Q1 2023 Innodata Inc Earnings Call
Greetings.
Welcome to <unk> first quarter 2023 earnings call at this time, all participants are in a listen only mode.
Question and answer session will follow the formal presentation.
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Please note. This conference is being recorded I will now turn the conference over to your host Amy Hey, Chris you may begin.
Thank you John Good afternoon, everyone. Thank you for joining us today, our speakers today are Jack Apple <unk> CEO of <unk> data and the race is spinelli interim CFO , we'll hear from Jack first who will provide perspective about the business and then a reasonable follow with a review of our <unk>.
Results for the first quarter will then take your questions first let me qualify the forward looking statements that are made during the call. These statements are being made pursuant to the safe Harbor provisions of section 21 E of.
Of the Securities Exchange Act of 1934 as amended and section 27, a of the Securities Act of 1933 as amended forward looking statements include without limitation any statement that may predict forecast indicate Brian by future results.
Performance or achievements. These statements are based on management's current expectations assumptions and estimates and are subject to a number of risks and uncertainties, including without limitation, the expected or potential effects of the novel Coronavirus, COVID-19 pandemic and the responses.
The government the general global population, our customers and our company. There are two impacts resulting from the rebound rapidly evolving conflicts between Russia, and Ukraine investments in large language models that contracts may be terminated by customers projected or committed volumes of work may.
Not materialize.
Client opportunities and customer discussions, which may not materialize into work our expected volumes of work that goes up our new capabilities.
And you mean digital data solutions segment reliance on project based work and the primarily at will nature of such contracts and the ability of these customers to reduce delay or cancel projects. The likelihood of continued development of the markets, particularly new and emerging markets that our services and.
<unk> support.
Digital data solutions segment revenue concentration in a living in number of customers potential inability to replace projects that are completed canceled or reduced our dependency on content providers in our agility segment continued downturn in or depressed market conditions.
Changes in external market factors, the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services and solutions 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 changes in our business our growth strategy, the emergence of new or growth in existing competitors are used up and.
Our reliance on information technology system, including potential security breaches cyber attacks privacy breaches or data breaches that result in the unauthorized disclosure of consumer customer employee or company information, our service interruptions and various other competitive.
And technological factors and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission, including our most recent reports on forms 10-K, 10-Q, and 8-K and any amendments thereto, we undertake no obligation to update forward looking.
Perforation or to announce revisions to any forward looking statements, except as required by the federal securities laws and actual results could differ materially from our current expectations. Thank you I will now turn the call over to Jack.
Good afternoon, everybody. Thank you for joining our call.
As you probably saw in our announcement earlier today, we have quite a lot of exciting news to share with you.
Over the last couple of weeks, we received verbal confirmation from two of the largest global technology companies.
We have been selected to provide data engineering.
Fishing programs and generative AI technology behind <unk> G. P T.
One of these companies is an existing customer and the other one will be a new customer.
In addition, the third company also a new customer and another of the largest five global technology companies.
Indicating that they are likely to choose us and we have just reached agreement with them on terms of a master services agreement.
We believe these accomplishments are potentially transformative for David.
For these companies, we expect to be performing a range of data engineering work required to build cutting edge generative AI.
We expect this potentially to include creating training datasets that are used to train the models.
Providing instruction datasets that teach the models to follow instructions.
Providing reinforcement learning the process by which we aligned models with human values and complex use cases.
And providing red teaming and model performance evaluation.
This involves potentially working in multiple languages and across several data modalities.
I will be focusing today's call first our view of the industry landscape and the tremendous change we believe generative AI will unleash.
<unk> operate worldwide.
Second how we are positioning in a data to take advantage of this tremendous change.
Third while we believe we have been able to get rapid traction with the largest tech companies as they seek to build out their generative AI capabilities and our growth drivers going forward.
Morris, how we're thinking about the potential cadence of the financial impact from these wins, both this year and beyond.
First and last our Q1 results and forward guidance.
We believe the agenda today, I will be even more transformative for businesses and consumers than the internet.
