Innodata Inc (INOD) 2022 Q4 法說會逐字稿

Greetings. Welcome to Innodata's fourth quarter and fiscal year 2022 earnings call. (Operator Instructions) Please note this conference is being recorded. I will now turn the conference over to your host, Amy Agress. You may begin.

問候。歡迎來到 Innodata 的第四季度和 2022 財年財報電話會議。 （操作員說明）請注意正在錄製此會議。我現在將會議轉交給主持人 Amy Agress。你可以開始了。

Thank you, John. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata; and Marissa Espineli, Interim CFO. We'll hear from Jack first, who will provide perspective about the business, and then Marissa will follow with a review of our results for the fourth quarter and the 12 months ended December 31, 2022. We'll then take your questions.

謝謝你，約翰。大家下午好。感謝您今天加入我們。我們今天的演講者是 Innodata 的首席執行官 Jack Abuhoff；和臨時首席財務官 Marissa Espineli。我們將首先聽取傑克的意見，他將提供有關業務的觀點，然後瑪麗莎將回顧我們第四季度和截至 2022 年 12 月 31 日的 12 個月的業績。然後我們將回答您的問題。

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 21E of the Securities Exchange Act of 1934 as amended and Section 27A of the Securities Act of 1933 as amended. Forward-looking statements include, without limitation, any statement that may predict, forecast, indicate, or imply future results, performance, or achievements.

首先，讓我對通話期間所做的前瞻性陳述進行限定。這些聲明是根據經修訂的 1934 年證券交易法第 21E 節和經修訂的 1933 年證券法第 27A 節的安全港條款作出的。前瞻性陳述包括但不限於任何可能預測、預測、指示或暗示未來結果、業績或成就的陳述。

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 novel coronavirus COVID-19 pandemic and the responses of governments, the general global population, our customers, and the company thereto; impacts resulting from the rapidly evolving conflict 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; pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work; acceptance of our new capabilities; continuing 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 market, particularly new and emerging markets, that our services and solutions support; continuing digital data solutions segment revenue concentration in the limited number of customers; potential inability to replace projects that are completed, canceled, or reduced; our dependency on content providers in our Agility segment; a continued downturn in or depressed market conditions, whether as a result of the COVID-19 pandemic or otherwise; 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 impairments of the carrying value of goodwill and other acquired intangible assets of companies and businesses that we may acquire; changes in our business or growth strategy; the emergence of new or growth in existing competitors; 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, customers, employee, or company information or 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 Form 10-K, 10-Q, and 8-K and any amendments thereto.

這些陳述基於管理層當前的預期、假設和估計，並受到許多風險和不確定性的影響，包括但不限於新型冠狀病毒 COVID-19 大流行的預期或潛在影響以及政府的反應、全球總體人口、我們的客戶和公司；俄羅斯和烏克蘭之間迅速演變的衝突造成的影響；對大型語言模型的投資，合同可能會被客戶終止，預計或承諾的工作量可能無法實現；可能無法實現工作或預期工作量的管道機會和客戶討論；接受我們的新能力；持續數字數據解決方案細分市場對基於項目的工作的依賴以及此類合同的主要隨意性質以及這些客戶減少、延遲或取消項目的能力；我們的服務和解決方案支持的市場，特別是新興市場持續發展的可能性；持續的數字數據解決方案部門收入集中在數量有限的客戶中；可能無法替代已完成、取消或減少的項目；我們在敏捷領域對內容提供商的依賴；無論是由於 COVID-19 大流行還是其他原因，市場狀況持續低迷或低迷；外部市場因素的變化；我們的客戶和潛在客戶執行對我們的服務和解決方案提出要求的業務計劃的能力和意願；難以從收購、合資和戰略投資中整合和獲得協同效應；我們可能收購的公司和企業的潛在未發現負債；我們可能收購的公司和企業的商譽和其他已收購無形資產的賬面價值的潛在減值；我們的業務或增長戰略發生變化；現有競爭對手的新出現或增長；我們對信息技術系統的使用和依賴，包括導致未經授權披露消費者、客戶、員工或公司信息或服務中斷的潛在安全漏洞、網絡攻擊、隱私洩露或數據洩露；以及我們不時向美國證券交易委員會提交的文件中指出的各種其他競爭和技術因素以及其他風險和不確定性，包括我們關於 10-K、10-Q 和 8-K 表格的最新報告及其任何修訂.

We undertake no obligation to update forward-looking information 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.

我們沒有義務更新前瞻性信息或宣布對任何前瞻性陳述的修訂，除非聯邦證券法要求。實際結果可能與我們目前的預期存在重大差異。

Thank you. I will now turn the call over to Jack.

謝謝。我現在將電話轉給傑克。

Thank you, Amy. Good afternoon, everybody. Thank you for joining our call. Today, I'm going to talk briefly about our Q4 and year-end results. And then I'm going to spend some time discussing recent acceleration in AI investment by large technology companies in large language models coinciding with OpenAI's fourth-quarter release of its large language model called ChatGPT and how we believe Innodata is quite well positioned to capitalize on this increased investment.

謝謝你，艾米。大家下午好。感謝您加入我們的電話。今天，我將簡要談談我們的第四季度和年終業績。然後我將花一些時間討論大型技術公司最近在大型語言模型中加速人工智能投資，恰逢 OpenAI 第四季度發布其名為 ChatGPT 的大型語言模型，以及我們認為 Innodata 如何很好地利用這增加了投資。

So first, our results. Q4 revenue was $19.4 million, a 5% increase over the prior quarter, which annualizes roughly to a 22% growth rate. We posted positive adjusted EBITDA of approximately $250,000 in the quarter, which was a positive swing of $1.5 million from Q3.

首先，我們的結果。第四季度收入為 1940 萬美元，比上一季度增長 5%，年化增長率約為 22%。我們公佈本季度調整後的 EBITDA 為正，約為 250,000 美元，比第三季度增加了 150 萬美元。

This significant improvement resulted primarily from our September, October cost containment and efficiency initiatives. The benefits of these initiatives will be fully reflected in our first-quarter 2023 results.

這一重大改進主要得益於我們 9 月、10 月的成本控制和效率舉措。這些舉措的好處將充分體現在我們 2023 年第一季度的業績中。

We ended the year with a healthy balance sheet, no appreciable debt, and $10.3 million in cash and short-term investments on the balance sheet. In 2022, overall, we grew revenues 13% despite the significant revenue decline from our large social media customer that underwent significant internal disruption in the second half of the year. But that, we believe, may normalize this year.

我們以健康的資產負債表結束了這一年，沒有明顯的債務，資產負債表上有 1030 萬美元的現金和短期投資。總體而言，2022 年我們的收入增長了 13%，儘管我們的大型社交媒體客戶在下半年經歷了嚴重的內部中斷，收入大幅下降。但我們相信，這可能會在今年正常化。

Let's now shift to the recent substantial uptick we are seeing in our market activity. As most everyone now knows, in late Q4, OpenAI unveiled ChatGPT. This AI large language model has since gone viral, capturing popular imaginations for its ability to write, to generate computer code, and to converse at what seems like human or even superhuman levels of intelligence.

