RCS - Innodata Inc. - Innodata Reports Fourth Quarter & Year End Results
23/02/2024 08:15
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RNS Number : 1868E Innodata Inc. 22 February 2024
Innodata Reports Fourth Quarter and Fiscal Year 2023 Results
Fourth Quarter Revenue Up 35% Year-Over-Year
NEW YORK, NY / ACCESSWIRE / February 22, 2024 / INNODATA INC. (NASDAQ:INOD)
today reported results for the fourth quarter and the year ended December 31,
2023.
· Revenue for the quarter ended December 31, 2023 was $26.1
million, up 35% from revenue of $19.4 million in the same period last year.
The comparative period included $0.5 million in revenue from the large social
media company that underwent a significant management change in the second
half of last year, as a result of which it dramatically pulled back spending
across the board. There was no revenue from this company in the three months
ended December 31, 2023.
· Net income for the quarter ended December 31, 2023 was $1.7
million, or $0.06 per basic share and $0.05 per diluted share, compared to a
net loss of $2.0 million, or $0.07 per basic and diluted share, in the same
period last year.
· Total revenue for the year ended December 31, 2023 was $86.8
million, up 10.0% from revenue of $79.0 million in 2022. The comparative
period included $8.5 million in revenue from the large social media company
referenced above. There was no revenue from this company in 2023.
· Net loss for the year ended December 31, 2023 was $0.9 million,
or $0.03 per basic and diluted share, compared to net loss of $12.0 million,
or $0.44 per basic and diluted share in 2022.
· Adjusted EBITDA was $4.3 million in the fourth quarter of 2023,
compared to Adjusted EBITDA of $0.2 million in the same period last year.*
· Adjusted EBITDA was $9.9 million for the year ended December 31,
2023, compared to Adjusted EBITDA loss of $3.3 million in 2022.*
· Cash, cash equivalents and short-term investments were $13.8
million at December 31, 2023 and $10.3 million at December 31, 2022.
* Adjusted EBITDA is defined below.
Amounts in this press release have been rounded. All percentages have been
calculated using unrounded amounts.
Jack Abuhoff, CEO, said, "We are pleased to announce fourth quarter 2023
revenues of $26.1 million, representing 35% year-over-year growth and 18%
sequential growth. We exceeded our guidance of $24.5 million by 6.5% as a
result of strong customer demand for generative AI services and our ability to
ramp up quickly to meet customer demand. In 2023 overall, we grew revenues
10%.
"It is worth noting that our Q4 2023 year-over-year revenue growth was 39%,
versus 35%, and our year-over-year revenue growth was 23%, versus 10%, if we
back out revenue from the large social media company that went through a
highly-publicized take-private in 2022 in conjunction with which it terminated
our services (as well as services from many of its other vendors) and laid off
80% of its staff. This customer contributed $8.5 million in revenue in 2022
and $0.5 million in revenue in Q4 of 2022. Beginning in Q1 2024, revenue from
this customer will no longer provide a drag on year-over-year comparisons.
"We are also very pleased to announce fourth quarter Adjusted EBITDA of $4.3
million, exceeding our guidance of $3.7 million by 16%.
"Growth in Q4 was driven primarily by ramp of generative AI development work
for one of the Big Five tech companies we signed mid-2023 and also benefited
by the start of the generative AI development program with another of the Big
Tech customers we announced late last summer.
"In late Q4, the first customer I mentioned signed a three-year deal with us
for our current, initial program, with an approximate value of $23 million per
year for each of 2024, 2025, and 2026, or $69 million for the three years,
based on the not-to-exceed value of the statement of work. We're very proud of
this achievement. It came with customer kudos for the work we've done and
expressions of interest in expanding the partnership further. That said, and
as a cautionary note, investors should understand that there are a number of
ways under the SOW that the customer could terminate early or reduce spend if
it chose to. We believe the quality of our services will always be the key to
enduring customer relationships, not the stated term or value of a contract.