And that's because of the tremendous productivity advantages, we believe that technology will provide as well as an innovative customer experience. We believe it will provide.
Virtually every company in the World will have to incorporate this technology to be competitive.
When the internet burst into the collective consciousness with netscape's IPO in 1995.
Most people could see the intranets potential.
But the basic infrastructure like broadband AD servers logistics et cetera, we're not yet been in place, resulting in massive capital destruction subsequently as revenues relate incoming and monetization capabilities were slow to develop.
Of course, when that infrastructure was built over time it created several of the most valuable companies in the world and revolutionize how business is conducted.
But by contrast in the case of Genesis II, we believe the adoption cycle and benefits of adoption will potentially be much more quickly realized.
We expect this will be driven by the massive productivity increases adoption of generative AI will likely provide.
Recent paper by M. I T referenced to 37% increase in productivity among workers using <unk> versus using their legacy processes.
This is an industrial revolution level leap in human productivity.
The adoption of generative AI in our opinion may do for the service sector with the steam engine and electric Florida.
Just real sector.
We believe our immediate opportunity is with large cap tech companies as we announced today that are in a race to build the generative AI foundation as well as new entrants companies like anthropic character.
You mean that are raising massive amounts of capital to enter the race.
These companies plan on generating revenues from providing the foundational layer of generative.
And licensing that technology to third parties to build their own AI applications for their specific niches and use cases.
We believe that the adoption by these licensees will potentially be very rapid potentially much more rapid than the internet.
Because the application of this technology is primarily focused on enhancing productivity and because the infrastructure and the ecosystem needed for its implementation is readily available in Peru.
Moreover, companies may likely feared that they do not incorporate these capabilities, they're resulting relatively higher cost structures may render them uncompetitive to say nothing of the customer experience, which will seem so yesterday.
The second opportunity do for in the data is to work with these large technology companies, providing data engineering services to their end customers that help their end customers build solutions on this foundation models or just fine tune their own versions of the foundation models.
We see the opportunity to provide these services on both the side by side basis and on a white label basis.
We are in active discussions with two of these companies about doing just that.
We hope to have progress to report on this front by our next call.
The third opportunity is to help large businesses build their own proprietary generative AI models.
We see this as likely becoming increasingly attractive to large businesses that have unique large datasets.
We believe this will become increasingly a viable path based on the like the kind of evolution of three predictions. We are making first that high performing commercially usable open source generally today models will become increasingly available.
Second that progressively more effective techniques for model fine tuning will become available.
And third the marginal cost of compute will overtime significantly reduced thanks to innovations and Gpus and other systems.
We're already seeing evidence of this we believe for example, we are close to signing a master services agreement with a leading investment bank that earlier. This year are deployed 40 data scientists to tame L O EMS in the context of their data there.
<unk> vision is to enable their people to quote share with their data much the way that people quarry databases today.
Yes.
There are essentially three main areas of cost and building generative AI.
Costs of building the model the cost of compute and the clustered data engineering.
As models commoditize fine tuning cycles become less expensive than compute costs come down overtime, we expect that large businesses. We will see the remaining cost data engineering is a high R O a investment.
Moreover, we expect it will become economic for them to address progressively more ambitious and sophisticated use cases.
We believe that the data engineering work that will be required including data collection creation curation pre processing for L. O M training and testing will become the key to unlocking the potential competitive advantage that their proprietary data represents.
This transition to our third point why do we believe and the data has been able to gain such rapid traction with leading companies focused on this new arms race.
I would say, sometimes it is better to be lucky than smart.
We did not predict that generative AI would burst onto the scene as quickly as it has nor are we architects of the system.
But our long history of building capabilities for other applications.
But you are now directly applicable to generative AI, we believe has put us in an enviable position.
We have in place scalable domain expertise in diverse areas, including material Sciences, agriculture, biology mess legal in pharma with thousands of subject matter experts around the world.
We have a proven reputation for creating consistently high quality datasets and complex subject areas rice with such activity.
We have a global reach enabling us to work in 40 plus languages.
We have the technical acumen, we have developed over the past seven years around AI model training and deployment.