現在讓我們轉向最近我們在市場活動中看到的大幅上漲。現在大多數人都知道，在第四季度末，OpenAI 推出了 ChatGPT。這種 AI 大型語言模型已經流行起來，因其能夠以類似於人類甚至超人類的智能水平進行書寫、生成計算機代碼以及進行對話的能力而俘獲了大眾的想像力。

We believe the release of ChatGPT has been broadly seen as a watershed event, potentially heralding a fundamental advancement in the way AI can drive changes in business communication, processes, and productivity.

我們認為，ChatGPT 的發布已被廣泛視為一個分水嶺事件，它可能預示著人工智能在推動業務溝通、流程和生產力變革方面的根本性進步。

Our market intelligence indicates that many large tech companies are accelerating their AI investments as they compete for domination in building and commercializing large language models and that an arms race of sorts is now forming. We believe that the significant investment that will likely result from this competition could dramatically accelerate the performance of these large language models.

我們的市場情報表明，許多大型科技公司正在加速他們的 AI 投資，因為他們在構建和商業化大型語言模型方面競爭主導地位，並且現在正在形成某種形式的軍備競賽。我們相信，這場比賽可能帶來的重大投資可能會顯著加快這些大型語言模型的性能。

As a result of this dramatic increase in performance, we expect almost every industry will face fundamental reinvention. We believe that the opportunity for Innodata in all of this is significant and that it is now upon us.

由於性能的顯著提高，我們預計幾乎每個行業都將面臨根本性的重塑。我們相信，Innodata 在所有這些方面的機會都是巨大的，而且現在就在我們身上。

We believe our opportunity is actually threefold: first, to help large technology companies, both existing customers and new customers, compete in this large language model arms race; second, to help businesses incorporate large language models into their products and operations; and third, to integrate these technologies into our own platforms.

我們相信我們的機會實際上有三個方面：首先，幫助現有客戶和新客戶的大型科技公司參與這場大型語言模型軍備競賽；第二，幫助企業將大型語言模型融入到他們的產品和運營中；第三，將這些技術整合到我們自己的平台中。

Let's take each of these in turn, starting with what I just laid out as our first opportunity, helping technology companies, both existing customers and new customers, compete in the large language model arms race.

讓我們依次考慮其中的每一個，從我剛剛提出的第一個機會開始，幫助現有客戶和新客戶的技術公司在大型語言模型軍備競賽中競爭。

While ChatGPT and a host of lesser-known-but-equally-impressive large language models are for sure amazing, our view is that it's still early days. We believe these large language models have room for significant improvements in output quality, in the languages they serve, in the domains they support, and in terms of safety. These are all challenges that we believe we can help with.

雖然 ChatGPT 和許多鮮為人知但同樣令人印象深刻的大型語言模型確實令人驚嘆，但我們認為它仍處於早期階段。我們相信這些大型語言模型在輸出質量、它們所服務的語言、它們支持的領域以及安全性方面都有顯著改進的空間。這些都是我們相信可以幫助應對的挑戰。

We expect to help by collecting large-scale, real-world data for training; by creating high-quality synthetic data when real-world data training is hard to come by; by annotating training data; and by providing reinforcement learning from human feedback or RLHF to fine-tune model performance and eliminate hallucinations, which is the tendency of these models to make things up on the fly.

我們希望通過收集大規模、真實世界的數據進行培訓來提供幫助；在難以獲得真實數據訓練時創建高質量的合成數據；通過註釋訓練數據；並通過提供來自人類反饋或 RLHF 的強化學習來微調模型性能並消除幻覺，這是這些模型即時彌補問題的趨勢。

In addition, we expect to help by minimizing the risk that models generate unsafe or biased results, and we expect to help by hyper-training generalized models for specialized domains. High-quality data is at the root of addressing all of these challenges, and this is and has been Innodata's bread and butter specialty for 30 years.

此外，我們希望通過將模型生成不安全或有偏見的結果的風險降至最低來提供幫助，並且我們希望通過針對專業領域的超訓練通用模型來提供幫助。高質量的數據是應對所有這些挑戰的根本，這也是 Innodata 30 年來的主要業務。

We believe that the arms race to which I'm referring has likely already begun. In just the past few weeks, it seems, activity for us has dramatically surged. We are now either expanding work with, beginning work with, or discussing working with four of the five largest technology companies in the world.

我們認為我所指的軍備競賽可能已經開始。就在過去幾週，我們的活動似乎急劇增加。我們現在正在與世界上最大的五家科技公司中的四家擴大合作、開始合作或討論合作。

I'm going to share some examples of the surge in activity we've seen in just the past few weeks. A major cloud provider, whose AI needs we began serving 24 months ago, engaged us to help them build a new large-scale generative AI model for images. We started the initial phase of this just this week. In addition, the customer asked us just last week to kick off a pilot to support their generative AI large language model development. We started the pilot this week.

我將分享我們在過去幾週看到的活動激增的一些例子。我們在 24 個月前開始滿足其 AI 需求的一家大型雲提供商聘請我們幫助他們構建一個新的大規模圖像生成 AI 模型。我們就在本週開始了這個的初始階段。此外，客戶就在上週要求我們啟動試點，以支持他們的生成式 AI 大型語言模型開發。我們本週開始了試點。

With the same customer, also in the last few weeks, we expanded our synthetic data program to support its large language model development. We believe high-quality synthetic data is likely to be a key ingredient to performing -- to a high-performing large language models of the future.

對於同一個客戶，也是在過去幾週，我們擴展了我們的合成數據程序以支持其大型語言模型開發。我們相信高質量的合成數據可能是未來高性能大型語言模型的關鍵要素。

Synthetic data is entirely new data that we generate through a machine-assisted process to match real-world data and maintain all of the statistical properties of real-world data, which is especially useful for capturing rare cohorts and outliers of interest. Synthetic data is also helpful to correct for data bias, to improve algorithmic fairness, and to avoid having to retrain proprietary or confidential data.

合成數據是我們通過機器輔助過程生成的全新數據，用於匹配真實世界的數據並保持真實世界數據的所有統計屬性，這對於捕獲稀有隊列和感興趣的異常值特別有用。合成數據還有助於糾正數據偏差，提高算法的公平性，並避免必須重新訓練專有或機密數據。

We started working with synthetic data back in 2022, and we've been continually improving our capabilities and technologies for synthetic data creation since then. With this customer, we've gone from serving just one of their product lines to now being firmly engaged with three product lines. And we are in pilots with three additional product lines.

早在 2022 年，我們就開始使用合成數據，從那時起，我們一直在不斷改進我們創建合成數據的能力和技術。有了這個客戶，我們已經從只為他們的一個產品線提供服務到現在與三個產品線緊密合作。我們正在試驗另外三個產品線。

Also in the past couple of weeks, with another of the world's largest tech companies -- this one a company that would be a new customer for us -- we've gotten a verbal commitment to assist them on projects relating to large language models. They have told us that they are in the final stages of putting in place a statement of work.

同樣在過去幾週，我們與世界上最大的另一家科技公司——這家公司將成為我們的新客戶——達成口頭承諾，協助他們開展與大型語言模型相關的項目。他們告訴我們，他們正處於製定工作說明的最後階段。

While there can be no assurance that the SOW is put in place, based on our current estimations and assumptions, value of this program could potentially approach approximately $1.8 million per year run rate in its initial phases and could ramp up to approximately $6 million per year as it gains momentum.