"We're off to a strong start to 2024. We entered the year with master service
agreements in place with five of the so-called Magnificent Seven technology
companies. With two of these companies, we are now solidly underway. A third
also contributed to Q4 growth, with a more significant ramp-up from this
customer starting this month. We are optimistic we will grow revenues with all
three of these customers in 2024.
"With the remaining two of the five Mag Seven customers, we've barely gotten
out of the gate, but we are optimistic about making significant inroads this
year. We are also in conversations with several additional companies,
including some of the most prominent leaders in generative AI today.
"We believe we have the strategy, business momentum and customer relationships
to deliver significant revenue growth in 2024. We will stick with our annual
growth target of 20% in 2024 with the intention of over-achieving this."
Abuhoff continued, "In 2024, we will target two broad markets. The first is
Big Tech companies that are building generative AI foundation models and we
believe are likely to spend significantly on generative AI development. For
these Big Tech companies, we provide a range of services they require to
support their gen AI programs. One of these services is the creation of
instruction data sets. You can think of instruction data sets as the
programming used to fine tune large language models. Fine tuning with
instruction data sets is what enables the models to understand prompts, to
accept instruction, to converse, to apparently reason, and to perform the
myriad of incredible feats that many of us have now experienced. We will also
be providing reinforcement learning and reward modeling, services which are
critical to provide the guardrails against toxic, bias and harmful responses.
In addition, we are also involved in model assessment and benchmarking,
helping ensure that models meet performance, risk and emerging regulatory
requirements. Based on my conversations with several of these companies, as
well as public remarks they have made, we believe they are likely to spend
hundreds of millions of dollars each year on these services. This spend is
separate from and in addition to their spend on data science and compute, the
other essential ingredient of high-performing large language models.
"Our second target market is enterprises across a wide range of verticals that
seek to integrate and fine-tune generative AI models. These are still early
days in terms of enterprise adoption of generative AI, but we believe that a
decade from now virtually all successful businesses will have adopted
generative AI technologies into their products and operations. For
enterprises, our offerings including business process management, in which we
re-engineer workflows with AI and LLMs and perform the work as ongoing managed
services. We also offer strategic technology consulting, where we work with
customers to define roadmaps for AI and LLM integration into both operations
and products and build prototypes and proofs-of-concept. We also fine-tune
models, both in isolation and as part of larger systems that incorporate other
technologies. For enterprises, we are capable of going soup-to-nuts,
everything from initial consulting to model selection to finetuning,
deployment, and integration, as well as testing and evaluations to ensure that
the LLMs are helpful, honest, and harmless.
"Also for enterprises, we offer subscription-based platforms and industry
solutions that encapsulate AI - both our own models and leading 3(rd) party
models. Much the way data is at the heart of the programming-like work we do
for Big Tech, data is similarly critical to enterprise deployments. Enterprise
use cases tend to be highly specific and targeted, requiring models that are
trained with industry-specific or domain-specific data or that require
significant prompt engineering efforts and in-context learning utilizing
carefully curated and organized company data.
"The bottom line here is that data engineering is important for the big tech
companies building generative AI foundation models and the enterprises
adopting these technologies. Data engineering has been our focus for the past
two decades, and we believe we are quite good at it."
Abuhoff concluded, "In response to some questions we've recently been asked by
investors:
· Several investors have asked whether we currently anticipate
needing to raise additional equity.
o The answer is no, we do not currently anticipate needing to raise
additional equity. We ended Q4 with $13.8 million in cash and short-term
investments, slightly down from $14.8 million last quarter, but that was
largely due to timing, as we had $2.4 million in cash receipts from major
customers collected right after the New Year, and we generated over $4 million
of Adjusted EBITDA in Q4 alone. Nonetheless, to support our growth and future
working capital requirements, we have a revolving line of credit with Wells
Fargo that provides up to $10 million of financing, 100% of which was
available under our borrowing base as of the end of Q4. We have not yet drawn
down on the Wells Fargo line. We anticipate generating enough cash from
operations in 2024 to fund our capital needs without having to draw down on
the Wells Fargo facility.