And we have developed flexible platforms, two IDE to digest and ingest data to auto entity data with zero shot learning and to track quality metrics in real time.
We believe the technology choices, we made starting back in 2016, and 2017 turned out to be tailor made for regenerative AI.
For example, we chose encoder based transformer architectures that supported generative AI.
At the time this was hardly an obvious choice while our first model is the only had 50 million parameters versus for example, today's GPT for that is reported to have one trillion parameters.
Central architecture is what we settled on seven years ago.
Moreover, our experience taught us.
Howard to deploy models into real world production environments in ways that are safe and value creating.
So maybe it was not all luck.
In addition over the past several years, we have been integrating both generative and classical AI into real world customer operations workflows and platforms.
We have developed recipes and technologies for building and configuring fine tunable <unk> and overcoming safety interests challenges.
More recently, we have developed deep expertise in prompt engineering and prompt shaming important skills required to deploy foundation models.
As we worked with our largest customers customers on the implementation side is we hope to and was a large businesses undergoing alone basis.
As we plan to we believe our experience will both be repurposed symbol and invaluable.
Fourth I want to give a framework for the customer wins and positive indication other wind, we announced today and broadly speak to the potential magnitude of these accomplishments and how we believe they could show up in our results.
These three customers.
Notified us over the past 10 days.
As I mentioned earlier, but it's worth repeating each of these three customers is in the list of the top five global Tech firms one is an existing customer while the remaining two are new.
With our existing customer we are in the process of putting in place the work order for a new win with.
With the two new customers. We are now in the process of putting in place formal agreements and finalizing initial scope.
Well, we are confident that agreements will be inked until this happens there's always the chance that it does not.
The deals are potentially large two illustrates one of the new customers has indicated that it will cut in an initial purchase order for $2 5 million to get us started but it will be supplementing that as we move forward.
They also shared with us their vision for where the initial scope of work might go which is fully realized we believe could result in approximately $12 million of new quarterly revenues at maturity.
Moreover, the draft contracts that are now being worked on our at our customers' request framework agreements that enable them to easily add scope.
Again, I want to emphasize that deals with the two new potential customers have not yet been signed but were expecting to get them signed and some details to be worked out over the next few weeks.
Based on our experience we believe that these engagements will ramp up over the course of several months.
Typically once an agreement is finalized we worked with the customer to create detailed specifications and run pilots to ensure that the specifications are yielding the intended results.
Oftentimes this requires several iterations once the specification is locked down our next step is to put in place the required infrastructure.
This includes custom configuring technology systems, finalizing process designs and assembling human resources data engineers and subject matter experts.
This can take two to three months typically.
Once this is completed ramp up begins we typically ramp up slowly so that we can continue to test and refine it as necessary with the customer.
Ramp up itself can take three to six months to achieve steady state.
The duration of the engagement will depend upon many factors, including the size of the model being built and the amount of ongoing updating and tuning that will be taking place. These are often not knowable in advance.
This is a dynamic process with customer dependencies, and checkpoints through rep, which makes it tough to do for the quarterly forecast.
But based on our experience. The end result that gets us to full ramp up is typically achieved in a roughly 12 month period.
Okay.
Finally, let's talk about our Q1 results and guidance in Q1 revenue was $18 8 million and adjusted EBITDA was $800000.
There was no revenue in the quarter from the three deals we are announcing today all of which happened in just the past couple of weeks well after Q1 close.
It is also worth knowing that there was no revenue in the quarter from the large social media company, which contributed $4 $4 million in revenue in Q1 of last year, but dramatically pulled back spending in the second half of last year as it underwent a significant management change.
If we back out revenue from this large social media company, our revenue growth in the quarter over Q1, 2022 would have been 12%.
We believe that it is possible that business from this customer could resume in the second half, but our 2023 business plan does not account for this upside.
Our 2023 business plan also does not account for revenue from the two new customers, we have spoken about today.
Even without these elements incorporated we expect that exiting this year, our revenue growth rate could potentially be high teens or in the twenties.
