foundation model development. We have the fundamentals in place for long-term secular growth: • Recently won transformative generative AI e
Slide 5: Growing Momentum – frequency and size of mega-wins has steadily increased in 2023
01. INVESTOR HIGHLIGHTS Growing Momentum – frequency and size of mega-wins has steadily increased in 2023 Innodata Signs New Big Tech for Generative AI Development - November 2023 Innodata Lands Significant LLM Expansion with New “Big Five” Customer Announced July 18, 2023 Innodata Announces Agreeme
Slide 6: Our customers now include five of the ten largest global tech companies
03. INVESTOR HIGHLIGHTS Our customers now include five of the ten largest global tech companies building the generative AI ecosystem. The ten largest global technology companies by market capitalization: $2 Trillion+ $2 Trillion+ $1 Trillion+ $1 Trillion+ $1 Trillion+ $800 Billion+ $600 Billion+ $40
Slide 7: Our customers also include leading enterprises seeking to deploy generative and classical AI
01. INVESTOR HIGHLIGHTS Our customers also include leading enterprises seeking to deploy generative and classical AI in a range of products and operations. Audio-Based Assistants Avatar Generation Autonomous Vehicles Content Operations Contract Extraction Content Moderation Conversational AI Custome
Slide 8: Generative AI is anticipated to be the next-generation computation platform
02. GENERATIVE AI OPPORTUNITY Generative AI is anticipated to be the next-generation computation platform. We believe it will rival the internet and mobile in significance. • Big Five AI arms race now underway – billions allocated1 • McKinsey & Co. believes generative AI will potentially: o Automate
Slide 9: We're just two steps into the generative AI marathon
02. GENERATIVE AI OPPORTUNITY We’re just “two steps into the [generative AI] marathon”. 4 Opportunities (and challenges) abound • Big Five Global Tech in virtual arms race deploying billions of capex to forge path forward and discover emergent properties • Enterprises seeking to adopt to stay compet
Slide 10: Seizing opportunities and addressing challenges is requiring large-scale data engineering
02. GENERATIVE AI OPPORTUNITY Seizing opportunities and addressing challenges is requiring large-scale data engineering. High-quality, robust training data is key AI ingredient for seizing opportunities and overcoming challenges: Compute Algorithms Data Engineering 10
Slide 11: We have a full set of services and platforms to support builders and adopters
03. HOW WE HELP We have a full set of services and platforms to support both the builders and the adopters of generative AI. Services Data Collection + Normalization LLM Integration Builders (Large Tech) Training Datasets Across Data Modalities AI Model Building and Management Synthetic Data AI Appl
Slide 12: Our data engineering capabilities span modalities, languages, and domains
03. HOW WE HELP Our data engineering capabilities span data modalities, languages, and domains, and nine categories of tasks required by generative AI. 1- Translation 3- Logic & Semantics 4- Textual Information • Source Language to Target Language • Coherence Classification • Coreference Resolution
Slide 13: Our data engineering capabilities span modalities, languages, and domains (continued)
03. HOW WE HELP Our data engineering capabilities span data modalities, languages, and domains, and nine categories of tasks required by generative AI. ...continued, from last page 5- Document information 6- Creation 7- Editing 8- Dialog 9- Programming • Section Classification • Paper Review • Gramm
Slide 14: Our training data results in better performance across key LLM objectives
03. HOW WE HELP Our training data results in better performance across key LLM objectives and assessments. Key Objectives Response ID: 8903X • Adapting to domains or contexts Assessed by: D.Rue • Improved model performance Date: 5.26.23 • Model behavior control • Data efficiency • Bias mitigation •
Slide 15: We Help Train World-Class Foundation Models