雖然不能保證 SOW 已經到位，但根據我們目前的估計和假設，該計劃的價值在其初始階段可能接近每年約 180 萬美元，並可能增加到每年約 600 萬美元隨著它獲得動力。

In addition, two weeks ago, one of the world's largest social media companies, another potential new customer for us, reached out to discuss how we might potentially support its large-scale model development. It has been referred to us by one of our existing customers who apparently said that we could be helpful in unlocking value, unlocking scale, and -- by bringing a consultative approach to a partnership.

此外，兩週前，世界上最大的社交媒體公司之一，我們的另一個潛在新客戶，與我們聯繫，討論我們如何可能支持其大規模模型開發。我們的一位現有客戶已將其轉介給我們，他們顯然表示，我們可以幫助釋放價值、擴大規模，以及——通過為合作夥伴關係引入諮詢方法。

We believe that the opportunities I've just mentioned, individually and in the aggregate, are potentially very large. I want to underscore that several of these are pipeline opportunities and at various stages of pipeline from early stage to late stage. Pipeline opportunities are inherently difficult to forecast and often do not close.

我們相信，我剛才提到的機會，無論是單獨的還是總體上，都可能非常大。我想強調的是，其中一些是管道機會，並且處於從早期到後期的管道的各個階段。管道機會本來就很難預測，而且通常不會關閉。

That said, I've offered them here in support of two beliefs: the first belief, that there is building momentum among big tech companies for AI innovation generally and large language model specifically; and the second belief, that Innodata's reputation for high-quality work with high-quality outcomes is becoming firmly instantiated in a dynamic market that is viewing us as a potential partner in one of our generation's greatest innovations.

也就是說，我在這裡提供它們是為了支持兩個信念：第一個信念，即大型科技公司在人工智能創新和特定的大型語言模型方面正在形成勢頭；第二個信念是，Innodata 在高質量工作和高質量成果方面的聲譽正在一個充滿活力的市場中得到堅定的體現，這個市場將我們視為我們這一代最偉大創新之一的潛在合作夥伴。

Now, let's shift to our second significant market opportunity. We believe that our second significant market opportunity is to help businesses harness the power of these foundational generative AI models. Most enterprises have tasks that generative AI can make easier. As the technology improves, and we expect it will, we believe that businesses will see incorporating the technology as a must-have rather than a nice-to-have.

現在，讓我們轉向第二個重要的市場機會。我們認為，我們的第二個重要市場機會是幫助企業利用這些基礎生成式 AI 模型的力量。大多數企業都有生成式 AI 可以簡化的任務。隨著技術的改進（我們預計會如此），我們相信企業會將採用該技術視為必備技術，而不是可有可無的好東西。

Analysts are predicting that this year, the most forward-thinking business leaders will be actively putting time and money into reimagining their products, service delivery, and operations based on what AI can do for them, leading to widespread deployments over 2024 and 2025. What we are also hearing, especially from CTOs, is that their biggest roadblock to deploying AI is finding the right engineers and data scientists to help them get there.

分析師預測，今年最具前瞻性的商業領袖將積極投入時間和金錢，根據人工智能的作用重新構想他們的產品、服務交付和運營，從而在 2024 年和 2025 年實現廣泛部署。我們還聽說，尤其是 CTO，他們部署 AI 的最大障礙是找到合適的工程師和數據科學家來幫助他們實現目標。

We believe our opportunity will be to do just that to help them get there. We anticipate that this will take the form of fine-tuning existing pre-trained large language models on specific tasks within specific domains, bringing expertise in prompt engineering, the art of prompting large language models to produce the appropriate results, and helping with large language model application integration.

我們相信我們的機會就是這樣做，幫助他們實現目標。我們預計這將採取的形式是針對特定領域內的特定任務微調現有的預訓練大型語言模型，帶來提示工程方面的專業知識，促使大型語言模型產生適當結果的藝術，並幫助大型語言模型應用集成。

Early in the first quarter of 2023, a large financial technology company expanded scope with us to leverage our proprietary AI models more fully and re-engineer their technology for the cloud to drive operational efficiencies. Our proprietary AI engine, Goldengate, uses the same underlying encoder-decoder transformer neural network architecture as GPT.

2023 年第一季度初，一家大型金融科技公司與我們一起擴大了範圍，以更充分地利用我們專有的 AI 模型，並重新設計他們的雲技術以提高運營效率。我們專有的 AI 引擎 Goldengate 使用與 GPT 相同的底層編碼器-解碼器轉換器神經網絡架構。

While GPT is trained broadly, Goldengate is trained narrowly on specific tasks and domains. We have experimented with coupling GPT and Goldengate, and this seems to result in even higher orders of performance. This is the third scope expansion we've had with this company over the course of the past six months, again, providing further validation of our land-and-expand strategy.

GPT 的訓練範圍很廣，而 Goldengate 的訓練範圍很窄，只針對特定的任務和領域。我們已經嘗試將 GPT 和 Goldengate 結合起來，這似乎會帶來更高的性能。這是我們在過去六個月中與該公司進行的第三次範圍擴張，再次為我們的土地擴張戰略提供了進一步的驗證。

We believe our third opportunity is to harness GPT and other large language models in our own AI industry platforms. Just last month, we announced PR CoPilot, a new module within our Agility PR platform that combines proprietary Innodata technology and GPT to enable communications professionals to generate first drafts of press releases and media outreach in record time.

我們相信我們的第三個機會是在我們自己的 AI 行業平台中利用 GPT 和其他大型語言模型。就在上個月，我們發布了 PR CoPilot，這是我們 Agility PR 平台中的一個新模塊，它結合了專有的 Innodata 技術和 GPT，使傳播專業人士能夠在創紀錄的時間內生成新聞稿和媒體宣傳的初稿。

With our release of PR CoPilot, we became, we believe, the PR industry's first integrated platform to incorporate large language model technology. The implementation was significant for Innodata, and we received a supportive write-up in PR Weekly for it.

我們相信，隨著 PR CoPilot 的發布，我們成為了 PR 行業第一個整合大型語言模型技術的集成平台。實施對 Innodata 來說意義重大，我們在 PR Weekly 上收到了一篇支持它的文章。

The start-up named Jasper vaulted to unicorn status when it implemented something very similar to PR CoPilot for creating blogs and social media postings. Their efforts got them a $125 million Series A round on a healthy $1.5 billion valuation.

名為 Jasper 的初創公司一躍成為獨角獸，因為它實施了與 PR CoPilot 非常相似的東西來創建博客和社交媒體帖子。他們的努力使他們以 15 億美元的健康估值獲得了 1.25 億美元的 A 輪融資。

With respect to our Agility platform, we're seeing positive momentum in key performance indicators, which we think PR CoPilot and our newly integrated social media listening product will help to further accelerate. In Q4, Agility platform sales grew 6% over Q3, which annualizes to a roughly 26% growth rate.

關於我們的 Agility 平台，我們看到了關鍵績效指標的積極勢頭，我們認為 PR CoPilot 和我們新集成的社交媒體聆聽產品將有助於進一步加速。第四季度，Agility 平台銷售額比第三季度增長 6%，年增長率約為 26%。

In 2022, overall, our direct sales new logo bookings increased by 83% year over year. And our direct sales net retention increased to 100%. In 2022, overall, approximately 83% of our Agility revenue came from direct sales, and 17% of our revenue came from channel partners. In Q4, our conversion from demo to win in direct sales increased to 33%, up from approximately 18% at the beginning of the year.