· Several investors have asked why we have no Chief Technology
Officer.
o In a sense we actually have four chief technology officers, or at least
their equivalents, each of which manage a specific technology area: we have a
PhD in computer science and AI who heads our AI labs research team and data
science teams; we have an SVP of engineering overseeing product and platform
engineering; we have another VP focused on software development and product
evolution for our Agility product; and we have a Chief Information Security
Officer who heads security and infrastructure. Under these leaders, we have
close to 300 developers, architects, infrastructure managers and data
scientists. We have found that this structure best supports the breadth and
scale of our business.
· Investors have asked us to share our recent spending on software
and product development, and why do we not separately disclose it, and to
comment on whether we have a significant spend on cloud infrastructure.
o In terms of our spending across software and product development, over the
last five years, we spent about $26 million. This peaked in 2022 at $8.9
million and came down to $6.4 million in 2023. However, since roughly 80%
percent of our business is managed services, we do not view the aggregate
spending across these areas as a focal point for investors. In terms of cloud,
we spend a couple of million dollars per year, mostly for software,
infrastructure and data hosting. It is our Big Tech customers, not us, that
spend massively on GPUs for training foundation models.
· Other investors have asked us how they should think about our
comps. Specifically, they asked whether our comps are the largest technology
and software companies in the world and whether they should compare our
R&D spend and Cloud compute spend to these companies.
o These companies are absolutely not our comps. Rather many of these
companies constitute part of our target market. We are not in their business
and, to state the obvious, we are not of similar scale. Players in this market
are building foundation models, and we are providing services to this market
that help them on their journey. Therefore, we do not believe that comparing
our R&D spend and Cloud compute spend to theirs is especially useful. We
view our competition as companies focused on AI data engineering services to
this market.
· Another question we've gotten is how did we manage to pivot to AI
without having to raise substantial capital?
o There are essentially three reasons we were able to pivot to AI without
having to raise capital. The first reason, which we believe is by far the most
important, is that the massive spend we read about being required to build
foundation models is incurred by our large tech customers, not by us. Our
customers are deploying extensive amounts of capital for cloud compute, for
data science, and for data engineering - three crucial ingredients to an LLM,
if you will. We provide the kinds of data engineering services they need, and
providing data engineering does not require that we separately incur compute
costs. The second reason we were able to transition to AI data engineering
without incurring massive upfront costs is that we have been a data
engineering company for over 20 years, and we were able to repurpose a lot of
what we already had in place, including management, resources, facilities, and
technologies, to serve the AI use cases. The third reason is that when we
began exploring AI back in 2016 and developing our Goldengate infrastructure
we incurred manageable investment. From a data perspective, because we were
already employing large teams of resources doing customer work, we did not
have to incur incremental additional costs for humans-in-the-loop. We simply
had to rearchitect our operator workbenches and to create the right data
lakes. The objectives we initially set for the models we built were to enable
us to reduce costs associated with maintaining rules-based data processing
technologies. We were not seeking to automate the work of humans, but to
augment it. Over the years, Goldengate, one of our proprietary platforms,
became, we believe, state-of-the-art at things like entity extraction, data
categorization and document zoning - all important aspects of what we do. We
use the technology in customer deployments and within our own platforms with
great results. That said, Goldengate is not ChatGPT - you can't converse with
it or ask it to perform magical feats like writing poetry. Goldengate has 50
million parameters, while ChatGPT is reputed to have 1.7 trillion parameters.
Nevertheless, Goldengate demonstrates that AI can be trained to perform
specific tasks very well without incurring massive spending; that AI
deployments leveraging open source algorithms and models can be within reach
for many enterprises for industry-specific datasets; and that for business
implementations especially, data engineering is more important than sheer
model size as a predictor of performance.
· A question we got recently is "How does revenue per employee
compare in your different lines of business?"
o The answer is that revenue per employee is lowest in our managed services
business, while it is multiple times higher in our AI data engineering scaled
services. Regardless, we target an adjusted gross margin of 35 to 37% across
these business lines, so we believe adjusted gross margin is the better metric
to track. In our software business, our targeted gross margin is anticipated
to be about 73% this year, and we intend to target a consolidated adjusted
gross margin of between 40 and 43%.