We back out from 2022 of the large social media company, we just discussed.
And that our adjusted EBITDA run rate could potentially exceed $15 million on an annualized basis.
We ended the quarter with a healthy balance sheet, no appreciable debt and $10 8 million in cash and short term investments on the balance sheet.
I'll now turn the call over to <unk> to go over the numbers and we will then open the line for questions.
Hey, Jack Good afternoon, everyone allow me to provide a recap of our Q1 results revenue for the quarter and that might start to 190 293 was $18 8 million compared to revenue of 21 2 million in the same period last year.
As Jack mentioned the comparative periods included four 4 million in revenue for applied Social media company that underwent a significant management change in second half of last year as a result, it dramatically pull back spending across the board.
There was no revenue from this company in the quarter ended March 31st 2023.
Net loss by the acquired their ended March 31, 2023 was $2 1 million or eight cents per basic and diluted.
Diluted share compared to net loss of $2 eight or 10 cents per basic and diluted share in the same period last year.
Our adjusted EBITDA was <unk>.
8 million in the first quarter of 2023 compared to adjusted EBIT loss of 1 million in the same period last year.
Cash and cash equivalents, including short term investments or a 10 8 million at March 31st 2023, and $10 3 million.
As of December 31st 2022.
Again, thanks, everyone. John we are now ready to take questions.
Thank you.
At this time, we will be conducting a question and answer session. If you would like to ask a question. Please press star one on your telephone keypad.
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Okay.
Okay and our first question comes from Tim.
Clarkson with van Clemens. Please proceed.
Hey, Jack our exciting news I'm, just kind of a basic question, but my customers always ask it's I'll ask for them.
How many other companies could potentially do the kind of stuff youre doing in in AI.
Keep your question Tim.
I mean, I don't have a real account on that there are several companies that we do run up against that.
Our are talked about in the marketplace.
I can tell you that on one of the three customers that are.
Yeah.
Who's likely window, we're announcing today.
We were.
Told that we were competing against 17 companies and that competition has been going on for several months. It was starting with 17. It got narrowed to four and then as I said, it's looking very good in terms of.
Being able to call that a win in getting that signed so I guess theres at least 17 that are entering the fray.
I expect that there'll be many more I think that there is a.
A very exciting opportunity, which you know as I.
I presented a little bit of today.
<unk>.
And I think it's you know it's fortunate.
The residue somewhat of luck and some good decisions and planning that we're in the position that we are it's very exciting and I believe transformative potentially for the company.
Okay. So is there a kind of a follow on question. What what are the factors you think that are allowing you to get these contracts.
In competitive situations.
I think when it all comes down to is.
Skills technology capabilities and culture.
We've got a culture of being very agile.
Very able to move quickly and to respond to customer demands customer centricity understanding what people want listening really really super carefully to their needs.
And kind of embedding us.
As parts of that team.
When it comes to some of the things that I mentioned on the call today.
We're fortunate to have the skills.
Ready to repurpose that are exactly what is required in order to to build these technologies.
Scalable domain expertise the ability to create and the proven ability to create high quality very consistent datasets and complex subjects 40 languages.
The technical acumen, having.
Built the recipes and the technologies to do AI model training and fine tuning and having chosen the right architectures over the years.
I think all of these are playing a great part in in.
The track record that we're now establishing.
Look you know unlike thrilled three out of three.
Looking very strongly.
And I think we're I'd like to think we're just getting started.
Right.
Supposedly when Microsoft did their check GPT supposedly a lot of the annotation was done in Kenya for $3 an hour I mean is there a difference in the kind of annotation you guys do.
Versus that kind of annotation.
Yes, there is a great deal of difference.
Some of the early models that have been built kind of go very broad, but very narrow theres not a great deal of agitation that's required a much of what we do is.
The complex stuff its going deep into subject matter domain, it's going deep into use cases.
I think it's the next phase of what will be required both among the large tech companies as well as other companies that are looking to own that foundation layer.
As well as large businesses, who are saying look we've got.
The tremendous amount of.