總體而言，在 2022 年，我們的直銷新標識預訂量同比增長了 83%。我們的直銷淨保留率增加到 100%。總體而言，到 2022 年，我們大約 83% 的 Agility 收入來自直接銷售，17% 的收入來自渠道合作夥伴。在第四季度，我們在直接銷售中從演示到獲勝的轉化率從年初的約 18% 增加到 33%。

We believe the notion that customers who use us love us is also very much apparent in our Synodex platform. Synodex grew by 71% in 2022, with a net retention of 168%.

我們相信，使用我們的客戶喜歡我們的觀念在我們的 Synodex 平台中也非常明顯。 Synodex 在 2022 年增長了 71%，淨保留率為 168%。

We announced in Q4 that one of our large Synodex customers had expanded its recurring revenue program with us. In the announcement, we stated that the expansion was valued at approximately $600,000, but we now believe the value of the expansion is actually closer to $1.2 million. This is now our second largest Synodex customer, with an estimated annual recurring revenue base of $2.3 million.

我們在第四季度宣布，我們的一位 Synodex 大客戶已經與我們一起擴大了其經常性收入計劃。在公告中，我們表示擴建的價值約為 600,000 美元，但我們現在認為擴建的價值實際上接近 120 萬美元。現在，這是我們的第二大 Synodex 客戶，估計每年的經常性收入基數為 230 萬美元。

This year, we will be focused on product development to expand our addressable market for medical data extraction. We've got new products currently being evaluated by charter customers in disability claims processing, personal injury claims processing, and long-term care claims processing, as well as in clinical medical data annotation and fully automated life underwriting. Integrated AI will be a feature in all of these products.

今年，我們將專注於產品開發，以擴大醫療數據提取的潛在市場。我們的新產品目前正在接受殘疾索賠處理、人身傷害索賠處理和長期護理索賠處理，以及臨床醫療數據註釋和全自動人壽承保方面的特許客戶評估。集成人工智能將成為所有這些產品的一個特點。

We are more enthusiastic than ever about our market opportunity and the intrinsic value of our business. In our last call, we said we anticipated expanding our adjusted EBITDA to $10 million or more in 2023 and, at the same time, capturing significant growth opportunities. We believe the activity we are now seeing in our markets will likely enable us to achieve this and potentially more.

我們比以往任何時候都更加熱衷於我們的市場機會和我們業務的內在價值。在我們上次電話會議中，我們表示我們預計到 2023 年將調整後的 EBITDA 擴大到 1000 萬美元或更多，同時抓住重要的增長機會。我們相信，我們現在在市場上看到的活動很可能使我們能夠實現這一目標，並有可能實現更多目標。

I'll now turn the call over to Marissa to go over the numbers, and then we'll open the line for some questions.

我現在將電話轉給瑪麗莎，讓她檢查一下數字，然後我們將打開熱線詢問一些問題。

Thank you, Jack. Good afternoon, everyone. Let me recap the fourth-quarter and fiscal year 2022 financial results.

謝謝你，傑克。大家下午好。讓我回顧一下第四季度和 2022 財年的財務業績。

Our revenue for the quarter ended December 31, 2022, was $19.4 million compared to revenue of $19.3 million in the same period last year. Our net loss for the quarter ended December 31, 2022, was $2 million or $0.07 per basic and diluted share compared to a net loss of $1.2 million or $0.04 per basic and diluted share in the same period last year.

我們截至 2022 年 12 月 31 日的季度收入為 1940 萬美元，而去年同期為 1930 萬美元。截至 2022 年 12 月 31 日止季度，我們的淨虧損為 200 萬美元或每股基本股和攤薄股 0.07 美元，而去年同期淨虧損為 120 萬美元或每股基本股和攤薄股 0.04 美元。

The total revenue for the year ended December 31, 2022, was $79 million, up 13% from revenue of $69.8 million in 2021. Net loss for the year ended December 31, 2022, was $12 million or $0.44 per basic and diluted share compared to a net loss of $1.7 million or $0.06 per basic and diluted share in 2021.

截至 2022 年 12 月 31 日止年度的總收入為 7900 萬美元，比 2021 年的 6980 萬美元增長 13%。截至 2022 年 12 月 31 日止年度的淨虧損為 1200 萬美元或每股基本和稀釋後每股虧損 0.44 美元2021 年淨虧損 170 萬美元或每股基本股和攤薄股 0.06 美元。

Adjusted EBITDA was $2 million in the fourth quarter of 2022 compared to adjusted EBITDA of $0.3 million in the same period last year. Adjusted EBITDA loss was $3.3 million for the year ended December 31, 2022, compared to adjusted EBITDA of $3 million in 2021.

2022 年第四季度調整後的 EBITDA 為 200 萬美元，而去年同期調整後的 EBITDA 為 30 萬美元。截至 2022 年 12 月 31 日止年度，調整後 EBITDA 虧損為 330 萬美元，而 2021 年調整後 EBITDA 虧損為 300 萬美元。

Our cash and cash equivalents and short-term investments were $10.3 million at December 31, 2022, consisting of cash and cash equivalents of $9.8 million and short-term investment of $0.5 million, and $18.9 million at December 31, 2021, consisting of cash and cash equivalents.

截至 2022 年 12 月 31 日，我們的現金及現金等價物和短期投資為 1030 萬美元，包括現金和現金等價物 980 萬美元和短期投資 50 萬美元，截至 2021 年 12 月 31 日為 1890 萬美元，包括現金和現金等價物。

So thanks, everyone. John, we are now ready for questions.

所以謝謝大家。約翰，我們現在準備好提問了。

(Operator Instructions) Tim Clarkson, Van Clemens.

（操作員說明）蒂姆克拉克森，范克萊門斯。

Hi, Jack. Apologize if you hear noise in the background. We had a major snowstorm in Minneapolis, and the snow is coming off our building, so anyhow. The first question I have is, what exactly is the technology experience that makes Innodata successful at moving forward artificial intelligence in these chatbots?

嗨，傑克。如果您聽到背景噪音，請道歉。我們在明尼阿波利斯遭遇了一場大暴風雪，我們大樓的積雪正在脫落，無論如何。我的第一個問題是，使 Innodata 在這些聊天機器人中成功推進人工智能的技術經驗到底是什麼？

Sure, Tim. Well, it's not limited to chatbots. Artificial intelligence, people believe and I firmly believe, is at a kind of a fundamental inflection point. We're now seeing the kinds of technologies that people have dreamt about probably since the 1950s.

當然，蒂姆。好吧，它不僅限於聊天機器人。人們相信，我也堅信，人工智能正處於一種根本性的轉折點。我們現在看到的是人們可能自 1950 年代以來就夢想的各種技術。

And when you think about building these technologies and think about what goes into them, it's not programming in the traditional sense. It's data. It's high-quality data and data that can help to address some of the fundamental problems that these technologies have.

當您考慮構建這些技術並考慮其中包含的內容時，這不是傳統意義上的編程。是數據。高質量的數據和數據可以幫助解決這些技術存在的一些基本問題。

They need to improve their output quality. They need to improve languages they're supporting. They need to be customized for particular domains. They need to improve what we think of as safety, the kinds of responses, the kinds of things that they tell us.