· Another question we've gotten several times recently is "Is
Agility now profitable?"
o The answer is yes. In this quarter, Agility posted Adjusted EBITDA of $1.2
million. This was a 69% sequential increase over Q3. We think we executed the
Agility business very well in 2023, growing it 15% in a difficult macro
environment. It had a strong adjusted gross margin of 69% over 2023 as a whole
and 74% in Q4. We also love what we've done with the product - we believe
we've taken a leadership position as the first end-to-end public relations and
media intelligence platform to integrate generative AI."
Marissa Espineli, Interim CFO, added, "Other questions we've gotten recently
from investors have been:
· We've been asked about why we keep cash overseas.
o The reason we keep cash overseas is to cover operating expenses in these
locations. We do not plan to repatriate these funds nor do we foresee the need
to.
· We've been asked recently about our cost-plus transfer pricing
agreements with our offshore subsidiaries.
o Companies that have revenue in, say, North America or Europe, but have
offshore delivery centers in countries like India and the Philippines, put in
place what's called transfer pricing arrangements to satisfy the arm's length
transaction principle. Under a transfer pricing arrangement, a percentage of
revenue is allocated to the delivery center. The percentage allocated is often
determined by statute or regulation in the foreign country. We understand that
the reason the foreign country does this is to make sure there are profits at
the local level for it to tax. When the consolidated enterprise is losing
money, and would not otherwise have to pay taxes, it unfortunately ends up
having to pay taxes offshore. Obviously, paying taxes when you are losing
money is not a good thing and is referred to as "tax leakage" - but even in
this situation, the tax we pay is insignificant versus the money we save by
operating offshore.
· We've been asked whether there is any structural reason that
Innodata would be expected to lose more money as it generates more revenue?
o The answer to this is absolutely not. As Innodata revenue increases, we
expect that its Adjusted EBITDA will increase at an even higher percentage.
This is because there is some operating leverage in our direct costs, for
things like production facilities, and significant operating leverage in our
general and administrative operating costs. We saw clear evidence of this in
both Q3 and Q4. In Q3, revenue grew sequentially by $2.5 million and Adjusted
EBITDA grew sequentially by $1.6 million. Similarly, in Q4, revenue grew
sequentially by $3.9 million and Adjusted EBITDA grew sequentially by $1.1
million. There will however, be quarterly fluctuations in how much revenue
falls to the EBITDA line based on how we flex our operating expenses,
particularly our sales and marketing efforts, based on market dynamics."
Timing of Conference Call with Q&A
Innodata will conduct an earnings conference call, including a
question-and-answer period, at 5:00 PM eastern time today. You can participate
in this call by dialing the following call-in numbers:
The call-in numbers for the conference call are:
1-888-506-0062 (Domestic)
+1 973-528-0011 (International)
Participant Access Code - 383451
1-877-481-4010 (Domestic Replay)
+1 919-882-2331 (International Replay)
Replay Passcode - 49773
It is recommended that participants dial in approximately 10 minutes prior to
the start of the call. Investors are also invited to access a live Webcast of
the conference call at the Investor Relations section of www.innodata.com
(https://pr.report/gTaRnVbq) . Please note that the Webcast feature will be in
listen-only mode.
Call-in or Webcast replay will be available for 30 days following the
conference call.
About Innodata
Innodata (NASDAQ:INOD) 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
business processes. Our low-code Innodata AI technology platform is at the
core of our offerings. In every relationship, we honor our 30+ year legacy
delivering the highest quality data and outstanding service to our customers.
Visit www.innodata.com (https://pr.report/gTaRnVbq) to learn more.
Forward Looking Statements
This press release may contain certain forward-looking statements within the
meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and
Section 27A of the Securities Act of 1933, as amended. These forward-looking
statements include, without limitation, statements concerning our operations,
economic performance, and financial condition. Words such as "project,"
"believe," "expect," "can," "continue," "could," "intend," "may," "should,"
"will," "anticipate," "indicate," "predict," "likely," "estimate," "plan,"
"potential," "possible," "promises," or the negatives thereof, and other
similar expressions generally identify forward-looking statements.