Data and information that's proprietary we're not looking to serve that data up to the foundation models via their apos, we want to control. This.
And I think that will afford them, probably make a ton of sense in light of the developments that are taking place with.
Kind of the Commoditization of the technology itself and the availability of it all of that puts us in a great position to be able to be helpful.
To be able to create greater ROI on these capabilities, but it will probably become table Stakes.
Right right now and what level of revenues do you think youre going to have to add employees would it be a $25 million a quarter or a 30 mile corridor or $35 million a quarter at what level would you think you'd have to add employees.
Yeah.
So I think I think it's going to depend a lot on exactly what we're going to end up being doing.
There are people that are in our cost of goods and we typically don't keep those people around.
In terms of services work that we do so there are people that will be added but I think important thing is that.
That's kind of in Cogs I think from a.
Sure.
Perspective of infrastructure of core operations, we've got what we need to begin executing on these contracts.
There isn't something that needs to be built there isn't something that we need to go acquire in order to execute so we're ready to go ahead.
Sure now you I saw that you got a line of credit what how does that fit into the business situation.
Yeah. So you know we thought that it was the right thing to do.
To have that in place we were anticipating that we might win one of these large opportunities.
That it would be useful in terms of helping us manage working capital.
That said it looks like we've got potentially a great chance of having having won all three.
And then there's more behind that hopefully.
Coming so I think it's from a corporate.
Housekeeping perspective from a corporate governance perspective, it's the right thing to have in place and I'm very happy to say that we have in place and we've had great. We have a great relationship with Wells Fargo that has started as a result of that.
Very happy to be working with them.
Sure and any color on on Senate Ericsson and agility.
Yeah sure you know there.
But they're doing well.
The agility perspective, we had.
A 4% sequential growth rate in the quarter that annualized to like a 17% growth rate. We did a lot of cost cutting there, but what we were able to do was.
No too to really hone in on and focus our resources on the best performing.
<unk>.
50% of the sales force.
Theyre doing wonderfully right now, we're very happy with that.
Sample, we just bagged a $200000 per year subscription deals with large automotive company and agility.
Rebuilt the PR copilot degenerative AI model that we launched within the platform in January it was a first mover within that industry.
We've got great strong customer reception, a super cool roadmap for success of releases around that this year.
And we're using that to like build a strong standalone asset as well as a playground to be able to show other customers well here's what you can do here is a way you can integrate these technologies.
Think about creating value from them in a safe and trusting way today.
Alright, what about Senate X.
Oh, yes, sure so <unk>.
Do we have like I think it was 8% sequential growth, which would translate into a 36% annualized growth rate.
A lot of the effort that we're doing this year is an expanding addressable market with.
New product models and new development.
We're working really closely with a.
A couple of very large.
Life insurance companies as charter customers testing new capabilities in disability claims processing.
Long term care claims processing.
Personal injuries and.
Increasingly.
AI is becoming an integrated feature of that capability as well.
Great well, thanks, I'll drop out of the queue, let some other people ask some questions. Thanks.
Great.
Once again, if you have a question or comment please indicate so by pressing star one. The next question is from Dana Bouska with Feltl. Please proceed.
Hi, Jack how are you today.
Doing great Dana.
That's what you are saying and you wrote in your press release, just sounds amazing.
I I have a question around.
The large language models with them with them being so new it sounds to me like you're creating the roadmap as you go.
And I was which I think is very very exciting place to be.
And I was just wondering what type of advantages does that give you that you are over your competitors that you're working with these large companies being should.
Should we say the first move mover doing this type of stuff.
Yeah, I think it gives us.
Potentially a tremendous advantage.
<unk>.
And again, we see opportunities kind of on two sides of the sensor one is helping companies build these models and the second is helping helping them deploy and utilize the technology.
So in the work that we're doing with the large <unk>.
Companies were getting exposed to all sorts of new technologies, we are creating.
Seeing things before they well before they come out potentially.
And.
That gives us the ability to help companies kind of think through how they will deploy things in.
In addition, as we go about these tests, we're building new capabilities.