他們需要提高輸出質量。他們需要改進他們支持的語言。它們需要針對特定域進行定制。他們需要改善我們認為的安全性、反應類型以及他們告訴我們的事情類型。

So well, what needs to be done for that? The things that need to be done are the things fundamentally that we've done for a very long time very, very successfully for some of the largest companies out there. When we were being retained by the large engagements we've had in the past, things like Apple, what we were doing for them was fundamentally building large quality data or high-quality data for products and for publishing.

好吧，為此需要做什麼？需要做的事情基本上是我們已經為一些最大的公司做了很長時間非常非常成功的事情。當我們被過去的大型活動留住時，比如 Apple，我們為他們做的是從根本上為產品和發布構建大量質量數據或高質量數據。

Here, we're building it because data is the programming language of AI and the programming language of large language models.

在這裡，我們正在構建它，因為數據是 AI 的編程語言，也是大型語言模型的編程語言。

Right, right. And what typically are the gross margins on the revenues you're getting from this? Are these high-gross-margin products?

是的是的。您從中獲得的收入的毛利率通常是多少？這些是高毛利產品嗎？

I think from a gross margin perspective, I would continue to expect a range of gross margins from our different capabilities. In the services sector, I think mid-30s to mid-40s gross margins are achievable, and they'll get better over time. As we introduce automations and technology, they tend to drift higher.

我認為從毛利率的角度來看，我會繼續期望我們不同能力的毛利率範圍。在服務行業，我認為 30 到 40 年代中期的毛利率是可以實現的，而且隨著時間的推移會變得更好。當我們引入自動化和技術時，它們往往會漂移得更高。

When we're starting up new projects, they tend to drift a little bit lower. On the platform side, they're higher than that. Incremental gross margins especially can be very substantial. And as we build scale and start to scale on our fixed costs, we can start to see the kinds of gross margins that will emerge from those kinds of business models.

當我們開始新項目時，它們往往會偏低一點。在平台方面，它們比這更高。增量毛利率尤其可能非常可觀。隨著我們建立規模並開始擴大我們的固定成本，我們可以開始看到從這些商業模式中產生的毛利率。

Right, right. Well, just one last comment. I've been all excited about Innodata lately. And the analogy I use is it's -- what's happening with artificial intelligence is -- before, it was like da Vinci seeing a picture of an airplane. But it's one thing to see a picture of an airplane. It's another one to see one fly by you and go from Minneapolis to New York.

是的是的。好吧，最後一個評論。最近我對 Innodata 非常興奮。我用的類比是——人工智能正在發生的事情是——以前，就像達芬奇看到飛機的照片一樣。但看到飛機的照片是一回事。看到一架飛機從你身邊飛過，從明尼阿波利斯飛往紐約，這是另一回事。

And when you actually see this artificial intelligence stuff work, you no longer have to be sold on the value. I mean, it's magical. So that's for me is -- that's the difference -- is people are really excited about the end product. And emotion is what drives, ultimately, decision making, and there's real excitement behind this. So with that, I'm done. Thanks.

當你真正看到這種人工智能的東西發揮作用時，你就不再需要被其價值出賣了。我的意思是，這很神奇。所以這對我來說是——這就是區別——人們對最終產品真的很興奮。最終，情緒是決策的驅動力，而這背後是真正的興奮。因此，我完成了。謝謝。

Thank you, Tim.

謝謝你，蒂姆。

Dana Buska, Feltl & Company.

Dana Buska, Feltl & Company。

Hi, Jack. How are you today?

嗨，傑克。你今天怎麼樣？

Dana, I'm doing great. Thank you. Welcome to the call.

丹娜，我過得很好。謝謝。歡迎來電。

You're welcome. Yeah, thank you for taking my questions. My first question is with your new PR CoPilot. Is -- you talked about your own technology, and I was just wondering if your technology is more than just accessing an API at OpenAI?

不客氣。是的，謝謝你回答我的問題。我的第一個問題是關於你的新 PR CoPilot。是——你談到了你自己的技術，我只是想知道你的技術是否不僅僅是訪問 OpenAI 的 API？

So it is. I mean, it's -- basically, what we're doing -- and we've got a very exciting road map. We really believe we've only just got started with ChatGPT. But what we're doing is we're combining an API with prompt engineering that we've done behind our UI and behind the scenes.

就是這樣。我的意思是，基本上，我們正在做的事情——我們有一個非常令人興奮的路線圖。我們真的相信我們才剛剛開始使用 ChatGPT。但我們正在做的是將 API 與我們在 UI 和幕後完成的提示工程相結合。

And in the future, what we're going to be doing is we're going to be enriching the training data to more specifically perform within the domain. We've also done other things in terms of that, and we will be doing other things in order to further enrich the experience.

在未來，我們要做的是豐富訓練數據，以便在領域內更具體地執行。我們還就此做了其他事情，我們將做其他事情以進一步豐富體驗。

So when you take the two fundamental use cases we're addressing -- one for writing press releases and one for writing media outreach -- we're looking at what goes on in someone's head when they're looking to do either one of those things. What connections are they making? Where are they having to go and research? And built into our product and built into our underlying data model are lots of connections that we're able to harvest and bring then into the prompts in order to create a more precise level of output.

因此，當您採用我們正在處理的兩個基本用例時——一個用於編寫新聞稿，一個用於編寫媒體宣傳——我們正在研究某人在尋求執行其中任何一個時的想法事物。他們在建立什麼聯繫？他們要去哪裡研究？內置於我們的產品和內置於我們的基礎數據模型中的是我們能夠收穫的大量連接，然後將其帶入提示中，以創建更精確的輸出級別。

So it's very much a combination of these things. Now one of the reasons -- I'm excited for several reasons. But one of the things that it is enabling us to do is to create a lot of value for PR but, at the same time, learn a lot about how do you integrate these technologies to create a superior customer experience. And we're able to bring that experience in turn to the work that we're doing for other customers.

所以它是這些東西的結合。現在，原因之一——我很興奮有幾個原因。但它使我們能夠做的一件事是為 PR 創造大量價值，同時，了解很多關於如何整合這些技術以創造卓越的客戶體驗的知識。我們能夠將這種體驗反過來應用到我們為其他客戶所做的工作中。

So it's great, fun, and it really is a new frontier.

所以它很棒，很有趣，而且它確實是一個新領域。

That sounds wonderful. I was wondering if you were going to be able to apply your CoPilot technology to other industries or other companies or other applications?

聽起來不錯。我想知道您是否能夠將您的 CoPilot 技術應用於其他行業、其他公司或其他應用程序？

So we're certainly talking to other companies about this. We're looking at some opportunities to apply large language models within our Synodex platform. That's kind of early days though, so I wouldn't -- I'd encourage you not to expect anything out of that very quickly.

因此，我們當然正在與其他公司討論此事。我們正在尋找在我們的 Synodex 平台中應用大型語言模型的機會。不過，那還為時過早，所以我不會——我鼓勵你不要很快期待任何結果。

We think the road map, though, for PR CoPilot is fairly extensive. And we've really only begun down that path. So we're very excited about that.

不過，我們認為 PR CoPilot 的路線圖相當廣泛。我們真的才剛剛開始走這條路。所以我們對此感到非常興奮。

Excellent. With Agility, where are you projecting breakeven for that is going to be?

出色的。有了 Agility，您預計盈虧平衡點在哪裡？

So I don't think we've put out a number relative to that. But what we've said is that we think we are going to be getting that business to become an adjusted EBITDA-positive business in the first half of the year.