These forward-looking statements are based on management's current
expectations, assumptions and estimates and are subject to a number of risks
and uncertainties, including, without limitation, impacts resulting from the
continuing conflict between Russia and the Ukraine and Hamas' attack against
Israel and the ensuing conflict; investments in large language models; that
contracts may be terminated by customers; projected or committed volumes of
work may not materialize; pipeline opportunities and customer discussions
which may not materialize into work or expected volumes of work; the
likelihood of continued development of the markets, particularly new and
emerging markets, that our services support; the ability and willingness of
our customers and prospective customers to execute business plans that give
rise to requirements for our services; continuing reliance on project-based
work in the Digital Data Solutions (DDS) segment and the primarily at-will
nature of such contracts and the ability of these customers to reduce, delay
or cancel projects; potential inability to replace projects that are
completed, canceled or reduced; continuing DDS segment revenue concentration
in a limited number of customers; our dependency on content providers in our
Agility segment; difficulty in integrating and deriving synergies from
acquisitions, joint ventures and strategic investments; potential undiscovered
liabilities of companies and businesses that we may acquire; potential
impairment of the carrying value of goodwill and other acquired intangible
assets of companies and businesses that we acquire; a continued downturn in or
depressed market conditions; changes in external market factors; changes in
our business or growth strategy; the emergence of new, or growth in existing
competitors; various other competitive and technological factors; our use of
and reliance on information technology systems, including potential security
breaches, cyber-attacks, privacy breaches or data breaches that result in the
unauthorized disclosure of consumer, customer, employee or Company
information, or service interruptions and other risks and uncertainties
indicated from time to time in our filings with the Securities and Exchange
Commission.
Our actual results could differ materially from the results referred to in
forward-looking statements. Factors that could cause or contribute to such
differences include, but are not limited to, the risks discussed in Part I,
Item 1A. "Risk Factors," Part II, Item 7. "Management's Discussion and
Analysis of Financial Condition and Results of Operations," and other parts of
our Annual Report on Form 10-K, filed with the Securities and Exchange
Commission on February 24, 2023, as updated or amended by our other filings
that we may make with the Securities and Exchange Commission. In light of
these risks and uncertainties, there can be no assurance that the results
referred to in the forward-looking statements will occur, and you should not
place undue reliance on these forward-looking statements. These
forward-looking statements speak only as of the date hereof.
We undertake no obligation to update or review any guidance or other
forward-looking statements, whether as a result of new information, future
developments or otherwise, except as may be required by the Federal securities
laws.
Company Contact
Marcia Novero
Innodata Inc.
Mnovero@innodata.com (mailto:Mnovero@innodata.com)
(201) 371-8015
Non-GAAP Financial Measures
In addition to the financial information prepared in conformity with U.S. GAAP
("GAAP"), we provide certain non-GAAP financial information. We believe that
these non-GAAP financial measures assist investors in making comparisons of
period-to-period operating results. In some respects, management believes
non-GAAP financial measures are more indicative of our ongoing core operating
performance than their GAAP equivalents by making adjustments that management
believes are reflective of the ongoing performance of the business.
We believe that the presentation of this non-GAAP financial information
provides investors with greater transparency by providing investors a more
complete understanding of our financial performance, competitive position, and
prospects for the future, particularly by providing the same information that
management and our Board of Directors use to evaluate our performance and
manage the business. However, the non-GAAP financial measures presented in
this press release have certain limitations in that they do not reflect all of
the costs associated with the operations of our business as determined in
accordance with GAAP. Therefore, investors should consider non-GAAP financial
measures in addition to, and not as a substitute for, or as superior to,
measures of financial performance prepared in accordance with GAAP. Further,
the non-GAAP financial measures that we present may differ from similar
non-GAAP financial measures used by other companies.