04. CASE STUDIES We Help Train World-Class Foundation Models CASE STUDY: HYPERSCALER CLOUD PROVIDER BUILDING FOUNDATION MODEL An LLM was trained to perform specific tasks based on user prompts or instructions. We then assessed the model’s helpfulness and identified instances where the output violate
Slide 16: We Fine-Tune LLMs on (Private) Vertical Domain Data
04. CASE STUDIES We Fine-Tune LLMs on (Private) Vertical Domain Data CASE STUDY: INFORMATION COMPANY TRAINING LLMs TO SUPPORT PRODUCT SET Our customer sought to support a range of information products with a multi-lingual private LLM foundation model in a virtual private cloud using its proprietary
Slide 17: We Reimagine Work Through Generative AI-Enabled Platforms
04. CASE STUDIES We Reimagine Work Through Generative AI-Enabled Platforms CASE STUDY: ONE OF TOP THREE GLOBAL BANKS REINVENTING REGULATORY CHANGE MANAGEMENT Leading global financial institution seeks to drive efficiency and performance in internal regulatory compliance workflows, reduce compliance
Slide 18: Our Unique Credentials are helping us win
05. DIFFERENTIATORS Our Unique Credentials are helping us win One of our largest customers calls Innodata its “secret weapon.” WE LEVERAGE: WE BRING: • 4,000 global subject matter experts that create consistently accurate complex data at scale • 30+ years providing quality data to the world’s leadin
Slide 19: Our competitive advantages are getting stronger every day
05. DIFFERENTIATORS Our competitive advantages are getting stronger every day. • Skills and referenceability acquired helping five of the 10 largest technology companies build the very foundation models enterprises seek to integrate or fine-tune • Experience gained across wide range of use cases wor
Slide 20: We are uniquely positioned in our competitive set
05. DIFFERENTIATORS We are uniquely positioned in our competitive set. We serve both tech companies building foundation models and enterprises integrating and fine-tuning models. We uniquely span three AI submarkets, creating a virtuous circle of competitive advantage. AI Infrastructure and Data Dat
Slide 21: We're successfully executing a Significant Market Opportunity
06. COMMERCIAL STRATEGY We’re successfully executing a Significant Market Opportunity Generative AI-Focused IT Services: 100% 10-Year CAGR (2022 – 2032) From $83M (2022) to $21.7B (2027E) to $85.9B (2032E) 21
Slide 22: Where we are headed
06. COMMERCIAL STRATEGY Where we are headed • Accelerating Revenue Growth • Servicing Customers at the Highest Point of Value • Right Place Right Time with Our AI Offerings • Improving Economics • Higher Mix of Subscription/Platform Revenue • Operating Leverage (Incremental Margin) • Accelerating Fr
Slide 23: Our execution is guided by a concrete vision, four strategic pillars and five opportunities
06. COMMERCIAL STRATEGY Our execution is guided by a concrete vision, four strategic pillars and five defined opportunities Our Vision: To Be the Leading Global AI Technology Company Serving Both AI Builders and AI Adopters Four Strategic Pillars Landing Expanding Innovating Eating New Customers and
Slide 24: Opportunity 1/5: World's largest global technology companies
06. COMMERCIAL STRATEGY Opportunity 1/5: World's largest global technology companies. POINT OF VIEW The world's largest technology companies will continue to commit billions of dollars to their LLM strategies over the next decade, including developing in-house foundational LLMs optimized for their o
Slide 25: Opportunity 2/5: 50-100 technology companies also building foundation models
06. COMMERCIAL STRATEGY Opportunity 2/5: 50-100 technology companies also building foundation models. POINT OF VIEW Beyond the world's largest tech companies, 50-100 tech companies, including well-funded startups, are now building or are likely to build LLMs. RECENT ACCOMPLISHMENTS Continuing to bui
Slide 26: Opportunity 3/5: Enterprise LLMs via white label distribution