所以我不認為我們已經提出了與此相關的數字。但我們所說的是，我們認為我們將使該業務在今年上半年成為調整後的 EBITDA 正業務。

Okay. Excellent. And in the past, you've given out projections for the first quarter. And I don't know if I missed it or not, but do you have any type of projection in revenue growth that you're thinking for the first quarter?

好的。出色的。在過去，你已經給出了第一季度的預測。而且我不知道我是否錯過了它，但是您對第一季度的收入增長有任何類型的預測嗎？

So we've decided not to -- this year, for the most part, we're probably not going to provide forward-looking guidance only because of the level of activity that's going on in the business.

所以我們決定不——今年，在大多數情況下，我們可能不會僅僅因為業務活動的水平而提供前瞻性指導。

It's so substantial and coming on so strong that being able to reduce these things to forecasts and know when something is going to close and know what it's going to look like and how it's going to ramp up, is almost impossible. So the likelihood that we would be wrong and maybe even significantly wrong is pretty substantial.

它是如此巨大，而且如此強大，以至於能夠將這些事情減少到預測並知道什麼時候會結束，知道它會是什麼樣子以及它將如何增加，這幾乎是不可能的。因此，我們犯錯甚至嚴重錯誤的可能性非常大。

Therefore, we've gone the other direction, which is kind of -- which I think is materially disclosing -- here's what's going on in the business. Here's what's coming our way. We are now working with four of the five largest technology companies who are fundamentally driving what will probably be the innovation of our lifetimes. We'll no doubt look back on it as that.

因此，我們走了另一個方向，這有點——我認為這是實質性的披露——這就是業務中正在發生的事情。這就是我們即將發生的事情。我們現在正在與五家最大的科技公司中的四家合作，這些公司正在從根本上推動可能成為我們一生的創新。毫無疑問，我們會這樣回顧它。

And we're partnering with them. We're involved, and that opportunity is coming our way. So long story short, we're not -- we're going to stay out of the guidance business right now, but we're going to try to disclose what we're doing and the level of activity that we're seeing.

我們正在與他們合作。我們參與其中，這個機會正在向我們走來。長話短說，我們不是 - 我們現在將不參與指導業務，但我們將嘗試披露我們正在做的事情以及我們所看到的活動水平。

Okay. Can we anticipate it will be a growth year?

好的。我們可以預期這將是增長的一年嗎？

Yes. We're very much focused on growth. So yes is the easy answer to that.

是的。我們非常關注增長。所以是的，這是一個簡單的答案。

Okay. Excellent. All right. Thank you.

好的。出色的。好的。謝謝。

Marco Petroni, MG Capital Management.

MG 資本管理公司的 Marco Petroni。

Hey, Jack. How are you?

嘿，傑克。你好嗎？

Marco, hi. How are you?

馬爾科，嗨。你好嗎？

Good. A couple of questions. One, everybody has AI and machine learning algorithms, and they put them to different uses. But is there any company out there that you know of that combines that with the ability that you have on the data side with regards to organizing, collecting, and overlaying synthetic data on top of that? Is there anybody out there, including the big guys, that can do that?

好的。幾個問題。第一，每個人都有人工智能和機器學習算法，他們將它們用於不同的用途。但是，您知道有哪家公司將其與您在數據方面的組織、收集和疊加合成數據的能力相結合嗎？有沒有人，包括大人物，可以做到這一點？

Well, I think there are. I think there are a couple of companies that are doing some things that are similar to us, though not very many. We've got a view of the world that we can do two things well. And we think that there is like a virtuous circle that forms when we do the three things well.

好吧，我認為有。我認為有幾家公司正在做一些與我們相似的事情，儘管不是很多。我們的世界觀是我們可以做好兩件事。我們認為，當我們做好這三件事時，就會形成一個良性循環。

First is AI data preparation. We're helping large companies accelerate their ability to innovate in AI by doing the things that we do on the data side. Second thing is we're then helping deploy those models and integrate them into people's businesses. So we're helping build the models, and then we're helping integrate the models.

首先是人工智能數據準備。我們正在通過做我們在數據方面做的事情來幫助大公司加速他們在 AI 方面的創新能力。第二件事是我們然後幫助部署這些模型並將它們集成到人們的業務中。所以我們正在幫助構建模型，然後我們正在幫助集成模型。

And then thirdly, we have our own platforms, and we're learning the hard way. We're eating our own dog food, and we're figuring out how to do it for ourselves first. So we then develop the expertise to bring to both the data collection.

第三，我們有自己的平台，我們正在努力學習。我們正在吃自己的狗糧，我們正在想辦法先為自己做。因此，我們隨後開發了專業知識，以將兩者都用於數據收集。

Well, how do you collect data in a way that results in high-performing models? And then on the model deployment, how do you best deploy models in legacy workflows and legacy systems? How -- what are the opportunities for reinvention that you can bring to bear?

那麼，您如何以產生高性能模型的方式收集數據？然後在模型部署方面，您如何最好地在遺留工作流和遺留系統中部署模型？如何 - 您可以帶來哪些重塑機會？

The ChatGPT is great to go in, but I've had experiences where I put the same data and it gave different answers. Obviously, that can't be used within a company. Are you guys -- do you guys have the same capabilities of OpenAI in terms of creating those type of ChatGPTs within an organization -- specifically for an organization, so for example, their call centers or internally, to be able to use that to interact amongst employees as well as customers?

ChatGPT 非常適合使用，但我有過輸入相同數據卻給出不同答案的經歷。顯然，這不能在公司內部使用。你們——你們在組織內創建那些類型的 ChatGPT 方面是否具有與 OpenAI 相同的能力——特別是對於一個組織，例如，他們的呼叫中心或內部，能夠使用它進行交互在員工和客戶之間？

Yeah. So the essential architecture behind GPT is an architecture that is also behind our proprietary Goldengate technology. Now, do we have the same ability to stand up something that performs the way that that one does in a generalized way? Absolutely not. We don't have the budgets.

是的。因此，GPT 背後的基本架構也是我們專有的 Goldengate 技術背後的架構。現在，我們是否有同樣的能力站起來以一種普遍的方式表現的東西？絕對不。我們沒有預算。

Hundreds of millions of dollars was likely spent on getting it trained to the level that it's been trained on. There is a tremendous amount of data that gets poured in to create the billions and hundreds of billions of parameters that drive that model. A tremendous amount of cloud processing went into that. We cannot do that.

可能花費了數億美元來將它訓練到它被訓練的水平。大量數據被注入以創建驅動該模型的數十億和數千億個參數。其中進行了大量的雲處理。我們不能那樣做。

But what we can do and what's the future of the way these things will work is we can build on those. We can train them. We can customize them. We can use what's called reinforcement learning from human feedback in order to train customized -- domain-customized models with private-side data to enable them to perform better. That's really going to be the future of this.

但是我們可以做的以及這些事情的未來運作方式是我們可以在這些基礎上再接再厲。我們可以訓練他們。我們可以定制它們。我們可以使用所謂的從人類反饋中強化學習來訓練定制的——領域定制的模型和私有數據，使它們能夠更好地執行。這真的會成為未來。

So we'll see the big companies with the large language models proprietary to them -- large language models -- we will help them build those. But we won't be able to fund those ourselves. But then what we'll be able to do is customize them and build upon them in order to create business outcomes for people.