Adjusted EBITDA
We define Adjusted EBITDA as net income (loss) attributable to Innodata Inc.
and its subsidiaries in accordance with U.S. GAAP before interest expense,
income taxes, depreciation and amortization of intangible assets (which
derives EBITDA), plus additional adjustments for loss on impairment of
intangible assets and goodwill, stock-based compensation, income (loss)
attributable to non-controlling interests, non-recurring severance, and other
one-time costs.
We use Adjusted EBITDA to evaluate core results of operations and trends
between fiscal periods and believe that these measures are important
components of our internal performance measurement process.
A reconciliation of Adjusted EBITDA to the most directly comparable GAAP
measure is included in the tables that accompany this release.
INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS
(Unaudited)
(In thousands, except per-share amounts)
Three Months Ended Year Ended
December 31 December 31
2023 2022 2023 2022
Revenues $ 26,112 $ 19,375 $ 86,775 $ 79,001
Operating costs and expenses:
Direct operating costs 15,948 12,740 55,482 51,533
Selling and administrative expenses 8,203 8,355 30,975 37,940
Interest expense, net 57 9 179 11
24,208 21,104 86,636 89,484
Income (loss) before provision for income taxes 1,904 (1,729 ) 139 (10,483 )
Provision for income taxes 248 229 1,028 1,522
Consolidated net income (loss) 1,656 (1,958 ) (889 ) (12,005 )
Income (loss) attributable to non-controlling interests 4 2 19 (70 )
Net income (loss) attributable to Innodata Inc. and Subsidiaries $ 1,652 $ (1,960 ) $ (908 ) $ (11,935 )
Income (loss) per share attributable to Innodata Inc. and Subsidiaries:
Basic $ 0.06 $ (0.07 ) $ (0.03 ) $ (0.44 )
Diluted $ 0.05 $ (0.07 ) $ (0.03 ) $ (0.44 )
Weighted average shares outstanding:
Basic 28,728 27,392 28,131 27,278
Diluted 31,983 27,392 28,131 27,278
INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED BALANCE SHEETS
(Unaudited)
(In thousands)
December 31, 2023 December 31, 2022
ASSETS
Current assets:
Cash and cash equivalents $ 13,806 $ 9,792
Short term investments - other 14 507
Accounts receivable, net 14,288 9,528
Prepaid expenses and other current assets 3,969 3,858
Total current assets 32,077 23,685
Property and equipment, net 2,281 2,511
Right-of-use asset, net 5,054 4,309
Other assets 2,445 1,498
Deferred income taxes, net 1,741 1,475
Intangibles, net 13,758 12,526
Goodwill 2,075 2,038
Total assets $ 59,431 $ 48,042
LIABILITIES, NON-CONTROLLING INTERESTS AND STOCKHOLDERS' EQUITY
Current liabilities:
Accounts payable, accrued expenses and other $ 9,245 $ 9,880
Accrued salaries, wages and related benefits 7,799 6,136
Income and other taxes 3,848 3,230
Long-term obligations - current portion 1,261 877
Operating lease liability - current portion 782 693
Total current liabilities 22,935 20,816
Deferred income taxes, net 22 65
Long-term obligations, net of current portion 6,778 5,079
Operating lease liability, net of current portion 4,701 4,036
Total liabilities 34,436 29,996
Non-controlling interests (708 ) (727 )
STOCKHOLDERS' EQUITY 25,703 18,773
Total liabilities, non-controlling interests and stockholders' equity $ 59,431 $ 48,042
INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF CASH FLOWS
(Unaudited)
(In thousands)
Year Ended
December 31,
2023 2022
Cash flows from operating activities:
Consolidated net loss $ (889 ) $ (12,005 )
Adjustments to reconcile consolidated net loss to net cash
provided by operating activities:
Depreciation and amortization 4,716 3,889
Stock-based compensation 4,027 3,283