06. COMMERCIAL STRATEGY Opportunity 3/5: Enterprise LLMs via white label distribution. POINT OF VIEW A decade from now, all successful companies will be AI companies. Companies with ability to leverage proprietary data for training LLMs will seek to fine-tune closed-source and commercially available
Slide 27: Opportunity 4/5: Enterprise LLMs via direct distribution
06. COMMERCIAL STRATEGY Opportunity 4/5: Enterprise LLMs via direct distribution. POINT OF VIEW We believe that many of our existing, legacy customers are likely to require our LLM services. We also believe that our sales and marketing will enable us to build out an increased pipeline of direct cust
Slide 28: Opportunity 5/5: AI-enabled platforms
06. COMMERCIAL STRATEGY Opportunity 5/5: AI-enabled platforms. POINT OF VIEW Successfully re-inventing the way work is done through LLM-augmentation will require a constellation of technologies, significant data science expertise, and large amounts of domain- specific training data. Abstracting this
Slide 29: FY2022 Financial Performance
Revenue $79.0M (+13% y/y) Revenue ($M) Adjusted Gross Profit 1 79.0 69.8 07. FINANCIAL OVERVIEW $31.5M 58.2 (40%) FY2022 Financial Performance. Adjusted EBITDA 1 $-3.3M (-211% y/y) 2020 2021 2022 Revenue 1 Non-GAAP measure. Please see “Reconciliation to Non-GAAP Measures" 29
Slide 30: 3Q2023 Financial Performance
Revenue $22.2M Revenue ($M) (20% y/y)* $24.0 $22.0 $20.0 $18.0 $16.0 $14.0 Adjusted Gross Profit 1 $12.0 $10.0 07. FINANCIAL OVERVIEW $9.5M Q1'20 Q2'20 Q3'20 Q4'20 Q1'21 Q2'21 Q3'21 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 (43%) Remaining Revenue Social Media Customer revenue 3Q2023 Financial
Slide 31: YTD2023 Financial Performance
Revenue Revenue ($M) $24.0 $60.7M $22.0 (2% y/y)* $20.0 $18.0 $16.0 $14.0 $12.0 $10.0 Adjusted Gross Profit 1 Q1'20 Q2'20 Q3'20 Q4'20 Q1'21 Q2'21 Q3'21 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Remaining Revenue Social Media Customer revenue 07. FINANCIAL OVERVIEW $25.1M (41%) YTD2023 Financia
Slide 32: We have a clean balance sheet
07. FINANCIAL OVERVIEW We have a clean balance sheet. • Cash and short-term investments o $14.8M of cash and short-term investments at the end of Q3’23 • No external debt o Clean balance sheet with no external debt/borrowings • Secured Line of credit o Access to Wells Fargo secured credit line of up
Slide 33: There's leverage in our operating model
07. FINANCIAL OVERVIEW There’s leverage in our operating model. 23.0 Revenue ($M) & Adjusted EBITDA1 as a % of Revenue 20% 22.2 22.0 21.2 15% 21.0 20.0 14% 19.7 10% 20.0 19.4 18.8 19.0 18.4 8% 5% 18.0 4% 0% 17.0 1% -5% 16.0 -5% -7% Strategic investments were made to support 15.0 -7% -10% Q1'22 Q2'22
Slide 34: Q4 2023 guidance points to continued growth and margin expansion
07. FINANCIAL OVERVIEW Q4 2023 guidance points to continued growth and margin expansion. We are forecasting the following in Q4’23: • Revenue of $24.5 million or more, representing 26% or higher year-over-year growth (30% if excluding comparative period revenue from the large social media company).
Slide 35: Reconciliation to Non-GAAP Measures
$ in Millions FY'22 Q3'23 Sep YTD'23 Consolidated Net Income (Loss) -$11.9 $0.4 -$2.6 Tax Provision $1.5 $0.4 $0.8 Interest Expense $0.0 $0.2 $0.3 Depreciation and Amortization $3.9 $1.2 $3.5 Severance $0.0 $0.0 $0.6 07. FINANCIAL OVERVIEW Stock-based compensation $3.3 $1.0 $3.0 NCI -$0.1 $0.0 $0.0
Slide 36: Focused on Winning Through Well-Defined Strategies and Superior Execution
08. MANAGEMENT AND BOARDS Focused on Winning Through Well-Defined Strategies and Superior Execution Eduardo Kassner Dr. Rada Mihalcea MICROSOFT CHIEF PROFESSOR, UNIVERSITY DATA OFFICER OF MICHIGAN & HEAD OF AI LAB ADVISORY BOARD Jack Abuhoff AK Mishra Rahul Singhal Marissa Espineli CHIEF EXECUTIVE C
Slide 37: Delivering the promise of AI
Delivering the promise of AI Innodata is a global data engineering 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 collection/annotation, AI digital transformation, and industry-specific