因此，我們將看到大公司擁有他們專有的大型語言模型——大型語言模型——我們將幫助他們構建這些模型。但我們無法自己資助這些。但隨後我們將能夠做的是定制它們並在它們的基礎上構建，以便為人們創造業務成果。

And one -- just one last question. You guys are trading at $200 million, roughly 2 times revenue. What is the company going to do going forward now that -- I mean, obviously, AI is everywhere. What is the company going to do to market our stock basically and get out there?

還有一個——只是最後一個問題。你們的交易價格為 2 億美元，大約是收入的 2 倍。公司現在打算做什麼——我的意思是，很明顯，人工智能無處不在。公司打算做什麼來基本推銷我們的股票並走出去？

I know earnings and revenue are great. But to get out there and do that, what do you have planned in the next coming months and quarters?

我知道收益和收入都很好。但是，為了走出去並做到這一點，您在接下來的幾個月和幾個季度裡有什麼計劃？

So yeah, I think the most important thing we can do for our shareholders -- and of course, I'm prominent among our shareholders -- is to continue to do the things that we're doing.

所以，是的，我認為我們可以為我們的股東做的最重要的事情——當然，我在我們的股東中很突出——就是繼續做我們正在做的事情。

The fact that we're in four out of five of the largest technology companies helping to develop what will be a transformative technology that's still in its infancy and will need a lot of work over the next several years -- that we've gotten there and we're doing that, I think, is huge -- that we've gotten there and are doing that without having to go out into the markets and dilute our equity and raise a ton of debt is, I think, it's an impressive feat.

事實上，五分之四的最大科技公司都在幫助開發一項仍處於起步階段且在未來幾年需要大量工作的變革性技術——我們已經做到了這一點我認為，我們正在做的事情是巨大的——我們已經做到了並且正在這樣做，而不必進入市場並稀釋我們的股權和籌集大量債務，我認為，這是一個令人印象深刻的事情壯舉。

Now, how do we better promote that? I think the first thing starts with execution. And then what follows from that is lots of conversations with people who, I'm hoping, will be attracted to the execution that we're bringing to bear. And of course, looking at some of the techniques that people use -- conferences and outreach and talking to analysts and all of those things. But fundamentally, we're going to be about execution.

現在，我們如何更好地推廣它？我認為首先要從執行開始。然後接下來是與人們的大量對話，我希望他們會被我們帶來的處決所吸引。當然，看看人們使用的一些技術——會議和外展以及與分析師交談等等。但從根本上說，我們將關注執行。

No, absolutely. But I mean, I've been a shareholder for two years, and nobody really knows about us. Every other AI company is trading at a 5, 10, 15 multiple on revenue. We're trading at 2 times. And going forward, we have the potential of growing at 30%-plus. Seems like we're pretty undervalued here compared to the sample. But thank you.

不，絕對。但我的意思是，我已經成為股東兩年了，但沒有人真正了解我們。其他所有 AI 公司的營收都是 5、10、15 倍。我們的交易價格是 2 倍。展望未來，我們有可能以 30% 以上的速度增長。與樣本相比，我們在這裡的價值似乎被低估了。但是謝謝你。

Thanks, Marco.

謝謝，馬可。

Craig Samuels, Samuels Capital Management.

克雷格塞繆爾斯，塞繆爾斯資本管理公司。

Hey, Jack. How are you?

嘿，傑克。你好嗎？

Hey, Craig. How are you? I'm well. Thanks.

嘿，克雷格。你好嗎？我很好。謝謝。

Pretty good. Thank you. Back in several quarters ago, you talked about your total number of sales reps for both Agility and then service solutions side. Where do you stand today with the numbers of sales reps?

不錯。謝謝。回到幾個季度前，您談到了敏捷和服務解決方案方面的銷售代表總數。您今天對銷售代表的數量持什麼態度？

So on the services solutions side, on the AI side, we're about at the same number that we last shared. We've taken down the number quite a bit on the Agility side. And a couple of things went into that decision.

因此，在服務解決方案方面，在人工智能方面，我們的數字與上次共享的數字大致相同。我們已經在敏捷方面大大降低了這個數字。這個決定有幾件事。

First of which was, in the beginning of the year, we were having a hard time retaining people very specifically in one of the sales offices that we put up. It was -- there was a labor shortage. That was pretty well known. We were in Austin, Texas, where a lot of SaaS companies were, and they were overpaying, as far as I'm concerned, for talent. And we didn't want to play that game.

首先是，在今年年初，我們很難特別留住我們設立的一個銷售辦事處的人員。這是——勞動力短缺。那是眾所周知的。我們在得克薩斯州奧斯汀，那裡有很多 SaaS 公司，在我看來，他們為人才支付的費用過高。我們不想玩那個遊戲。

So what we decided instead was -- let's be good stewards of capital. Let's not play the game of overpaying for talent. Let's instead work with a smaller number. And we've had great success retaining very talented salespeople and others of our offices.

所以我們決定改為 - 讓我們成為資本的好管家。讓我們不要玩為人才多付錢的遊戲。讓我們使用較小的數字。我們在留住非常有才華的銷售人員和我們辦公室的其他人方面取得了巨大成功。

Let's retain them. Let's work on it. Let's build a sales organization that has very much a data-driven approach to sales and to optimizing our customer experience and build from there. And I think that's proving to be the right decision.

讓我們保留他們。讓我們努力吧。讓我們建立一個銷售組織，該組織在很大程度上採用數據驅動的銷售方法並優化我們的客戶體驗並以此為基礎。我認為事實證明這是正確的決定。

As we look out at Agility and we see what's going on now, we see new logo booking up 83% year over year. Our net retention going from the 90s up to 100% now. Significant performance improvement relative to the number of demos that we do that end up in closed sales going from like 18% at the beginning of the year to 33% now.

當我們審視 Agility 並了解現在的情況時，我們看到新徽標的預訂量同比增長 83%。我們的淨保留率從 90 年代上升到現在的 100%。相對於我們所做的演示數量而言，性能顯著提高，最終以封閉式銷售結束，從年初的 18% 增加到現在的 33%。

So with that, we will, I believe, see acceleration in growth. And it's always easier to throw logs on a fire that's burning strongly. So that's where we are.

因此，我相信，我們將看到增長加速。把木頭扔到熊熊燃燒的火上總是更容易。這就是我們的處境。

Right. So I don't remember the last numbers that you had. Can you share them for services? Going back in time, I seem to remember, it was like six or seven. And on the Agility side, you had a target of 110, and the last numbers I have in the 67 zone. It's been a little while. Can you actually share the numbers?

正確的。所以我不記得你的最後一個數字。你能分享他們的服務嗎？時光倒流，我似乎記得，大約是六七歲。在敏捷方面，您的目標是 110，而我的最後一個數字是 67。已經有一段時間了。你真的可以分享這些數字嗎？

Sure. I think what we said was we had nine folks, quota-bearing executives in the services solutions area. And then we had a combination of about 90 people, 42 quota-bearing people in Agility, and another 37 BDRs.

當然。我想我們所說的是我們有九個人，服務解決方案領域的配額高管。然後我們有大約 90 人的組合，42 名敏捷配額人員和另外 37 名 BDR。

We've -- in Agility, we've reshuffled those numbers pretty considerably in terms of the mix and the workflows, and we brought that number down quite a bit. I don't have the current number to share with you. Happy to do that off the call when I go get that. But we brought that down, and we're getting the performance off of that smaller cohort which is, at the end of the day, what it's all about.