Deferred income taxes (276 ) 217
Provision for doubtful accounts 426 480
Pension cost 1,046 943
Loss on lease termination - 125
Changes in operating assets and liabilities:
Accounts receivable (5,116 ) 1,303
Prepaid expenses and other current assets 372 (226 )
Other assets (171 ) 750
Accounts payable, accrued expenses and other (490 ) 322
Accrued salaries, wages and related benefits 1,653 (310 )
Income and other taxes 605 13
Net cash provided by (used in) operating activities 5,903 (1,216 )
Cash flows from investing activities:
Capital expenditures (5,564 ) (6,526 )
Proceeds from (purchase of) short term investments - others 493 (507 )
Net cash used in investing activities (5,071 ) (7,033 )
Cash flows from financing activities:
Proceeds from exercise of stock options 3,324 332
Payment of long-term obligations (452 ) (639 )
Net cash provided by (used in) financing activities 2,872 (307 )
Effect of exchange rate changes on cash and cash equivalents 310 (554 )
Net increase (decrease) in cash and cash equivalents 4,014 (9,110 )
Cash and cash equivalents, beginning of year 9,792 18,902
Cash and cash equivalents, end of year $ 13,806 $ 9,792
INNODATA INC. AND SUBSIDIARIES
RECONCILIATION OF GAAP TO NON-GAAP FINANCIAL MEASURES
(Unaudited)
(In thousands)
Three Months Ended December 31, Year Ended December 31,
Consolidated 2023 2022 2023 2022
Net income (loss) attributable to Innodata Inc. and Subsidiaries $ 1,652 $ (1,960 ) $ (908 ) $ (11,935 )
Provision for income taxes 248 229 1,028 1,522
Interest expense 105 9 400 11
Depreciation and amortization 1,237 1,053 4,716 3,889
Severance** - - 580 -
Stock-based compensation 1,029 913 4,027 3,283
Non-controlling interests 4 2 19 (70 )
Adjusted EBITDA (loss) - Consolidated $ 4,275 $ 246 $ 9,862 $ (3,300 )
Three Months Ended December 31, Year Ended December 31,
DDS Segment 2023 2022 2023 2022
Net income (loss) attributable to DDS Segment $ 974 $ (501 ) $ 223 $ (711 )
Provision for income taxes 246 228 1,018 1,423
Interest expense 104 9 395 10
Depreciation and amortization 351 211 1,161 694
Severance** - - 33 -
Stock-based compensation 986 760 3,511 2,690
Non-controlling interests 4 2 19 4
Adjusted EBITDA - DDS Segment $ 2,665 $ 709 $ 6,360 $ 4,110
Three Months Ended December 31, Year Ended December 31,
Synodex Segment 2023 2022 2023 2022
Net income (loss) attributable to Synodex Segment $ 238 $ (282 ) $ 219 $ (2,525 )
Depreciation and amortization 144 174 623 656
Severance** - - 6 -
Stock-based compensation (10 ) 130 167 258
Non-controlling interests - - - (74 )
Adjusted EBITDA (loss) - Synodex Segment $ 372 $ 22 $ 1,015 $ (1,685 )
Three Months Ended December 31, Year Ended December 31,
Agility Segment 2023 2022 2023 2022
Net income (loss) attributable to Agility Segment $ 440 $ (1,177 ) $ (1,350 ) $ (8,699 )
Provision for income taxes 2 1 10 99
Interest expense 1 - 5 1
Depreciation and amortization 742 668 2,932 2,539
Severance** - - 541 -
Stock-based compensation 53 23 349 335
Adjusted EBITDA (loss) - Agility Segment $ 1,238 $ (485 ) $ 2,487 $ (5,725 )
** Represents non-recurring severance incurred for a reduction in headcount in
connection with the re-alignment of the Company's cost structure.
INNODATA INC. AND SUBSIDIARIES
CONSOLIDATED REVENUE BY SEGMENT
(Unaudited)
(In thousands)
Three Months Ended December 31, Year Ended December 31,
2023 2022 2023 2022
Revenues:
DDS $ 19,646 $ 13,579 $ 61,576 $ 56,523
Synodex 1,807 1,729 7,511 7,105
Agility 4,659 4,067 17,688 15,373
Total Consolidated $ 26,112 $ 19,375 $ 86,775 $ 79,001
SOURCE: Innodata Inc.
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