We're going to be building, new technologies, and new systems and through that process of identifying weaknesses in existing technologies and trying to innovate around that so I think with everything that we do we become stronger and we become better.
My prediction that is behind these large tech companies. There is another 40, either well funded startups or large tech companies, who are going to be closely following we're having conversations with several of them.
And then beyond that I think companies that are large companies with significant proprietary information assets.
No.
Over time have the ability and be able to make a use case for building <unk> themselves and again I think we will be very well positioned to partner with them to do that.
So.
That we are winning these are critical it will accelerate our innovation to accelerate our capabilities and make us more valuable progressively as we go from there.
Okay.
Excellent that's it.
Also sounds wonderful.
And an amazing.
I recently saw a paper that was out of Stanford talking about being able to use large language models to do reinforced learning.
Is that something that you think.
But.
Beat some type of issue for you or do you do not consider that to be a.
Threats to your business.
Yes, I missed the terminate reusing Stanford study that shows that it can be used for what exactly.
Tampered University that said that large language models can be used to do reinforced learning.
Reinforcement learning yeah reinforcement learning sorry, yeah, yeah. So reinforcement learning is sometimes called <unk> H F.
It was basically used as part of one of the processes to build the models and to fine tune the models.
Youre basically comparing the output of language model to human generated pairs of questions and answers and then you are modifying the internal variables to favor the responses that are similar to the human responses. So thats inherent process in the model fine tuning itself.
Okay.
And then when we start when you when you start looking at building like your prompt engineering practice.
Do you have those people on staff right now or is that something someplace, where you're going to have to start hiring people.
How do you look at you know.
Creating these new.
Almost new jobs.
That you're going to need these new positions that youre going to need to do.
To fulfill your ambitions.
Yes, it's a great question.
We're finding that.
The skills do not exist in surplus in the world.
As shown.
Several weeks ago, a job at four <unk>.
Hiring a prompt engineer for $250000, a year, which I don't know if that was real or not but.
It made its way around the internet.
We're taking people that have been with us for a long time within our engineering group, they're figuring out prompt engineering. There is no real book written about prompt engineering or product management or prompt shiny.
But we're building techniques and one of the great things about our.
Our company is we've got.
So many real world applications fused technologies. So it's not academic it's not theoretical were like figuring out how do you deploy deploy this.
Safely and in a trustworthy way into real world production scenarios.
And from that there is a tremendous amount of learning that we're capturing.
And can repurpose for our customers' continued benefit.
Yes.
Excellent I think I saw that same article.
That was floating around the internet about prompt engineering.
But that does it for me thank you.
Thanks, Dan I appreciate it.
We have reached the end of the question and answer session and I will now turn the call over to Jack for closing remarks.
Operator, thank you so yeah I'll quickly recap today, we're announcing that we have received verbal wins from two of the top five global tech companies and.
We are a strong indication of a win from a third.
These three companies to our new customers.
These are all companies that are widely expected to forged the path forward and generative AI development over the next several years.
Last quarter, we caution you that these are just pipeline and that pipeline often does not close this quarter. We're proud to say that to we're now verbally confirmed wins in that we've got a strong indication from the third but it also is likelier win.
We hope to have.
Each of these papers in the next few weeks.
We believe that these new deals, perhaps individually, but certainly in the aggregate present, a potential transformative opportunity for our company.
As you know we have a solid track record of land and expand with large tech companies and now with the additional tailwind of regenerative AI. We think we are extraordinarily well positioned.
We believe the revenue growth opportunity with these companies is significant in the near medium and long term perspectives. We're also excited about the endorsement. We believe these new wins and accomplishments represent for us.
We believe virtually every company out there will need to become an AI company over the next several years and we believe that we will be well positioned to help them do just that.
I also want to say that we plan on stepping up our investor relations activities in the second half of the year.
We plan to be presenting at several investor conferences, and we will announce these up once plans are firmed up.
So again, thank you everybody for joining us today.
We'll be looking forward to our next call with you.
This concludes today's conference and you may disconnect. Your lines at this time. Thank you for your participation.