我們 - 在 Agility 中，我們在組合和工作流程方面對這些數字進行了相當大的重組，並且我們將這個數字降低了很多。我沒有當前號碼可以與您分享。當我去拿那個的時候，很高興能在電話中做到這一點。但是我們降低了它，並且我們正在從那個較小的隊列中獲得性能，這在一天結束時就是它的全部。

Right. And then on the nine service sales reps, I recall from -- again, this is probably two years ago -- the (technical difficulty)

正確的。然後關於九個服務銷售代表，我記得——再一次，這可能是兩年前的——（技術困難）

Craig, sorry, I think you dropped off.

克雷格，對不起，我想你掉線了。

One moment, I'll reconnect Craig.

等一下，我會重新連接克雷格。

Okay.

好的。

Craig, can you hear us?

克雷格，你能聽到我們嗎？

I can hear you. Are you there?

我可以聽見你。你在嗎？

Okay, your line is live.

好的，您的線路已接通。

Not sure what happened, but --

不確定發生了什麼，但是——

Yeah, hi, Craig. I'm not sure what's happening.

是的，嗨，克雷格。我不確定發生了什麼。

I had asked about productivity. And I seem to remember about $1.5 million of quota per service AI sales rep. Is that still consistent with where you guys are today, or has that number gone up or down?

我問過生產力。我似乎記得每個服務 AI 銷售代表的配額約為 150 萬美元。這仍然與你們今天的情況一致，還是這個數字上升或下降了？

Yeah. So I think we put that out there where they average the -- in the services and solutions area, the quotas are actually derived from the account assignments. So depending upon the accounts that people are working on, they can be fairly significantly different from that. They can be much higher than that. And an entry-level person who's kind of building his account base can be lower than that.

是的。所以我認為我們把它放在他們平均的地方 - 在服務和解決方案領域，配額實際上來自賬戶分配。因此，根據人們正在處理的賬戶，他們可能與此有很大的不同。他們可以比這高得多。而建立帳戶基礎的入門級人員可能比這低。

Got it. And then, also, Nvidia had some news regarding the data center providing computer power for AI. And just wondering if that helps you or if that's competitive.

知道了。然後，Nvidia 也有一些關於數據中心為 AI 提供計算能力的消息。只是想知道這是否對您有幫助，或者這是否具有競爭力。

No, I think it's very much supportive of the value position. I was on their call, and I read their -- what was said on the call. And I think the way they're viewing the opportunity is very much the way we're viewing the opportunity.

不，我認為它非常支持價值立場。我正在接聽他們的電話，我讀了他們在電話中所說的話。而且我認為他們看待機會的方式與我們看待機會的方式非常相似。

They are looking at it from a different perspective. They are looking at it from a perspective of enabling it from a processor side. We're looking at it from a perspective of enabling it with data. And these are two sides of the same coin as far as I'm concerned.

他們正在從不同的角度看待它。他們從處理器端啟用它的角度來看待它。我們正在從使用數據啟用它的角度來看待它。就我而言，這是同一枚硬幣的兩個方面。

Yeah, that's exactly what I thought. Just wanted to hear you confirm that.

是的，這正是我的想法。只是想听聽你證實這一點。

And then lastly, would you expect your gross margins to increase over the next 12 to 36 months? Will there be a greater software component? Or will it still be heavily weighted towards services?

最後，您預計您的毛利率在未來 12 到 36 個月內會增加嗎？會有更大的軟件組件嗎？還是它仍然會偏重於服務？

I think it depends on what happens. Given the activity that we're now seeing, of great significance, I think we will continue -- if we're successful at closing the opportunities that are before us -- I think we're going to continue to see a very heavy weighting towards solutions and services from a consolidated margin perspective. I'm willing to live with that problem, though.

我認為這取決於發生了什麼。鑑於我們現在看到的具有重大意義的活動，我認為我們將繼續——如果我們成功地抓住擺在我們面前的機會——我認為我們將繼續看到一個非常重要的權重從綜合利潤的角度來看解決方案和服務。不過，我願意忍受這個問題。

Right, so that means still in the below 40%?

對，也就是說還在40%以下？

Not necessarily. I think that as we start to execute the plan, we will be able to move above 40% over time, but probably still below 50%. Again, it remains to be seen what we're able to deliver on the platform side. But if the opportunity is as large as we're hoping it is on the solutions side, I think it will weigh towards that.

不必要。我認為，隨著我們開始執行該計劃，我們將能夠隨著時間的推移超過 40%，但可能仍低於 50%。同樣，我們能夠在平台方面提供什麼還有待觀察。但如果解決方案方面的機會像我們希望的那樣大，我認為它會偏重於此。

Yeah. Sounds good. Keep up the good work, and look forward to continuing to monitor your progress over time. Thanks.

是的。聽起來不錯。保持良好的工作，並期待隨著時間的推移繼續監控您的進度。謝謝。

Thank you.

謝謝。

We have reached the end of the question-and-answer session, and I will now turn the call over to Jack for closing remarks.

問答環節已經結束，我現在將把電話轉給傑克作結束語。

Thank you, operator. So yeah, I'll quickly recap.

謝謝你，運營商。所以是的，我會快速回顧一下。

We're seeing very recent acceleration in AI investment by large tech companies -- seems to be coinciding with OpenAI's release of ChatGPT. We're now either expanding work with or beginning work with or discussing starting work with four of the five largest tech companies in the world. And much of what is under discussion has to do with building and improving large language models.

我們看到大型科技公司最近對人工智能的投資加速——似乎與 OpenAI 發布 ChatGPT 相吻合。我們現在正在擴大與全球五家最大的科技公司中的四家合作，或者開始合作，或者討論開始合作。正在討論的大部分內容都與構建和改進大型語言模型有關。

Very excited about where we are with these companies and excited about where we are with a host of similarly impressive companies across other domains. Even though forecasting exact close dates remains challenging, we think we're in the right place at the right time to ride this wave. We're seeing positive trends across our other business segments as well.

對我們與這些公司的合作感到非常興奮，對我們與其他領域的許多同樣令人印象深刻的公司的合作感到興奮。儘管預測確切的結束日期仍然具有挑戰性，但我們認為我們在正確的時間處於正確的位置來駕馭這股浪潮。我們也看到了其他業務部門的積極趨勢。

Synodex growth last year was huge. It's well positioned to expand in its market this year. And Agility, supported by what we believe was a very successful release of PR CoPilot, we continue to make great strides in win rate, net retention bookings, all of which are, of course, leading indicators of accelerating growth.

Synodex 去年的增長是巨大的。它已準備好在今年擴展其市場。而 Agility，在我們認為非常成功的 PR CoPilot 發布的支持下，我們繼續在贏率、淨保留預訂方面取得長足進步，當然，所有這些都是加速增長的領先指標。

So very excited to be here today, very excited with the news that we're sharing today. And thank you, all, for participating in this call. We will look forward to our next call with you.

今天很高興來到這裡，對我們今天分享的消息感到非常興奮。感謝大家參與此次電話會議。我們期待與您的下一次通話。

This concludes today's conference, and you may disconnect your lines at this time. Thank you for your participation.

今天的會議到此結束，此時您可以斷開您的線路。感謝您的參與。